A novel adaptive sequential niche technique for multimodal function optimization

Abstract This paper proposes a novel adaptive sequential niche particle swarm optimization (ASNPSO) algorithm, which uses multiple sub-swarms to detect optimal solutions sequentially. In this algorithm, the hill valley function is used to determine how to change the fitness of a particle in a sub-swarm run currently. This algorithm has strong and adaptive searching ability. The experimental results show that the proposed ASNPSO algorithm is very effective and efficient in searching for multiple optimal solutions for benchmark test functions without any prior knowledge.

[1]  Ralph R. Martin,et al.  A Sequential Niche Technique for Multimodal Function Optimization , 1993, Evolutionary Computation.

[2]  Russell C. Eberhart,et al.  Human tremor analysis using particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[3]  W. Godwin Article in Press , 2000 .

[4]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[5]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

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

[7]  Samir W. Mahfoud A Comparison of Parallel and Sequential Niching Methods , 1995, ICGA.

[8]  R. K. Ursem Multinational evolutionary algorithms , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[9]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[10]  Lin Guo,et al.  Combining genetic optimisation with hybrid learning algorithm for radial basis function neural networks , 2003 .

[11]  Daniela Zaharie A MULTIPOPULATION DIFFERENTIAL EVOLUTION ALGORITHM FOR MULTIMODAL OPTIMIZATION , 2004 .

[12]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[13]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .