A novel particle swarm optimizer hybridized with extremal optimization
A novel particle swarm optimizer hybridized with extremal optimization
复制标题
一种与极值优化相结合的新型粒子群优化器
DOI：
10.1016/j.asoc.2009.08.014
复制
发表时间：
2010-03-01
影响因子：
8.7
通讯作者：
Lu, Yong-Zai
中科院分区：
文献类型：
--
作者：
Chen, Min-Rong;Li, Xia;Lu, Yong-Zai
粒子群优化（PSO）由于其实现简单且计算开销低廉而受到优化界越来越多的关注。然而，PSO 存在早熟收敛的问题，尤其是在复杂的多模态函数中。极值优化（EO）是最近开发的局部搜索启发式方法，已成功应用于各种硬优化问题。为了克服PSO的局限性，本文通过将EO引入到PSO中，提出了一种新的混合算法，称为混合PSO-EO算法。这种混合方法巧妙地将 PSO 的探索能力与 EO 的开发能力结合起来。我们在一系列单峰/多峰基准函数上验证了所提出的方法的性能，并提供与其他元启发式方法的比较。与其他算法相比，所提出的方法被证明具有优越的性能和防止过早收敛的强大能力。 (C) 2009 Elsevier B.V. 保留所有权利。
Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of hard optimization problems. To overcome the limitation of PSO, this paper proposes a novel hybrid algorithm, called hybrid PSO-EO algorithm, through introducing EO to PSO. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of EO. We testify the performance of the proposed approach on a suite of unimodal/multimodal benchmark functions and provide comparisons with other meta-heuristics. The proposed approach is shown to have superior performance and great capability of preventing premature convergence across it comparing favorably with the other algorithms. (C) 2009 Elsevier B.V. All rights reserved.