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利用切比雪夫(Chebyshev)映射在[-1,1]区间上的遍历性和随机性,提出了一种基于切比雪夫映射的新型混沌粒子群优化(CPSO)算法.该算法在粒子群算法求出的最优解附近进行混沌搜索,提高了混沌粒子群算法的全局优化能力,能有效避免算法容易陷入局部最优以及解决逻辑斯谛(logistic)映射不能在负值区间进行搜索的问题.针对模型中复杂的约束条件,采用分段线性插值函数实现了对目标函数的求解,并通过对采用丰枯电价时三峡梯级水电系统长期优化调度问题的计算及与其他算法的对比,验证了该算法可解决具有复杂约束条件的工程优化问题.
Based on the ergodicity and randomness of the Chebyshev map on the [-1,1] interval, a new chaotic particle swarm optimization (CPSO) algorithm based on Chebyshev mapping is proposed, which is based on Particle Swarm Optimization Chaos particle swarm optimization algorithm can improve the global optimization ability of the chaotic particle swarm optimization algorithm, and can effectively avoid the algorithm from falling into the local optimum and solve the problem that the logistic map can not search in the negative range. Aiming at the complicated constraints in the model, the piecewise linear interpolation function is used to solve the objective function. The calculation of the long-term optimal scheduling problem of the Three Gorges Cascaded Hydroelectric Power Plant when comparing with the other algorithms is validated by comparing with other algorithms The algorithm can solve the engineering optimization problem with complex constraints.