Current position-based Fitness Euclidean-distance Ratio Particle Swarm Optimizer for multi-modal optimization

In this paper, the nonlinear constrained multi-objective environmental economic dispatch (EED) problem is solved using fast multi-objective evolutionary programming (FMOEP). Due to the global warming by fossil fuel, environmental concern becomes more and more important in recent years. The purpose of multi-objective optimization algorithm is minimizing all the different objectives simultaneously and finds the best tradeoff solution for this environmental/economic dispatch problem. In order to evaluate the performance of FMOEP on EED problems, the standard IEEE 30-bus six-generator test system is studied. The performance is compared against NSGAII and a number of results reported in literature. The results show that the FMOEP is effective in solving EED problems.

[1]  Xiaodong Li,et al.  A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio , 2007, GECCO '07.

[2]  P. John Clarkson,et al.  A Species Conserving Genetic Algorithm for Multimodal Function Optimization , 2002, Evolutionary Computation.

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

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

[5]  Frans van den Bergh,et al.  A NICHING PARTICLE SWARM OPTIMIZER , 2002 .

[6]  Xiaodong Li,et al.  Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization , 2004, GECCO.

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

[8]  Xiaodong Li,et al.  Erratum to "Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology" [Feb 10 150-169] , 2010, IEEE Trans. Evol. Comput..

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

[10]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

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

[12]  David H. Ackley,et al.  An empirical study of bit vector function optimization , 1987 .

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

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

[15]  K. Koper,et al.  Multimodal function optimization with a niching genetic algorithm: A seismological example , 1999, Bulletin of the Seismological Society of America.

[16]  Xiaodong Li,et al.  Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology , 2010, IEEE Transactions on Evolutionary Computation.

[17]  Samir W. Mahfoud.  Niching methods for genetic algorithms , 1996 .

[18]  Ofer M. Shir,et al.  Niche Radius Adaptation in the CMA-ES Niching Algorithm , 2006, PPSN.