Hybridization of metaheuristic algorithms with local search has been investigated in many studies. This paper proposes a hybrid pathfinder algorithm (HPFA), which incorporates the mutation operator in differential evolution (DE) into the pathfinder algorithm (PFA). The proposed algorithm combines the searching ability of both PFA and DE. With a test on a set of twenty-four unconstrained benchmark functions including both unimodal continuous functions, multimodal continuous functions, and composition functions, HPFA is proved to have significant improvement over the pathfinder algorithm and the other comparison algorithms. Then HPFA is used for data clustering, constrained problems, and engineering design problems. The experimental results show that the proposed HPFA got better results than the other comparison algorithms and is a competitive approach for solving partitioning clustering, constrained problems, and engineering design problems.

中文翻译：

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用于无约束和约束优化问题的混合探路优化器。


许多研究都对元启发式算法与局部搜索的混合进行了研究。本文提出一种混合探路算法（HPFA），将差分进化（DE）中的变异算子融入到探路算法（PFA）中。该算法结合了 PFA 和 DE 的搜索能力。通过对24个无约束基准函数（包括单峰连续函数、多峰连续函数和复合函数）的测试，证明HPFA相对于pathfinder算法和其他比较算法有显着的改进。然后HPFA用于数据聚类、约束问题和工程设计问题。实验结果表明，所提出的HPFA比其他对比算法获得了更好的结果，是解决分区聚类、约束问题和工程设计问题的有竞争力的方法。