一种求解约束优化问题的混沌文化算法

Cultural algorithm for solving constrained optimization problems

在求解约束优化问题时,为了有效处理约束条件,克服文化算法易陷入局部极值点、混沌搜索优化初值敏感、搜索效率低等缺陷,将混沌搜索优化嵌入至文化算法框架,提出一种求解约束优化问题的混沌文化算法。该模型由基于混沌的群体空间和存储知识的信念空间组成,利用地形知识表达约束条件,标准知识和地形知识共同引导混沌搜索,并利用形势知识引导混沌扰动。实例表明,该算法具有较优良的搜索性能,尤其能有效处理高维复杂约束优化问题。

In solving constrained optimization problems, based on cultural algorithm and chaos search optimization,this paper proposed a chaos cultural algorithm （CCA）to effectively handle the constraints, avoided premature convergence of cultural algorithm and overcame chaos search optimization’s sensitivity to initial values and poor efficiency. The algorithm model consisted of a chaos-based population space and a stored knowledge belief space, using topographical knowledge to represent constraint condition, normative knowledge to guide chaos search and situational knowledge to guide chaos perturbation.Test results show that this algorithm has good searching performance, especially in solving complex constraints optimization problem.