SlideShare una empresa de Scribd logo
1 de 19
Scientific Research Group in Egypt (SRGE) 
Gravitational search algorithm 
(GSA) 
Dr. Ahmed Fouad Ali 
Suez Canal University, 
Dept. of Computer Science, Faculty of Computers and informatics 
Member of the Scientific Research Group in Egypt . 
Company 
LOGO
Company 
LOGO Scientific Research Group in Egypt 
www.egyptscience.net
Company 
LOGO Outline 
1. Gravitational search algorithm (History and main idea) 
2. Gravitational constant G 
3. The gravity low 
4. Acceleration of agents 
5. Agent velocity and positions 
6. Gravitational search algorithm 
7. References
Company 
LOGO Gravitational search algorithm (History and main idea) 
•Gravitational search algorithm (GSA) is a 
population search algorithm proposed by 
Rashedi et al. in 2009. 
• The GSA is based on the low of gravity 
and mass interactions. 
•The solutions in the GSA population are 
called agents, these agents interact with 
each other through the gravity force. 
•The performance of each agent in the 
population is measured by its mass.
Company 
LOGO Gravitational search algorithm (History and main idea) 
•Each agent is considered as object and all 
objects move towards other objects with 
heavier mass due to the gravity force. 
•This step represents a global movements 
(exploration step) of the object, while the 
agent with a heavy mass moves slowly, 
which represents the exploitation step of 
the algorithm. 
•The best solution is the solution with the 
heavier mass.
Company 
LOGO Gravitational constant G 
•The gravitational constant G at iteration 
t is computed as follows. 
(1) 
•Where G0 and 훼 are initialized in the 
beginning of the search, and their 
values will be reduced during the 
search. T is the total number of 
iterations.
Company 
LOGO The gravity low 
•The objects masses are obeying the low 
of gravity as following 
(2) 
•Equation 2 represents the Newton law 
of gravity, where 
• F is a magnitude of the gravitational 
force 
• G is gravitational constant 
•M1 is the mass of the first object 
•M2 is the mass of the second object 
•R is the distance between the two 
objects M1, M2.
Company 
LOGO The gravity low (Cont.) 
•According to the Newton's second low, 
when a force F is applied to an object, 
the object moves with acceleration a 
depending on the applied force and the 
object mass M as shown in Equation 3. 
(3)
Company 
LOGO Acceleration of agents 
There are three kind of masses 
Active gravitational mass Ma 
Passive gravitational mass Mp 
Inertial mass Mi. 
The gravitational force Fij that acts 
on mass i by mass j is defined by: 
(4) 
Where Maj, Mpi are the active and 
passive masses of objects j, i, 
respectively.
Company 
LOGO Acceleration of agents (Cont.) 
•The acceleration of object (agent) i is 
computed as follows. 
(5) 
Where Mii is inertia mass of agent i.
Company 
LOGO Agent velocity and positions 
•During the search, the agents update 
their velocities and positions as shown in 
Equations 6, 7, respectively. 
(6) 
(7)
Company 
LOGO Gravitational search algorithm 
The main steps of the GSA can be 
summarized as follows. 
 Step 1. The algorithm starts by 
setting the initial values of 
gravitational constant G0, 훼, 휀 and the 
iteration counter t. 
Step 2. The initial population is 
generated randomly and consists of N 
agents, the position of each agent is 
defined by:
Company 
LOGO Gravitational search algorithm (Cont.) 
Step 3. The following steps are 
repeated until termination criteria 
satisfied 
 Step 3.1. All agents in the population 
are evaluated and the best, worst agents 
are assigned. 
Step 3.2. The gravitational constant is 
updated as shown in Equation 1
Company 
LOGO Gravitational search algorithm (Cont.) 
Step 3.3. When agent j acts on agent i with force, at a 
specific time (t) the force is calculated as following: 
(8) 
Where Maj is the active gravitational mass of agent j, Mpi is 
the passive gravitational mass of agent i, G(t) is 
gravitational constant at time t
Company 
LOGO Gravitational search algorithm (Cont.) 
Step 3.4. At iteration t, calculate the total force acting on 
agent i as following: 
(9) 
Where Kbest is the set of first K agents with the best fitness 
value and biggest mass 
Step 3.5. Calculate the inertial mass as following: 
(10) 
(11)
Company 
LOGO Gravitational search algorithm (Cont.) 
Step 3.6. The acceleration of agent i is calculated as 
following: 
(12) 
Step 3.7. The velocity and the position of agent i are 
computed as shown in Equations 6, 7 
Step 3.8. The iteration counter is increased until 
termination criteria satisfied 
Step 4. The best optimal solution is produced.
Company 
LOGO Gravitational search algorithm (Cont.) 
Parameters 
initialization 
Initial population 
Solution s evaluation 
Solutions Update 
Produce the best solution
Company 
LOGO References 
E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, GSA: “A 
Gravitational Search Algorithm ”, Information Sciences, vol. 
179, no. 13, pp.2232-2248, 2009.
Company 
LOGO 
Thank you 
Ahmed_fouad@ci.suez.edu.eg 
http://www.egyptscience.net

