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Cooperative Coevolution for Large-Scale Optimization Based on Kernel Fuzzy Clustering and Variable Trust Region Methods

机译:基于核模糊聚类和可变信赖域方法的大规模优化协同进化

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摘要

Large-scale optimization arises in a variety of scientific and engineering applications. In this paper, a particle swarm optimization (PSO) approach with dynamic neighborhood that is based on kernel fuzzy clustering and variable trust region methods (called FT-DNPSO) is proposed for large-scale optimization. The cooperative coevolution incorporated with a kernel fuzzy C-means clustering strategy is introduced to divide high-dimensional problems in to subproblems, and explore their search spaces. Furthermore, the independent variable ranges change adaptably by using the variable trust region learning method, which expedites the convergence process and explores in the effective space. In addition, the dynamic neighborhood topology assists the PSO algorithm in cooperating with neighbor particles and avoids the problem of premature convergence. Simulation results substantiate the effectiveness of the proposed algorithm to solve large-scale optimization problems with many well-known benchmark functions.
机译:大规模优化出现在各种科学和工程应用中。本文提出了一种基于核模糊聚类和可变信赖域方法(称为FT-DNPSO)的具有动态邻域的粒子群优化(PSO)方法进行大规模优化。引入了带有核模糊C均值聚类策略的协同协进化,将高维问题划分为子问题,并探索它们的搜索空间。此外,通过使用变量信任区域学习方法,自变量范围可以适应性地变化,这加快了收敛过程并在有效空间中进行了探索。此外,动态邻域拓扑有助于PSO算法与相邻粒子协作,避免了过早收敛的问题。仿真结果证实了该算法解决具有许多著名基准函数的大规模优化问题的有效性。

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