Optimization and particle swarm optimization (O & PSO) Engr Nosheen Memon
The document discusses particle swarm optimization (PSO) which is a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. It summarizes PSO as follows: PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate is adjusted based on the best candidates in the local neighborhood and overall population. This process is repeated until a termination criterion is met.
The document discusses Particle Swarm Optimization (PSO) algorithms and their application in engineering design optimization. It provides an overview of optimization problems and algorithms. PSO is introduced as an evolutionary computational technique inspired by animal social behavior that can be used to find global optimization solutions. The document outlines the basic steps of the PSO algorithm and how it works by updating particle velocities and positions to track the best solutions. Examples of applications to model fitting and inductor design optimization are provided.
Optimization involves finding the best values for variables to minimize or maximize an objective function subject to constraints. An optimization problem consists of an objective function, variables, and constraints. The objective function expresses the performance of a system and must be minimized or maximized. Variables define the objective function and constraints. Constraints allow variables to take on certain values but exclude others to ensure feasibility. Common optimization techniques include mathematical programming, calculus methods, network methods, and meta-heuristic algorithms such as genetic algorithms, simulated annealing, and whale optimization.
TEXT FEUTURE SELECTION USING PARTICLE SWARM OPTIMIZATION (PSO)yahye abukar
This document discusses using particle swarm optimization (PSO) for feature selection in text categorization. It provides an introduction to PSO, explaining how it was inspired by bird flocking behavior. The document outlines the PSO algorithm, parameters, and concepts like particle velocity and position updating. It also discusses feature selection techniques like filter and wrapper methods and compares different feature utility measures that can be used.
This document discusses advanced algorithm design and analysis techniques including dynamic programming, greedy algorithms, and amortized analysis. It provides examples of dynamic programming including matrix chain multiplication and longest common subsequence. Dynamic programming works by breaking problems down into overlapping subproblems and solving each subproblem only once. Greedy algorithms make locally optimal choices at each step to find a global optimum. Amortized analysis averages the costs of a sequence of operations to determine average-case performance.
The document summarizes the Whale Optimization Algorithm (WOA), which is a meta-heuristic optimization algorithm inspired by the hunting behavior of humpback whales. It describes how WOA simulates the bubble-net feeding mechanism of humpback whales to optimize problem solutions. The algorithm includes steps of encircling prey to find the best solution, then exploiting and exploring further to update positions and potentially find an even better solution. WOA iterates through these steps until a termination criterion is met, at which point it outputs the best found solution.
The document discusses various metaheuristic algorithms for optimization problems including particle swarm optimization, bee colony optimization, ant colony optimization, and cuckoo search. It explains the components and mechanisms of these algorithms, provides pseudocode examples, and evaluates them in comparison to other metaheuristics like genetic algorithms and simulated annealing. The metaheuristics aim to efficiently search large solution spaces by mimicking natural processes like swarming behavior.
Optimization and particle swarm optimization (O & PSO) Engr Nosheen Memon
The document discusses particle swarm optimization (PSO) which is a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. It summarizes PSO as follows: PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate is adjusted based on the best candidates in the local neighborhood and overall population. This process is repeated until a termination criterion is met.
The document discusses Particle Swarm Optimization (PSO) algorithms and their application in engineering design optimization. It provides an overview of optimization problems and algorithms. PSO is introduced as an evolutionary computational technique inspired by animal social behavior that can be used to find global optimization solutions. The document outlines the basic steps of the PSO algorithm and how it works by updating particle velocities and positions to track the best solutions. Examples of applications to model fitting and inductor design optimization are provided.
Optimization involves finding the best values for variables to minimize or maximize an objective function subject to constraints. An optimization problem consists of an objective function, variables, and constraints. The objective function expresses the performance of a system and must be minimized or maximized. Variables define the objective function and constraints. Constraints allow variables to take on certain values but exclude others to ensure feasibility. Common optimization techniques include mathematical programming, calculus methods, network methods, and meta-heuristic algorithms such as genetic algorithms, simulated annealing, and whale optimization.