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Particle swarm optimization
Particle swarm optimizationParticle swarm optimization
Particle swarm optimization
 
Genetic Algorithms - Artificial Intelligence
Genetic Algorithms - Artificial IntelligenceGenetic Algorithms - Artificial Intelligence
Genetic Algorithms - Artificial Intelligence
 
Particle swarm optimization
Particle swarm optimization Particle swarm optimization
Particle swarm optimization
 
Hill climbing algorithm
Hill climbing algorithmHill climbing algorithm
Hill climbing algorithm
 
Genetic Algorithm in Artificial Intelligence
Genetic Algorithm in Artificial IntelligenceGenetic Algorithm in Artificial Intelligence
Genetic Algorithm in Artificial Intelligence
 
Introduction to genetic programming
Introduction to genetic programmingIntroduction to genetic programming
Introduction to genetic programming
 
Sunflower optimization algorithm
Sunflower optimization algorithmSunflower optimization algorithm
Sunflower optimization algorithm
 
Structure of agents
Structure of agentsStructure of agents
Structure of agents
 
PSO
PSOPSO
PSO
 
Intelligent agents
Intelligent agentsIntelligent agents
Intelligent agents
 
Differential Evolution Algorithm (DEA)
Differential Evolution Algorithm (DEA) Differential Evolution Algorithm (DEA)
Differential Evolution Algorithm (DEA)
 
ABC Algorithm.
ABC Algorithm.ABC Algorithm.
ABC Algorithm.
 
Firefly algorithm
Firefly algorithmFirefly algorithm
Firefly algorithm
 
AI
AIAI
AI
 
Flowchart of GA
Flowchart of GAFlowchart of GA
Flowchart of GA
 
Evolutionary Computing - Genetic Algorithms - An Introduction
Evolutionary Computing - Genetic Algorithms - An IntroductionEvolutionary Computing - Genetic Algorithms - An Introduction
Evolutionary Computing - Genetic Algorithms - An Introduction
 
Crow search algorithm
Crow search algorithmCrow search algorithm
Crow search algorithm
 
Differential evolution
Differential evolutionDifferential evolution
Differential evolution
 
Genetic programming
Genetic programmingGenetic programming
Genetic programming
 
Radial basis function network ppt bySheetal,Samreen and Dhanashri
Radial basis function network ppt bySheetal,Samreen and DhanashriRadial basis function network ppt bySheetal,Samreen and Dhanashri
Radial basis function network ppt bySheetal,Samreen and Dhanashri
 

Destacado

Cuckoo search algorithm
Cuckoo search algorithmCuckoo search algorithm
Cuckoo search algorithm
Ritesh Kumar
 
Turning robot locomotion using truncated fourier series and gravitational sea...
Turning robot locomotion using truncated fourier series and gravitational sea...Turning robot locomotion using truncated fourier series and gravitational sea...
Turning robot locomotion using truncated fourier series and gravitational sea...
kameltreen
 

Destacado (20)

Cuckoo search algorithm
Cuckoo search algorithmCuckoo search algorithm
Cuckoo search algorithm
 
Newtonian Law Inspired Optimization Techniques Based on Gravitational Search ...
Newtonian Law Inspired Optimization Techniques Based on Gravitational Search ...Newtonian Law Inspired Optimization Techniques Based on Gravitational Search ...
Newtonian Law Inspired Optimization Techniques Based on Gravitational Search ...
 
