A supervised parallel optimisation framework for metaheuristic algorithms
A supervised parallel optimisation framework for metaheuristic algorithms
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DOI：
10.1016/j.swevo.2023.101445
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发表时间：
2023-12-19
影响因子：
10
通讯作者：
Fletcher，Lloyd
中科院分区：
文献类型：
--
作者：
Muttio，Eugenio J.;Dettmer，Wulf G.;Fletcher，Lloyd
提出了一种有监督的并行优化算法（SPO）。所提出的框架耦合不同的优化算法来解决单目标优化问题。监督平衡了不同优化器的探索和开发能力，提供了一个通用框架来解决具有不同特征的问题。在这项工作中，五个优化算法包括在合奏：粒子群优化（PSO），遗传算法（GA），协方差矩阵自适应进化策略（CMA-ES），差分进化（DE），和改进的布谷鸟搜索（MCS）。一个具有许多局部极小值的几何寻路问题被用来证明SPO的优势。该方法的有效性进行了比较与独立的综合优化策略的发病率，并与国家的最先进的算法。此外，一个基准测试套装组成的工程应用程序被用来验证SPO的一般适用性方面的各种问题。SPO产生的良好的解决方案被证明是一般可重复的，而孤立的算法，充其量，只是偶尔呈现良好的解决方案。
A Supervised Parallel Optimisation (SPO) is presented. The proposed framework couples different optimisation algorithms to solve single-objective optimisation problems. The supervision balances the exploration and exploitation capabilities of the distinct optimisers included, providing a general framework to solve problems with diverse characteristics. In this work, five optimisation algorithms are included in the ensemble: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Covariance Matrix Adaption-Evolution Strategy (CMA-ES), Differential Evolution (DE), and Modified Cuckoo Search (MCS). A geometric path-finding problem with numerous local minima is used to demonstrate the advantage of SPO. The effectiveness of the approach is compared with that of stand-alone incidences of the integrated optimisation strategies and with state-of-the-art algorithms. In addition, a benchmark test suit composed of engineering applications is utilised to validate the general applicability of SPO with respect to a variety of problems. The good solutions generated by SPO are shown to be generally reproducible, while isolated algorithms, at best, render good solutions only occasionally.