TEXT FEUTURE SELECTION USING PARTICLE SWARM OPTIMIZATION (PSO)yahye abukar
This document discusses using particle swarm optimization (PSO) for feature selection in text categorization. It provides an introduction to PSO, explaining how it was inspired by bird flocking behavior. The document outlines the PSO algorithm, parameters, and concepts like particle velocity and position updating. It also discusses feature selection techniques like filter and wrapper methods and compares different feature utility measures that can be used.
This document discusses advanced algorithm design and analysis techniques including dynamic programming, greedy algorithms, and amortized analysis. It provides examples of dynamic programming including matrix chain multiplication and longest common subsequence. Dynamic programming works by breaking problems down into overlapping subproblems and solving each subproblem only once. Greedy algorithms make locally optimal choices at each step to find a global optimum. Amortized analysis averages the costs of a sequence of operations to determine average-case performance.
The document summarizes the Whale Optimization Algorithm (WOA), which is a meta-heuristic optimization algorithm inspired by the hunting behavior of humpback whales. It describes how WOA simulates the bubble-net feeding mechanism of humpback whales to optimize problem solutions. The algorithm includes steps of encircling prey to find the best solution, then exploiting and exploring further to update positions and potentially find an even better solution. WOA iterates through these steps until a termination criterion is met, at which point it outputs the best found solution.
The document discusses various metaheuristic algorithms for optimization problems including particle swarm optimization, bee colony optimization, ant colony optimization, and cuckoo search. It explains the components and mechanisms of these algorithms, provides pseudocode examples, and evaluates them in comparison to other metaheuristics like genetic algorithms and simulated annealing. The metaheuristics aim to efficiently search large solution spaces by mimicking natural processes like swarming behavior.
This document discusses particle swarm optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking or fish schooling. PSO uses a population of candidate solutions called particles that fly through the problem hyperspace, with each particle adjusting its position based on its own experience and the experience of neighboring particles. The algorithm iteratively improves the particles' positions to locate the best solution based on fitness evaluations.
Particle swarm optimization is a technique for finding the best solution to a problem within a search space, inspired by bird flocking behavior. It initializes a population of random particles representing potential solutions and updates their positions based on their own experience and the experiences of neighboring particles. Over iterations, the population is guided toward better solutions as particles emulate the most successful neighbors. Compared to genetic algorithms, particle swarm optimization uses a one-way information sharing mechanism to guide the population toward the best found solution. The key parameters that can be adjusted include the number of particles, their maximum velocity, and learning factors that balance how much particles rely on their own experience versus the experiences of neighbors.
Glowworm swarm optimization (GSO) is a swarm intelligence based algorithm, introduced by K.N. Krishnanand and D. Ghose in 2005, for simultaneous computation of multiple optima of multimodal functions
DriP PSO- A fast and inexpensive PSO for drifting problem spacesZubin Bhuyan
Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to use a few stagnant particles to determine the approximate direction in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm.
The document discusses Particle Swarm Optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking. PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate, or particle, updates its position based on its own experience and the experience of neighboring highly-ranked particles. The algorithm is simple to implement and converges quickly to produce approximate solutions to difficult optimization problems.
This document discusses adaptive filtering techniques, specifically the Least Mean Square (LMS) and Recursive Least Squares (RLS) algorithms. It describes the basic structure and operation of adaptive filters, including their use of error signals as feedback to optimize transfer functions. The LMS algorithm is commonly used due to its computational simplicity, while RLS provides faster convergence but with higher complexity. The document proposes a modified Delayed LMS (DLMS) adaptive filter architecture to reduce adaptation delay by feeding error computations forward through pipeline stages. Simulation results show this DLMS design achieves lower area, delay and power compared to conventional LMS and RLS filters.
The document outlines the policies, course objectives, and schedule for CHE 536 Engineering Optimization taught by Prof. Shi-Shang Jang at National Tsing Hua University. The class will meet every Thursday from 2-5pm in room 221 of the Chemical Engineering building. The course aims to teach problem formulation, numerical optimization algorithms, and their applications. Homework is due biweekly and grades are based on homework, a midterm exam, and a term project. Topics include single variable optimization, unconstrained optimization, linear programming, and nonlinear programming.
