针对工程优化设计问题,提出了基于混沌粒子群算法的工程约束优化问题求解方法。CPSO算法利用混沌搜

索的全局遍历性、随机性和规律性等特点, 引导粒子在全局范围内搜索, 从而克服了传统粒子群算法早熟收敛的缺点。

该算法以种群适应度方差作为粒子群优化算法早熟收敛的判据, 并用惩罚函数法处理违法约束的粒子, 当基本粒子群算

法陷入早熟时, 随机选择粒子群中的部分粒子实施混沌搜索, 直至满足迭代收敛条件为止。CPSO算法能提高种群的多

样性和粒子搜索的遍历性, 从而有效提高了PSO算法的收敛速度和精度。两个工程约束优化实例的求解结果表明,该算

法的优化结果最好, 收敛速度也比较快-Based on engineering design optimization problems, and put forward based on chaotic particle swarm optimization algorithm of engineering problem solving methods. CPSO algorithm by using chaos search

The global ergodicity, stochastic characteristics and regularity, and lead particles in the global scope search, and overcome the traditional particle swarm algorithm premature convergence faults.

In this algorithm, the population fitness variance as the particle swarm optimization algorithm of the criterion of premature convergence, the penalty function method and deal with illegal constraint particles, when basic particle swarm to calculate

Law in early maturity, random selection of particle swarm of these particles implementation chaotic search, until the convergence conditions meet so far. CPSO algorithm can improve the population

Sample sex and particles of searching ergodicity, thus effectively improved PSO algorithm convergence speed and accuracy. Two engineering constraint optim