Journal of Global Optimization
A Study of Global Optimization Using Particle Swarms
Journal of Global Optimization
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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During the past decade, the particle swarm optimization (PSO) with various versions showed competitiveness on the constrained optimization problems. In this paper, an improved Gaussian particle swarm optimization algorithm (GPSO) is proposed to improve the diversity and local search ability of the population. A mutation operator based on differential evolution (DE) is designed and employed to update the personal best position of the particle and the global best position of the population. The purpose is to improve the local search ability of GPSO and the probability to find the global optima. The regeneration strategy is employed to update the stagnated particle so as further to improve the diversity of GPSO. A simple feasibility-based method is employed to compare the performances of different particles. Simulation results of three constrained engineering optimization problems demonstrate the effectiveness of the proposed algorithm.