Batch mode reinforcement learning based on the synthesis of artificial trajec...Université de Liège (ULg)
This document discusses batch mode reinforcement learning where the only available information is a set of trajectories. It proposes a model-free Monte Carlo estimator (MFMC) that estimates the performance of a policy by rebuilding trajectories from the available trajectory pieces in order to mimic Monte Carlo rollouts. The MFMC sequentially selects trajectory pieces to rebuild trajectories while minimizing distance between states and actions. This allows estimating a policy's performance without knowledge of the system dynamics or reward function. The analysis shows the MFMC has zero bias and variance that decreases with the number of rebuilt trajectories.
The modern power system around the world has grown in complexity of interconnection and
power demand. The focus has shifted towards enhanced performance, increased customer focus,
low cost, reliable and clean power. In this changed perspective, scarcity of energy resources,
increasing power generation cost, environmental concern necessitates optimal economic dispatch.
In reality power stations neither are at equal distances from load nor have similar fuel cost
functions. Hence for providing cheaper power, load has to be distributed among various power
stations in a way which results in lowest cost for generation. Practical economic dispatch (ED)
problems have highly non-linear objective function with rigid equality and inequality constraints.
Particle swarm optimization (PSO) is applied to allot the active power among the generating
stations satisfying the system constraints and minimizing the cost of power generated. The
viability of the method is analyzed for its accuracy and rate of convergence. The economic load
dispatch problem is solved for three and six unit system using PSO and conventional method for
both cases of neglecting and including transmission losses. The results of PSO method were
compared with conventional method and were found to be superior. The conventional
optimization methods are unable to solve such problems due to local optimum solution
convergence. Particle Swarm Optimization (PSO) since its initiation in the last 15 years has been
a potential solution to the practical constrained economic load dispatch (ELD) problem. The
optimization technique is constantly evolving to provide better and faster results.
While writing the report on our project seminar, we were wondering that Science and smart
technology are as ever expanding field and the engineers working hard day and night and make
the life a gift for us
This document provides an overview of regression analysis and compares regression to neural networks. It defines regression as estimating the relationship between variables. The main types covered are linear, nonlinear, simple, multiple and logistic regression. Examples are given to illustrate simple linear regression and least squares methods. The document also discusses best practices like avoiding overfitting and dealing with multicollinearity. Finally, it provides examples comparing regression and deep learning approaches.
Beyond function approximators for batch mode reinforcement learning: rebuildi...Université de Liège (ULg)
- The document discusses using function approximators for batch reinforcement learning problems where the only available information is a set of trajectories.
- It argues that function approximators have limitations in addressing risk-sensitive criteria, safety, optimal use of trajectories, and generating new experiments.
- An alternative approach called "rebuilding trajectories" is proposed, which does not use function approximators. It involves analyzing and recombining pieces of the original trajectories to compute policies and estimates.
This document provides an overview of optimization techniques. It defines optimization as identifying variable values that minimize or maximize an objective function subject to constraints. It then discusses various applications of optimization in finance, engineering, and data modeling. The document outlines different types of optimization problems and algorithms. It provides examples of unconstrained optimization algorithms like gradient descent, conjugate gradient, Newton's method, and BFGS. It also discusses the Nelder-Mead simplex algorithm for constrained optimization and compares the performance of these algorithms on sample problems.
A Comparison of Particle Swarm Optimization and Differential Evolutionijsc
Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are compared on twelve constrained nonlinear test functions. Generally, the results show that Differential Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and robustness.
ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATIONMln Phaneendra
In this ppt particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated.The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior.
Particle swarm optimization is a metaheuristic algorithm inspired by the social behavior of bird flocking. It works by having a population of candidate solutions, called particles, that fly through the problem space, adjusting their positions based on their own experience and the experience of neighboring particles. Each particle keeps track of its best position and the best position of its neighbors. The algorithm iteratively updates the velocity and position of each particle to move it closer to better solutions.
Distributed Parallel Process Particle Swarm Optimization on Fixed Charge Netw...Corey Clark, Ph.D.
