A Novel PSO Model Based on Simulating Human Social Communication Behavior
In order to solve the complicated multimodal problems, this paper presents a variant of particle swarm optimizer (PSO) based on the simulation of the human social communication behavior (HSCPSO). In HSCPSO, each particle initially joins a default number of social circles (SC) that consist of some pa...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2012/791373 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832550671862202368 |
|---|---|
| author | Yanmin Liu Ben Niu |
| author_facet | Yanmin Liu Ben Niu |
| author_sort | Yanmin Liu |
| collection | DOAJ |
| description | In order to solve the complicated multimodal problems, this paper presents a variant of particle swarm optimizer (PSO) based on the simulation of the human social communication behavior (HSCPSO). In HSCPSO, each particle initially joins a default number of social circles (SC) that consist of some particles, and its learning exemplars include three parts, namely, its own best experience, the experience of the best performing particle in all SCs, and the experiences of the particles of all SCs it is a member of. The learning strategy takes full advantage of the excellent information of each particle to improve the diversity of the swarm to discourage premature convergence. To weight the effects of the particles on the SCs, the worst performing particles will join more SCs to learn from other particles and the best performing particles will leave SCs to reduce their strong influence on other members. Additionally, to insure the effectiveness of solving multimodal problems, the novel parallel hybrid mutation is proposed to improve the particle’s ability to escape from the local optima. Experiments were conducted on a set of classical benchmark functions, and the results demonstrate the good performance of HSCPSO in escaping from the local optima and solving the complex multimodal problems compared with the other PSO variants. |
| format | Article |
| id | doaj-art-0745943abd404aed890b8a16acf7636f |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-0745943abd404aed890b8a16acf7636f2025-02-03T06:06:01ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2012-01-01201210.1155/2012/791373791373A Novel PSO Model Based on Simulating Human Social Communication BehaviorYanmin Liu0Ben Niu1School of Economics and Management, Tongji University, Shanghai 200092, ChinaCollege of Management, Shenzhen University, Shenzhen 518060, ChinaIn order to solve the complicated multimodal problems, this paper presents a variant of particle swarm optimizer (PSO) based on the simulation of the human social communication behavior (HSCPSO). In HSCPSO, each particle initially joins a default number of social circles (SC) that consist of some particles, and its learning exemplars include three parts, namely, its own best experience, the experience of the best performing particle in all SCs, and the experiences of the particles of all SCs it is a member of. The learning strategy takes full advantage of the excellent information of each particle to improve the diversity of the swarm to discourage premature convergence. To weight the effects of the particles on the SCs, the worst performing particles will join more SCs to learn from other particles and the best performing particles will leave SCs to reduce their strong influence on other members. Additionally, to insure the effectiveness of solving multimodal problems, the novel parallel hybrid mutation is proposed to improve the particle’s ability to escape from the local optima. Experiments were conducted on a set of classical benchmark functions, and the results demonstrate the good performance of HSCPSO in escaping from the local optima and solving the complex multimodal problems compared with the other PSO variants.http://dx.doi.org/10.1155/2012/791373 |
| spellingShingle | Yanmin Liu Ben Niu A Novel PSO Model Based on Simulating Human Social Communication Behavior Discrete Dynamics in Nature and Society |
| title | A Novel PSO Model Based on Simulating Human Social Communication Behavior |
| title_full | A Novel PSO Model Based on Simulating Human Social Communication Behavior |
| title_fullStr | A Novel PSO Model Based on Simulating Human Social Communication Behavior |
| title_full_unstemmed | A Novel PSO Model Based on Simulating Human Social Communication Behavior |
| title_short | A Novel PSO Model Based on Simulating Human Social Communication Behavior |
| title_sort | novel pso model based on simulating human social communication behavior |
| url | http://dx.doi.org/10.1155/2012/791373 |
| work_keys_str_mv | AT yanminliu anovelpsomodelbasedonsimulatinghumansocialcommunicationbehavior AT benniu anovelpsomodelbasedonsimulatinghumansocialcommunicationbehavior AT yanminliu novelpsomodelbasedonsimulatinghumansocialcommunicationbehavior AT benniu novelpsomodelbasedonsimulatinghumansocialcommunicationbehavior |