Whale optimizatio algorithm
Whale optimizatio algorithmWhale optimizatio algorithm
Whale optimizatio algorithm
 
Cuckoo Optimization ppt
Cuckoo Optimization pptCuckoo Optimization ppt
Cuckoo Optimization ppt
 
Artificial fish swarm optimization
Artificial fish swarm optimizationArtificial fish swarm optimization
Artificial fish swarm optimization
 
Flower pollination
Flower pollinationFlower pollination
Flower pollination
 
Bat algorithm
Bat algorithmBat algorithm
Bat algorithm
 
Bat algorithm and applications
Bat algorithm and applicationsBat algorithm and applications
Bat algorithm and applications
 
Cuckoo search algorithm
Cuckoo search algorithmCuckoo search algorithm
Cuckoo search algorithm
 
Latex symbols and commands
Latex symbols  and commandsLatex symbols  and commands
Latex symbols and commands
 
Ant colony algorithm
Ant colony algorithm Ant colony algorithm
Ant colony algorithm
 
Backtraking optimziation algorithm
Backtraking optimziation algorithmBacktraking optimziation algorithm
Backtraking optimziation algorithm
 
Spider Monkey Optimization Algorithm
Spider Monkey Optimization AlgorithmSpider Monkey Optimization Algorithm
Spider Monkey Optimization Algorithm
 
Social spider optimization
Social spider optimizationSocial spider optimization
Social spider optimization
 
Think and grow rich
Think and grow richThink and grow rich
Think and grow rich
 
Turning robot locomotion using truncated fourier series and gravitational sea...
Turning robot locomotion using truncated fourier series and gravitational sea...Turning robot locomotion using truncated fourier series and gravitational sea...
Turning robot locomotion using truncated fourier series and gravitational sea...
 
Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization
Flower Pollination Algorithm: A Novel Approach for Multiobjective OptimizationFlower Pollination Algorithm: A Novel Approach for Multiobjective Optimization
Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization
 
Firefly algorithm
Firefly algorithmFirefly algorithm
Firefly algorithm
 
A Comparison between FPPSO and B&B Algorithm for Solving Integer Programming ...
A Comparison between FPPSO and B&B Algorithm for Solving Integer Programming ...A Comparison between FPPSO and B&B Algorithm for Solving Integer Programming ...
A Comparison between FPPSO and B&B Algorithm for Solving Integer Programming ...
 
Eee3420 lecture05 rev2011
Eee3420 lecture05 rev2011Eee3420 lecture05 rev2011
Eee3420 lecture05 rev2011
 

Similar a Gravitational search algorithm

Graph 1= no massGraph 2= no massGraph 3 mass added .docx
Graph 1= no massGraph 2= no massGraph 3 mass added .docxGraph 1= no massGraph 2= no massGraph 3 mass added .docx
Graph 1= no massGraph 2= no massGraph 3 mass added .docx
whittemorelucilla
 
Implementing Generate-Test-and-Aggregate Algorithms on Hadoop
Implementing Generate-Test-and-Aggregate Algorithms on HadoopImplementing Generate-Test-and-Aggregate Algorithms on Hadoop
Implementing Generate-Test-and-Aggregate Algorithms on Hadoop
Yu Liu
 
1 Lab 3 Newton’s Second Law of Motion Introducti.docx
1 Lab 3 Newton’s Second Law of Motion  Introducti.docx1 Lab 3 Newton’s Second Law of Motion  Introducti.docx
1 Lab 3 Newton’s Second Law of Motion Introducti.docx
mercysuttle
 

Similar a Gravitational search algorithm (20)

PSOC presentation.pptx
PSOC presentation.pptxPSOC presentation.pptx
PSOC presentation.pptx
 
GSA-BBO HYBRIDIZATION ALGORITHM
GSA-BBO HYBRIDIZATION ALGORITHMGSA-BBO HYBRIDIZATION ALGORITHM
GSA-BBO HYBRIDIZATION ALGORITHM
 
Software Effort Estimation Using Particle Swarm Optimization with Inertia Weight
Software Effort Estimation Using Particle Swarm Optimization with Inertia WeightSoftware Effort Estimation Using Particle Swarm Optimization with Inertia Weight
Software Effort Estimation Using Particle Swarm Optimization with Inertia Weight
 
Graph 1= no massGraph 2= no massGraph 3 mass added .docx
Graph 1= no massGraph 2= no massGraph 3 mass added .docxGraph 1= no massGraph 2= no massGraph 3 mass added .docx
Graph 1= no massGraph 2= no massGraph 3 mass added .docx
 
Population based optimization algorithms improvement using the predictive par...
Population based optimization algorithms improvement using the predictive par...Population based optimization algorithms improvement using the predictive par...
Population based optimization algorithms improvement using the predictive par...
 
Chapter5.pdf
Chapter5.pdfChapter5.pdf
Chapter5.pdf
 
M017127578
M017127578M017127578
M017127578
 
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...
 