The document presents a dynamically distributed binary particle swarm optimization (BPSO) approach for solving fixed-charge network flow problems. The approach distributes the BPSO algorithm across a cluster of devices using a distributed accelerated analytics platform. Testing showed the distributed BPSO approach found better solutions faster than serial BPSO and optimization approaches for various problem sizes, demonstrating the benefits of dynamic distributed computing for difficult mixed integer programs.
Iterative improvement is an algorithm design technique for solving optimization problems. It starts with a feasible solution and repeatedly makes small changes to the current solution to find a solution with a better objective function value until no further improvements can be found. The simplex method, Ford-Fulkerson algorithm, and local search heuristics are examples that use this technique. The maximum flow problem can be solved using the iterative Ford-Fulkerson algorithm, which finds augmenting paths in a flow network to incrementally increase the flow from the source to the sink until no more augmenting paths exist.
Iterative improvement is an algorithm design technique for solving optimization problems. It starts with a feasible solution and repeatedly makes small changes to the current solution to find a solution with a better objective function value until no further improvements can be found. The simplex method, Ford-Fulkerson algorithm, and local search heuristics are examples that use this technique. The maximum flow problem can be solved using the iterative Ford-Fulkerson algorithm, which finds augmenting paths in a flow network to incrementally increase the flow from the source to the sink until no more augmenting paths exist.
optimization methods by using matlab.pptxabbas miry
This document discusses optimization techniques in MATLAB. It describes how to perform both unconstrained and constrained optimization. For unconstrained problems, the fminunc function is used to find the minimum of an objective function. For constrained problems, fmincon is used to minimize an objective function subject to inequality, equality, and bound constraints. The document provides an example of using these functions to solve a sample constrained optimization problem.
This document provides an overview and introduction for a course on wireless systems. It discusses the following key points in 3 sentences or less:
- The course will cover wireless communications theory and apply it through hands-on laboratory exercises. Students will gain skills relevant to careers in the wireless industry.
- Wireless networking differs from wired due to properties like mobility and lower infrastructure costs that enable broadcasting to multiple users.
- Modern wireless networks have evolved from analog to digital cellular networks supporting various data applications on smartphones, and future networks will need to support the growing "Internet of Things" with billions of connected devices.
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Similar a swarm pso and gray wolf Optimization.pdf
This document discusses particle swarm optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking or fish schooling. PSO uses a population of candidate solutions called particles that fly through the problem hyperspace, with each particle adjusting its position based on its own experience and the experience of neighboring particles. The algorithm iteratively improves the particles' positions to locate the best solution based on fitness evaluations.
Particle swarm optimization is a technique for finding the best solution to a problem within a search space, inspired by bird flocking behavior. It initializes a population of random particles representing potential solutions and updates their positions based on their own experience and the experiences of neighboring particles. Over iterations, the population is guided toward better solutions as particles emulate the most successful neighbors. Compared to genetic algorithms, particle swarm optimization uses a one-way information sharing mechanism to guide the population toward the best found solution. The key parameters that can be adjusted include the number of particles, their maximum velocity, and learning factors that balance how much particles rely on their own experience versus the experiences of neighbors.
Glowworm swarm optimization (GSO) is a swarm intelligence based algorithm, introduced by K.N. Krishnanand and D. Ghose in 2005, for simultaneous computation of multiple optima of multimodal functions
DriP PSO- A fast and inexpensive PSO for drifting problem spacesZubin Bhuyan
Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to use a few stagnant particles to determine the approximate direction in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm.
The document discusses Particle Swarm Optimization (PSO), which is an optimization technique inspired by swarm intelligence and the social behavior of bird flocking. PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate, or particle, updates its position based on its own experience and the experience of neighboring highly-ranked particles. The algorithm is simple to implement and converges quickly to produce approximate solutions to difficult optimization problems.
This document discusses adaptive filtering techniques, specifically the Least Mean Square (LMS) and Recursive Least Squares (RLS) algorithms. It describes the basic structure and operation of adaptive filters, including their use of error signals as feedback to optimize transfer functions. The LMS algorithm is commonly used due to its computational simplicity, while RLS provides faster convergence but with higher complexity. The document proposes a modified Delayed LMS (DLMS) adaptive filter architecture to reduce adaptation delay by feeding error computations forward through pipeline stages. Simulation results show this DLMS design achieves lower area, delay and power compared to conventional LMS and RLS filters.
