A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems

• Main Authors: Xiangbo Qi, Zhonghu Yuan, Yan Song
• Format: Article
• Language: English
• Published: Hindawi Limited 2020-01-01
• Series: Computational Intelligence and Neuroscience
• Online Access: http://dx.doi.org/10.1155/2020/5787642

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.