Recent approaches to global optimization problems through Particle Swarm Optimization

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and 1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given. Key words: Differential Evolution, Evolutionary Computation, Global Optimization, Integer Programming, Matlab Code Implementation, Minimax Problems, Multiobjective Optimization, Noisy Problems, Particle Swarm Optimization, Swarm Intelligence Abbreviations: ANN

• Type: Journal
• Year: 2002
• Where: NC
• Authors: Konstantinos E. Parsopoulos, Michael N. Vrahatis
• Keywords: Multiobjective Optimization, NC 2002, Neural Networks, Particle Swarm Optimization, Weighted Aggregation