Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.
Próxima SlideShare
Cargando en…5
×

# Harmony search algorithm

8.066 visualizaciones

Harmony search algorithm

• Full Name
Comment goes here.

Are you sure you want to Yes No
• Sé el primero en comentar

### Harmony search algorithm

1. 1. Scientific Research Group in Egypt (SRGE) Harmony search algorithm 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. 2. Company LOGO Scientific Research Group in Egypt www.egyptscience.net
3. 3. Company LOGO Outline 1.Harmony search algorithm (History and main idea) 2. Initialization of harmony memory 3. Improvisation of new harmony vectors 4. Harmony memory updating 5. Harmony search algorithm 6. Application of the harmony search Algorithm 7. References
4. 4. Company LOGO Harmony search algorithm (History and main idea) •Harmony search (HS) is a population based metaheuristics algorithm inspired from the musical process of searching for a perfect state of harmony. •HS has been proposed by Geem et al. in (2001) •The pitch of each musical instrument determines the aesthetic quality, just as the fitness function value determines the quality of the decision variables. •In the music improvisation process, all players sound pitches within possible range together to make one harmony.
5. 5. Company LOGO Harmony search algorithm (History and main idea) •If all pitches make a good harmony, each player stores in his memory that experience and the possibility of making a good harmony is increased next time. •The same thing in optimization, the initial solution is generated randomly from decision variables within the possible range. • If the objective function values of these decision variables is good to make a promising solution, then the possibility to make a good solution is increased next time.
6. 6. Company LOGO Initialization of harmony memory • The initial population HM contains of HMS vectors is generated randomly, where xi = xij , i = 1, …,HMS and j = 1, …, n. • The HM matrix is filled with HMS vectors as follows:
7. 7. Company LOGO Improvisation of new harmony vectors  Harmony memory considering (HMC) rule. •For this rule, a new random number r1 is generated within the range [0,1]. •If r1 < HMCR, where HMCR is the harmony memory consideration rate, then the first decision variable in the new vector xij {new} is chosen randomly from the values in the current HM as follows:
8. 8. Company LOGO Improvisation of new harmony vectors (cont) Pitch adjusting rate (PAR). • The obtained decision variables from the harmony memory consideration rule is further examined to determined if it needs to pitch adjustment or not. • A new random number r2 is generated within the range [0 1]. • If r2 < PAR, where PAR is a pitch adjustment rate, then the pitch adjustment decision variable is calculated as follows:
9. 9. Company LOGO Improvisation of new harmony vectors (cont) Pitch adjusting rate (PAR). where BW is a bandwidth factor, which is used to control the local search around the selected decision variable in the new vector.
10. 10. Company LOGO Improvisation of new harmony vectors (cont) Random initialization rule If the condition r1 < HMCR fails, the new first decision variable in the new vector x {new} ij is generated randomly as follows: where l, u is the lower and upper bound for the given problem.
11. 11. Company LOGO Harmony memory updating After the harmony vector x{new} is generated, it will replace the worst harmony vector x{worst} in the harmony memory if its objective function value is better than the objective function value of the worst harmony vector.
12. 12. Company LOGO Harmony search algorithm Parameter setting Initial population (harmony memory Memory consideration step Pitch adjustment Random initialization Harmony memory update
13. 13. Company LOGO Harmony search Flowchart
14. 14. Company LOGO Application of the HS Algorithm •Engineering optimization problems •NP hard combinatorial optimization problems •Data fusion in wireless sensor networks •Nanoelectronic technology based operation-amplifier • (OP-AMP) •Train neural network •Manufacturing scheduling •Nurse scheduling problem
15. 15. Company LOGO References •Z.W. Geem, J.-H. Kim, G.V. Loganathan, A new heuristic optimization algorithm: harmony search, Simulation 76 (2) (2001) 60–68. •K.S. Lee, Z.W. Geem, A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice, Comput. Methods Appl. Mech. Engrg. 194 (2005) 3902–3933
16. 16. Company LOGO Thank you Ahmed_fouad@ci.suez.edu.eg http://www.egyptscience.net