The document outlines the policies, course objectives, and schedule for CHE 536 Engineering Optimization taught by Prof. Shi-Shang Jang at National Tsing Hua University. The class will meet every Thursday from 2-5pm in room 221 of the Chemical Engineering building. The course aims to teach problem formulation, numerical optimization algorithms, and their applications. Homework is due biweekly and grades are based on homework, a midterm exam, and a term project. Topics include single variable optimization, unconstrained optimization, linear programming, and nonlinear programming.
Batch mode reinforcement learning based on the synthesis of artificial trajec...Université de Liège (ULg)
This document discusses batch mode reinforcement learning where the only available information is a set of trajectories. It proposes a model-free Monte Carlo estimator (MFMC) that estimates the performance of a policy by rebuilding trajectories from the available trajectory pieces in order to mimic Monte Carlo rollouts. The MFMC sequentially selects trajectory pieces to rebuild trajectories while minimizing distance between states and actions. This allows estimating a policy's performance without knowledge of the system dynamics or reward function. The analysis shows the MFMC has zero bias and variance that decreases with the number of rebuilt trajectories.
The modern power system around the world has grown in complexity of interconnection and
power demand. The focus has shifted towards enhanced performance, increased customer focus,
low cost, reliable and clean power. In this changed perspective, scarcity of energy resources,
increasing power generation cost, environmental concern necessitates optimal economic dispatch.
In reality power stations neither are at equal distances from load nor have similar fuel cost
functions. Hence for providing cheaper power, load has to be distributed among various power
stations in a way which results in lowest cost for generation. Practical economic dispatch (ED)
problems have highly non-linear objective function with rigid equality and inequality constraints.
Particle swarm optimization (PSO) is applied to allot the active power among the generating
stations satisfying the system constraints and minimizing the cost of power generated. The
viability of the method is analyzed for its accuracy and rate of convergence. The economic load
dispatch problem is solved for three and six unit system using PSO and conventional method for
both cases of neglecting and including transmission losses. The results of PSO method were
compared with conventional method and were found to be superior. The conventional
optimization methods are unable to solve such problems due to local optimum solution
convergence. Particle Swarm Optimization (PSO) since its initiation in the last 15 years has been
a potential solution to the practical constrained economic load dispatch (ELD) problem. The
optimization technique is constantly evolving to provide better and faster results.
While writing the report on our project seminar, we were wondering that Science and smart
technology are as ever expanding field and the engineers working hard day and night and make
the life a gift for us
This document provides an overview of regression analysis and compares regression to neural networks. It defines regression as estimating the relationship between variables. The main types covered are linear, nonlinear, simple, multiple and logistic regression. Examples are given to illustrate simple linear regression and least squares methods. The document also discusses best practices like avoiding overfitting and dealing with multicollinearity. Finally, it provides examples comparing regression and deep learning approaches.
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- The document discusses using function approximators for batch reinforcement learning problems where the only available information is a set of trajectories.
- It argues that function approximators have limitations in addressing risk-sensitive criteria, safety, optimal use of trajectories, and generating new experiments.
- An alternative approach called "rebuilding trajectories" is proposed, which does not use function approximators. It involves analyzing and recombining pieces of the original trajectories to compute policies and estimates.
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A Comparison of Particle Swarm Optimization and Differential Evolutionijsc
Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are compared on twelve constrained nonlinear test functions. Generally, the results show that Differential Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and robustness.
ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATIONMln Phaneendra
In this ppt particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated.The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior.
Particle swarm optimization is a metaheuristic algorithm inspired by the social behavior of bird flocking. It works by having a population of candidate solutions, called particles, that fly through the problem space, adjusting their positions based on their own experience and the experience of neighboring particles. Each particle keeps track of its best position and the best position of its neighbors. The algorithm iteratively updates the velocity and position of each particle to move it closer to better solutions.
Distributed Parallel Process Particle Swarm Optimization on Fixed Charge Netw...Corey Clark, Ph.D.
