Abstract
Aiming at the the poor local search capability of Particle Swarm Optimization(PSO) algorithm, a hybrid particle swarm optimization algorithm is proposed. Firstly, the population is initialized by tent chaotic map to improve the diversity of the initial population. In the evolution process, the tabu search strategy is adopted to improve algorithm convergence rate. Combining the chaos optimization strategy, this algorithm could jump out of local optimization and improve the local search ability. The simulation results of constrained optimization problems are reported and compared with the typical PSO algorithm. Simulation results show that this algorithm could effectively avoid local optimization, have good global search ability and local search ability.
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