A heuristic whale optimization algorithm with niching strategy for global multi-dimensional engineering optimization

• Published in: Computers & industrial engineering 2022-09, Vol.171, p.108361, Article 108361
• Main Authors: Lin, Xiankun, Yu, Xianxing, Li, Weidong
• Format: Article
• Language: English
• Subjects: Global engineering optimization; Niching strategy; Whale optimization algorithm
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2022.108361

•A niching strategy hybrid heuristic whale optimization algorithm (NHWOA).•The NHWOA for global multi-dimensional engineering optimization.•Algorithm design with good exploration capability as well as fast convergence.•Better performance of the NHWOA compared with other state-of-the-art algorithms.•Validation for the NHWOA by benchmark functions and practical engineering problems. Whale optimization algorithm (WOA) has received increasing attention in engineering optimization owing to its high computation efficiency, whereas, it has exhibited the drawback of premature convergence in solving multi-dimensional engineering global optimization problems. In this research, a niching hybrid heuristic whale optimization algorithm (NHWOA) is proposed to enhance convergence speed and search coverage in solving global optimization problems. In the algorithm, the niching technique is introduced to promote the diversity of population and restrain premature convergence in search of a global best solution. A heuristic adjustment to the parameters of the hybrid WOA algorithm is made to promote the exploration potential of search agents in the evolution. A designed perturbation to all the search agents’ positions is executed to avoid their falling into a local optimum. Optimization to the CEC2014 benchmark functions as validation cases are conducted along with comparisons to both conventional intelligent optimization algorithms and other state-of-the-art modified WOAs. Results indicate the effectiveness and superiority of NHWOA in solving the problems. Five practical engineering problems for global optimization with multiply variables are introduced to validate the performance of the presented algorithm with good performance results in the global computations.