The document presents a dynamically distributed binary particle swarm optimization (BPSO) approach for solving fixed-charge network flow problems. The approach distributes the BPSO algorithm across a cluster of devices using a distributed accelerated analytics platform. Testing showed the distributed BPSO approach found better solutions faster than serial BPSO and optimization approaches for various problem sizes, demonstrating the benefits of dynamic distributed computing for difficult mixed integer programs.
Iterative improvement is an algorithm design technique for solving optimization problems. It starts with a feasible solution and repeatedly makes small changes to the current solution to find a solution with a better objective function value until no further improvements can be found. The simplex method, Ford-Fulkerson algorithm, and local search heuristics are examples that use this technique. The maximum flow problem can be solved using the iterative Ford-Fulkerson algorithm, which finds augmenting paths in a flow network to incrementally increase the flow from the source to the sink until no more augmenting paths exist.
Iterative improvement is an algorithm design technique for solving optimization problems. It starts with a feasible solution and repeatedly makes small changes to the current solution to find a solution with a better objective function value until no further improvements can be found. The simplex method, Ford-Fulkerson algorithm, and local search heuristics are examples that use this technique. The maximum flow problem can be solved using the iterative Ford-Fulkerson algorithm, which finds augmenting paths in a flow network to incrementally increase the flow from the source to the sink until no more augmenting paths exist.
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This document discusses optimization techniques in MATLAB. It describes how to perform both unconstrained and constrained optimization. For unconstrained problems, the fminunc function is used to find the minimum of an objective function. For constrained problems, fmincon is used to minimize an objective function subject to inequality, equality, and bound constraints. The document provides an example of using these functions to solve a sample constrained optimization problem.
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- Wireless networking differs from wired due to properties like mobility and lower infrastructure costs that enable broadcasting to multiple users.
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Signal flow graphs are an alternative to block diagrams for graphically describing systems. They consist of nodes to represent signals and branches to represent system blocks labeled with transfer functions. To convert a block diagram to a signal flow graph, identify and label all signals, place a node for each, connect nodes with branches in place of blocks while maintaining direction, and label branches with transfer functions. Mason's rule provides a formula to calculate the overall transfer function of a system represented by a signal flow graph based on terms like forward path gains, loop gains, and non-touching loop gains. Controller design uses feedback to modify a system's response to meet performance specifications by placing the closed-loop poles through selection of controller parameters.
This document contains two examples of transfer functions and their corresponding Bode plots:
1) The first transfer function has a gain of 0.75 and a single pole at 0.25 rad/sec. Its Bode plot shows the gain of -2.5 dB and a slope of -20 dB/decade above the corner frequency.
2) The second transfer function has a gain of 4, a double pole at 0.5 rad/sec, and a first-order derivative term with a corner frequency of 1 rad/sec. Its Bode plot shows the gain of 12 dB and slopes of -40 and -20 dB/decade associated with the double and single poles.
The document contains two examples of transfer functions and their corresponding Bode plots. The first transfer function has a gain of 0.75 and a pole at 0.25 rad/sec. Its Bode plot shows the gain of -2.5 dB and a slope of -20 dB/decade above the pole frequency. The second transfer function has a gain of 4, a double pole at 0.5 rad/sec, and a first-order derivative term. Its Bode plot shows the gain of 12 dB and slopes of -40 and -20 dB/decade below and above the double pole frequency.
1) The document discusses parameter identification algorithms that can be used for online identification of parametric models.
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3) An example of identifying the parameters of a first-order plant model is provided to illustrate the three steps.
1. Parametric models express physical systems in terms of unknown parameters that need to be identified. The models take the form of linear static (SPM), linear dynamic (DPM), bilinear (B-SPM, B-DPM), or state-space (SSPM) models.
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The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
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Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
2. An optimization problem seeks to find the largest (the smallest)
value of a quantity (such as maximum revenue or minimum surface
area) given certain limits to a problem.
An optimization problem can usually be expressed as “find the
maximum (or minimum) value of some quantity Q under a certain
set of given conditions”.
Definition of Optimization problems
3. Problems that can be modelled and solved by optimization
techniques
Scheduling Problems (production, airline, etc.)
