Comprehensive_learning_particle_swarm_optimizer_for_global_optimization_of_multimodal_functions.pdf

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 10, NO. 3, JUNE 2006 281 Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions J. J. Liang, A. K. Qin, Student Member, IEEE, Ponnuthurai Nagaratnam Suganthan, Senior Member, IEEE, and S. Baskar Abstract—This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning par- ticle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles’ historical best information is used to update a particle’s velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes available from .sg/home/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and composition functions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO. Index Terms—Composition benchmark functions, comprehen- sive learning particle swarm optimizer (CLPSO), global numerical optimization, particle swarm optimizer (PSO). I. INTRODUCTION OPTIMIZATION has been an active area of researchfor several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Unconstrained optimization problems can be formulated as a -dimensional minimization problem as follows: where is the number of the parameters to be optimized. The particle swarm optimizer (PSO) [1], [2] is a relatively new technique. Although PSO shares many similarities with evolutionary computation techniques, the standard PSO does not use evolution operators such as crossover and mutation. PSO emulates the swarm behavior of insects, animals herding, birds flocking, and fish schooling where these swarms search for food in a collaborative manner. Each member in the