Implementing Generate-Test-and-Aggregate Algorithms on Hadoop
Implementing Generate-Test-and-Aggregate Algorithms on HadoopImplementing Generate-Test-and-Aggregate Algorithms on Hadoop
Implementing Generate-Test-and-Aggregate Algorithms on Hadoop
 
Proposing a scheduling algorithm to balance the time and cost using a genetic...
Proposing a scheduling algorithm to balance the time and cost using a genetic...Proposing a scheduling algorithm to balance the time and cost using a genetic...
Proposing a scheduling algorithm to balance the time and cost using a genetic...
 
An efficient use of temporal difference technique in Computer Game Learning
An efficient use of temporal difference technique in Computer Game LearningAn efficient use of temporal difference technique in Computer Game Learning
An efficient use of temporal difference technique in Computer Game Learning
 
1 Lab 3 Newton’s Second Law of Motion Introducti.docx
1 Lab 3 Newton’s Second Law of Motion  Introducti.docx1 Lab 3 Newton’s Second Law of Motion  Introducti.docx
1 Lab 3 Newton’s Second Law of Motion Introducti.docx
 
Optimization and particle swarm optimization (O & PSO)
Optimization and particle swarm optimization (O & PSO) Optimization and particle swarm optimization (O & PSO)
Optimization and particle swarm optimization (O & PSO)
 
Reinforcement Learning Guide For Beginners
Reinforcement Learning Guide For BeginnersReinforcement Learning Guide For Beginners
Reinforcement Learning Guide For Beginners
 
Performance Analysis of GA and PSO over Economic Load Dispatch Problem
Performance Analysis of GA and PSO over Economic Load Dispatch ProblemPerformance Analysis of GA and PSO over Economic Load Dispatch Problem
Performance Analysis of GA and PSO over Economic Load Dispatch Problem
 
PPT - Discovering Reinforcement Learning Algorithms
PPT - Discovering Reinforcement Learning AlgorithmsPPT - Discovering Reinforcement Learning Algorithms
PPT - Discovering Reinforcement Learning Algorithms
 
Comparative study of different algorithms
Comparative study of different algorithmsComparative study of different algorithms
Comparative study of different algorithms
 
Lecture 07 search techniques
Lecture 07 search techniquesLecture 07 search techniques
Lecture 07 search techniques
 
Unit 5 Introduction to Planning and ANN.pptx
Unit 5 Introduction to Planning and ANN.pptxUnit 5 Introduction to Planning and ANN.pptx
Unit 5 Introduction to Planning and ANN.pptx
 
0415_seminar_DeepDPG
0415_seminar_DeepDPG0415_seminar_DeepDPG
0415_seminar_DeepDPG
 

Más de Ahmed Fouad Ali (11)

Manta Ray Optimization.pptx
Manta Ray Optimization.pptxManta Ray Optimization.pptx
Manta Ray Optimization.pptx
 
Harris hawks optimization
Harris hawks optimizationHarris hawks optimization
Harris hawks optimization
 
Butterfly optimization algorithm
Butterfly optimization algorithmButterfly optimization algorithm
Butterfly optimization algorithm
 
Salp swarm algorithm
Salp swarm algorithmSalp swarm algorithm
Salp swarm algorithm
 
Grasshopper optimization algorithm
Grasshopper optimization algorithmGrasshopper optimization algorithm
Grasshopper optimization algorithm
 
Firefly algorithm
Firefly algorithmFirefly algorithm
Firefly algorithm
 
bat algorithm
bat algorithmbat algorithm
bat algorithm
 
Tabu search
Tabu searchTabu search
Tabu search
 
Simulated annealing
Simulated annealingSimulated annealing
Simulated annealing
 
Variable neighborhood search
Variable neighborhood searchVariable neighborhood search
Variable neighborhood search
 
Group search optimizer
Group search optimizerGroup search optimizer
Group search optimizer
 

Último

The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
heathfieldcps1
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global Impact
PECB
 

Último (20)

Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
 
Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..Sports & Fitness Value Added Course FY..
Sports & Fitness Value Added Course FY..
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
 
ICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptx
 
Application orientated numerical on hev.ppt
Application orientated numerical on hev.pptApplication orientated numerical on hev.ppt
Application orientated numerical on hev.ppt
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activity
 
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 
Web & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfWeb & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdf
 
PROCESS RECORDING FORMAT.docx
PROCESS      RECORDING        FORMAT.docxPROCESS      RECORDING        FORMAT.docx
PROCESS RECORDING FORMAT.docx
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global Impact
 
This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
Class 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdfClass 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdf
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
fourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingfourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writing
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
 
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
 
Unit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptxUnit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptx
 

