多维度惯性权重衰减混沌化粒子群算法及应用

针对标准粒子群优化算法在处理多维、多峰值优化问题时暴露出的易早熟收敛的难题,提出了MDDCIW_PSO算法。算法的主要思路如下:在粒子群进化过程中,赋予每代群体中每个粒子的每一维度以不同的线性衰减混沌化惯性权重,即从纵向看,随着迭代次数的增加,惯性权重呈现线性衰减变化;从横向看,当代的每个粒子的每一维度都在当前衰减半径内呈现独立的混沌变化。MDDCIW_PSO算法从纵横两个方向,最大可能地增强了粒子在搜索后期的群活性和局部搜索能力,从而尽可能地避免陷入局部最优。大量的标准测试函数仿真结果表明:MDDCIW_PSO算法与已有的典型惯性权重改进策略相比,能够较大幅度地提高粒子群算法的搜索精度。最后将MDDCIW_PSO算法应用于印染定型机的能耗模型优化求解中,取得了满意的结果。

Multi dimensional descending chaotic inertia weight based PSO and its application

Aiming at the difficulty of premature local convergence of standard particle swarm optimization (SPSO) algorithm exposed in tackling multi dimensional and multimodal optimization issues. In this paper, a new MDDCIW_PSO algorithm (multi dimensional descending chaotic inertia weight based PSO) is proposed. The main idea of the algorithm is as follows: in the particle swarm optimization process, different linear decreasing chaotic inertia weights are attached to every dimension of each particle. That is to say, vertically, the value of the inertia weight linearly decreases as the number of iterations increases; horizontally, every dimension of each particle is given an independent chaotic inertia weight within current attenuation radius. Thus, from both vertical and horizontal directions, the proposed MDDCIW_PSO algorithm tries its best to enhance the group activity and local search ability in the late period of search to avoid premature convergence risk. The simulation test results on a lot of typical benchmark functions show that the MDDCIW_PSO algorithm outperforms the other classic inertia weight adaptation strategies in terms of searching precision. Finally, the MDDCIW_PSO algorithm was applied to a dyeing heat setting machine to solve the energy consuming model optimization problem, and satisfactory results were achieved.