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 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.

[1]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[2]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[3]  A. Stacey,et al.  Particle swarm optimization with mutation , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[5]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  Thomas Kiel Rasmussen,et al.  Hybrid Particle Swarm Optimiser with breeding and subpopulations , 2001 .

[7]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[8]  Shao Zeng-zhen.  Particle swarm optimizer based on dynamic neighborhood topology and mutation operator , 2010 .

[9]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[10]  Paul S. Andrews,et al.  An Investigation into Mutation Operators for Particle Swarm Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[11]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[12]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[13]  Christian Posthoff,et al.  Neighborhood Re-structuring in Particle Swarm Optimization , 2005, Australian Conference on Artificial Intelligence.

[14]  Jing Liu,et al.  Using quantum-behaved particle swarm optimization algorithm to solve non-linear programming problems , 2007, Int. J. Comput. Math..

[15]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[16]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[17]  Martin Middendorf,et al.  A hierarchical particle swarm optimizer and its adaptive variant , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Hassan M. Emara,et al.  Clubs-based Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[19]  Kalyan Veeramachaneni,et al.  Fitness-distance-ratio based particle swarm optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[20]  R. Salomon.  Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. , 1996, Bio Systems.

[21]  Suganthan.  [IEEE 1999. Congress on Evolutionary Computation-CEC99 - Washington, DC, USA (6-9 July 1999)] Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) - Particle swarm optimiser with neighbourhood operator , 1999 .