Gravitational search algorithm

  • 1. Scientific Research Group in Egypt (SRGE) Gravitational search algorithm (GSA) Dr. Ahmed Fouad Ali Suez Canal University, Dept. of Computer Science, Faculty of Computers and informatics Member of the Scientific Research Group in Egypt . Company LOGO
  • 2. Company LOGO Scientific Research Group in Egypt www.egyptscience.net
  • 3. Company LOGO Outline 1. Gravitational search algorithm (History and main idea) 2. Gravitational constant G 3. The gravity low 4. Acceleration of agents 5. Agent velocity and positions 6. Gravitational search algorithm 7. References
  • 4. Company LOGO Gravitational search algorithm (History and main idea) •Gravitational search algorithm (GSA) is a population search algorithm proposed by Rashedi et al. in 2009. • The GSA is based on the low of gravity and mass interactions. •The solutions in the GSA population are called agents, these agents interact with each other through the gravity force. •The performance of each agent in the population is measured by its mass.
  • 5. Company LOGO Gravitational search algorithm (History and main idea) •Each agent is considered as object and all objects move towards other objects with heavier mass due to the gravity force. •This step represents a global movements (exploration step) of the object, while the agent with a heavy mass moves slowly, which represents the exploitation step of the algorithm. •The best solution is the solution with the heavier mass.
  • 6. Company LOGO Gravitational constant G •The gravitational constant G at iteration t is computed as follows. (1) •Where G0 and 훼 are initialized in the beginning of the search, and their values will be reduced during the search. T is the total number of iterations.
  • 7. Company LOGO The gravity low •The objects masses are obeying the low of gravity as following (2) •Equation 2 represents the Newton law of gravity, where • F is a magnitude of the gravitational force • G is gravitational constant •M1 is the mass of the first object •M2 is the mass of the second object •R is the distance between the two objects M1, M2.
  • 8. Company LOGO The gravity low (Cont.) •According to the Newton's second low, when a force F is applied to an object, the object moves with acceleration a depending on the applied force and the object mass M as shown in Equation 3. (3)
  • 9. Company LOGO Acceleration of agents There are three kind of masses Active gravitational mass Ma Passive gravitational mass Mp Inertial mass Mi. The gravitational force Fij that acts on mass i by mass j is defined by: (4) Where Maj, Mpi are the active and passive masses of objects j, i, respectively.
  • 10. Company LOGO Acceleration of agents (Cont.) •The acceleration of object (agent) i is computed as follows. (5) Where Mii is inertia mass of agent i.
  • 11. Company LOGO Agent velocity and positions •During the search, the agents update their velocities and positions as shown in Equations 6, 7, respectively. (6) (7)
  • 12. Company LOGO Gravitational search algorithm The main steps of the GSA can be summarized as follows.  Step 1. The algorithm starts by setting the initial values of gravitational constant G0, 훼, 휀 and the iteration counter t. Step 2. The initial population is generated randomly and consists of N agents, the position of each agent is defined by:
  • 13. Company LOGO Gravitational search algorithm (Cont.) Step 3. The following steps are repeated until termination criteria satisfied  Step 3.1. All agents in the population are evaluated and the best, worst agents are assigned. Step 3.2. The gravitational constant is updated as shown in Equation 1
  • 14. Company LOGO Gravitational search algorithm (Cont.) Step 3.3. When agent j acts on agent i with force, at a specific time (t) the force is calculated as following: (8) Where Maj is the active gravitational mass of agent j, Mpi is the passive gravitational mass of agent i, G(t) is gravitational constant at time t
  • 15. Company LOGO Gravitational search algorithm (Cont.) Step 3.4. At iteration t, calculate the total force acting on agent i as following: (9) Where Kbest is the set of first K agents with the best fitness value and biggest mass Step 3.5. Calculate the inertial mass as following: (10) (11)
  • 16. Company LOGO Gravitational search algorithm (Cont.) Step 3.6. The acceleration of agent i is calculated as following: (12) Step 3.7. The velocity and the position of agent i are computed as shown in Equations 6, 7 Step 3.8. The iteration counter is increased until termination criteria satisfied Step 4. The best optimal solution is produced.
  • 17. Company LOGO Gravitational search algorithm (Cont.) Parameters initialization Initial population Solution s evaluation Solutions Update Produce the best solution
  • 18. Company LOGO References E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, GSA: “A Gravitational Search Algorithm ”, Information Sciences, vol. 179, no. 13, pp.2232-2248, 2009.
  • 19. Company LOGO Thank you Ahmed_fouad@ci.suez.edu.eg http://www.egyptscience.net