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中文摘要：
使用混沌运动产生均匀分布的初始种群,并且对早熟的种群进行混沌变异,以增强算法的全局寻优能力;用一个改进的粒子群优化算法对种群进化,对那些不可行的粒子再用差分进化算法进行演化;通过自适应的半可行域竞争选择策略形成新一代种群,直到达到全局寻优的目的,由此提出一个约束优化问题带有混沌变异的PSO-DE混合算法.数值结果表明,所提出的算法具有较高的计算精度、较好的稳定性、较强的全局寻优能力.
英文摘要：
An initial population with uniform distribution was generated by using chaotic motion and the premature populations were chaos-mutated so as to enhance the algorithm ability with global optimization.The population was evolved with an improved particle swarm optimization algorithm,and then,those infeasible particles were evolved by using differential evolution algorithm.By means of adaptive semi-feasible region selection strategy,a new generation population was to be formed until a global optimization would be achieved.Thus a PSO-DE hybrid algorithm with chaotic mutation was presented for solving constrained optimization problems.The numerical results showed that the proposed algorithm exhibited higher accuracy,better stability,and stronger ability of global optimization.
同期刊论文项目
同项目期刊论文
Modified Multi-Objective Particle Swarm Optimization Algorithm for Multi-objective Optimization Prob
Modified differential evolution algorithm of constrained nonlinear mixed integer programming problem
PSO algorithm with chaos and gene density mutation for solving nonlinear zero-one integer programmin
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