A Novel Niche Genetic Algorithm for Multimodal Optimization

Genetic drift usually takes place when we use genetic algorithm to calculate multimodal functions. It causes the calculations can not obtain multiple globally optimization solutions. For making up this shortage, we propose a new crowding niche genetic algorithm based on the most similar individuals (MSICNGA). This new algorithm adds two novel mechanisms: crowding mechanism based on the most similar individuals and crowding errors repair mechanism. The crowding mechanism aims to maintain population diversity through repeatedly excluding the inferior one of the two most similar individuals in current population; it is able to effectively avoid the setting of any parameters related to the priori knowledge about optimization functions in the operation process. The repair mechanism is used to protect optimum solutions from loss to repair crowding errors through storing the better ones among the individuals crowded out in the operation process; it is capable of making sure algorithm finds out multiple solutions more easily. In order to assess the performance of MSICNGA, we adopt four typical benchmarks to make optimization experiments, and compare the performance with other two commonly known algorithms. Results and analysis show that MSICNGA is able to well maintain population diversity and easily search multiple solutions while calculating multimodal functions, besides, it takes on a better stability and universality for optimizing various kinds of problems.

[1]  Samir W. Mahfoud.  Crowding and Preselection Revisited , 1992, PPSN.

[2]  D. J. Cavicchio,et al.  Adaptive search using simulated evolution , 1970 .

[3]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[4]  David E. Goldberg,et al.  Probabilistic Crowding: Deterministic Crowding with Probabilistic Replacement , 1999 .

[5]  Lilin Fan,et al.  Application Research on Optimal Path Based on Genetic Algorithm , 2010, J. Digit. Content Technol. its Appl..

[6]  Alain Pétrowski,et al.  A clearing procedure as a niching method for genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[7]  Behrouz Minaei-Bidgoli,et al.  Clustering Based Multi-Objective Rule Mining using Genetic Algorithm , 2010, J. Digit. Content Technol. its Appl..

[8]  Yonggang Zhang,et al.  An Improved Genetic Algorithm for Optimal Tree Decomposition of Bayesian Networks , 2011 .

[9]  Kevin Warwick,et al.  A Genetic Algorithm with Dynamic Niche Clustering for Multimodal Function Optimisation , 1999, ICANNGA.

[10]  Georges R. Harik,et al.  Finding Multimodal Solutions Using Restricted Tournament Selection , 1995, ICGA.

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

[12]  Michael J. Shaw,et al.  Genetic algorithms with dynamic niche sharing for multimodal function optimization , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[13]  V. Rao Vemuri,et al.  Analysis of Speciation and Niching in the Multi-Niche Crowding GA , 1999, Theor. Comput. Sci..

[14]  K. Warwick,et al.  Dynamic Niche Clustering: a fuzzy variable radius niching technique for multimodal optimisation in GAs , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[15]  Xiaodong Yin,et al.  A Fast Genetic Algorithm with Sharing Scheme Using Cluster Analysis Methods in Multimodal Function Optimization , 1993 .