Network Design Problems
Facility Location Problems
Inventory management
Transportation Problems
Minimum spanning tree problem
Shortest path problem
Maximum flow problem
Min-cost flow problem
4. 1. Classical Optimization
Useful in finding the optimum solution or unconstrained maxima
or minima of continuous and differentiable functions.
Analytical methods make use of differential calculus in locating
the optimum solution
5. cont.…
Have limited scope in practical applications as some of them
involve objective functions which are not continuous and/or
differentiable.
Basis for developing most of numerical techniques that involved
into advanced techniques more suitable to today’s practical
problem
6. Linear Program (LP)
studies the case in which the objective function (f ) is linear and the set design
variable space (A) is specified Using only linear equalities and inequalities.
2. Numerical Methods
7. Optimization Problem Types
Non-Linear Program (NLP)
studies the general case in which the objective function or the constraints or
both contain nonlinear parts.
(P) Convex problems easy to solve
Non-convex problems harder, not guaranteed to find global optimum
8. Optimization Problem Types
Integer Programs (IP)
studies linear programs in which some or all variables are constrained to
take on integer values
Quadratic programming
allows the objective functions to have quadratic terms, while the set (A) must be
specified with linear equalities and inequalities
9. Optimization Problem Types
Stochastic Programming
studies the case in which some of the constraints depend on random variables
Dynamic programming
studies the case in which the optimization strategy is based on splitting the
problem into smaller sub-problems.
10. 3. Advanced Methods
Swarm Intelligence Based Algorithms
Bio-inspired (not SI-based) algorithms
Physical and chemistry based algorithms
others
12. Introduction to Optimization
The optimization can be defined as a mechanism through which the maximum or minimum value of a
given function or process can be found.
The function that we try to minimize or maximize is called as objective function.
Variable and parameters.
Statement of optimization problem
Minimize f(x)
subject to g(x)<=0
h(x)=0.
13. Particle Swarm Optimization(PSO)
Inspired from the nature social behavior and dynamic movements with communications of insects,
birds and fish.
14. Particle Swarm Optimization(PSO)
Uses a number of agents (particles) that constitute a
swarm moving around in the search space looking for the
best solution
Each particle in search space adjusts its “flying” according to its
own flying experience as well as the flying experience of other
particles.
Each particle has three parameters position, velocity, and previous
best position, particle with best fitness value is called as global best
position.
15. Contd..
Collection of flying particles (swarm) - Changing solutions
Search area - Possible solutions
Movement towards a promising area to get the global optimum.
Each particle adjusts its travelling speed dynamically corresponding to the flying
experiences of itself and its colleagues.
Each particle keeps track:
its best solution, personal best, pbest.
the best value of any particle, global best, gbest.
Each particle modifies its position according to:
• its current position
• its current velocity
• the distance between its current position and pbest.
• the distance between its current position and gbest.
16. Algorithm - Parameters
f : Objective function
Xi: Position of the particle or agent.
Vi: Velocity of the particle or agent.
A: Population of agents.
W: Inertia weight.
C1: cognitive constant.
R1, R2: random numbers.
C2: social constant.
17. Algorithm - Steps
1. Create a ‘population’ of agents (particles) uniformly distributed over X
2. Evaluate each particle’s position according to the objective function( say
y=f(x)= -x^2+5x+20
1. If a particle’s current position is better than its previous best position, update it.
2. Determine the best particle (according to the particle’s previous best positions).
Y=F(x) = -x^2+5x+20
18. Contd..
5. Update particles’ velocities:
6. Move particles to their new positions:
7. Go to step 2 until stopping criteria are satisfied.
19. Contd…
Particle’s velocity
:
1. Inertia
2. Personal
Influence
3. Social
Influence
• Makes the particle move in the same direction and
with the same velocity
• Improves the individual
• Makes the particle return to a previous position,
better than the current
• Conservative
• Makes the particle follow the best neighbors
direction
20. Acceleration coefficients
• When , c1=c2=0 then all particles continue flying at their current speed until they hit the search space’s boundary.
Therefore, the velocity update equation is calculated as:
t
ij
t
ij v
v
1
• When c1>0 and c2=0 , all particles are independent. The velocity update equation will be:
t
ij
i
best
t
t
j
t
ij
t
ij x
P
r
c
v
v
,
1
1
1
• When c1>0 and c2=0 , all particles are attracted to a single point in the entire swarm and
the update velocity will become
t
ij
best
t
j
t
ij
t
ij x
g
r
c
v
v
2
2
1
• When c1=c2, all particles are attracted towards the average of pbest and gbest.
29. Grey wolf optimizer (GWO)
• The social hierarchy consists of four levels as
follow.
•The first level is called Alpha ( ). The alpha
wolves are the leaders of the pack and they are
a male and a female.
•They are responsible for making decisions
about hunting, time to walk, sleeping place and
so on.
30. Grey wolf optimizer (GWO)
• The alpha wolf is considered the dominant
wolf in the pack and all his/her orders should
be followed by the pack members.
Social hierarchy of grey wolf
31. Grey wolf optimizer (GWO)
•The second level is called Beta ( ).
•The betas are subordinate wolves, which help
the alpha in decision making.
•The beta wolf can be either male or female and
it consider the best candidate to be the alpha
when the alpha passes away or becomes very
old.
•The beta reinforce the alpha's commands
throughout the pack and gives the feedback to
alpha.
32. Grey wolf optimizer (GWO)
• The third level is called Delta ( )
• The delta wolves are not alpha or beta wolves
and they are called subordinates.
•Delta wolves have to submit to the alpha and
beta but they dominate the omega (the lowest
level in wolves social hierarchy).
33. Grey wolf optimizer (GWO)(History and main idea)
The fourth (lowest) level is called Omega ( )
•The omega wolves are considered the
scapegoat in the pack, they have to submit to
all the other dominant wolves.
•They may seem are not important individuals
in the pack and they are the last allowed
wolves to eat.
34. Social hierarchy of grey wolf
• In the grey wolf optimizer (GWO), we
consider the fittest solution as the alpha , and
the second and the third fittest solutions are
named beta and delta , respectively.
•The rest of the solutions are considered omega
•In GWO algorithm, the hunting is guided by
and
• The solutions follow these three wolves.
35. Grey wolf encircling prey
•During the hunting, the grey wolves encircle
prey.
•The mathematical model of the encircling
behavior is presented in the following
equations.
36. Grey wolf encircling prey (Cont.)
Where t is the current iteration, A and C are
coefficient vectors, Xp is the position vector of
the prey, and X indicates the position vector of
a grey wolf.
•The vectors A and C are calculated as follows:
Where components of a are linearly decreased
from 2 to 0 over the course of iterations and r1,
r2 are random vectors in [0, 1]
37. Grey wolf Hunting
•The hunting operation is usually guided by
the alpha .
•The beta and delta might participate in
hunting occasionally.
•In the mathematical model of hunting
behavior of grey wolves, we assumed the alpha
, beta and delta have better knowledge about
the potential location of prey.
•The first three best solutions are saved and the
other agent are oblige to update their positions
according to the position of the best search
agents as shown in the following equations.
39. Attacking prey (exploitation)
•The grey wolf finish the hunt by attacking the
prey when it stop moving.
•The vector A is a random value in interval
[-2a, 2a], where a is decreased from 2 to 0 over
the course of iterations.
When |A| < 1, the wolves attack towards the
prey, which represents an exploitation process.
40. Search for prey (exploration)
•The exploration process in GWO is applied
according to the position , and , that diverge
from each other to search for prey and
converge to attack prey.
•The exploration process modeled
mathematically by utilizing A with random
values greater than 1 or less than -1 to oblige
the search agent to diverge from the prey.
When |A| > 1, the wolves are forced to
diverge from the prey to fined a fitter prey.
41. Example (Unconstrained problem): Find the minimum of the function 𝑦 = (𝑥 − 3)2
By using GWO algorithm with the following parameters:
r1a: 0.273 r1b: 0.778 r1d: 0.222 MaxIter: 50
r2a: 0.718 r2b: 0.081 r2d: 0.204
Use initial positions [
6.6506
9.0463
−6.6383
−4.032
]
Solution :
𝒂 = 𝟐 (𝟏 −
𝒕
𝒎𝒂𝒙𝒊𝒕𝒆𝒓
) ,