Improved Whale Optimization Algorithm for Solving High-Dimensional Optimiza-tion Problems

Abstract

Aiming at the problems of insufficient global exploration ability and easy to fall into local extremes when dealing with high-dimensional optimization problems, an improved whale optimization algorithm is proposed. Firstly, an initialization strategy combining Fuch chaos mapping and optimized oppsition-based learning is used in the search space to generate good quality chaotic initial populations with good diversity by using higher search effi-ciency of Fuch mapping, and then combined with the optimized oppsition-based learning strategy to generate good whale populations while ensuring population diversity, laying foundation for the global search of the algorithm. Secondly, the parameter A is adjusted in the global exploration phase to help the whale populations to perform global search more effectively and avoid premature convergence while balancing global exploration and local exploitation.Finally, the Laplace operator is introduced in the local exploitation stage to perform dynamic crossover operation for optimal individual. Children generation is produced farther away from the parent generation in the early iteration to improve the global search ability to get rid of local extreme value binding, and points are produced closer to the parent generation in the late iteration to refine the search range to improve the solution accuracy. Ten standard test functions are selected for simulation in 100, 500 and 1000 dimensions. The results show that this algorithm is significantly better than other comparative algorithms in terms of convergence speed, solution accuracy and sta-bility, and can effectively deal with high-dimensional optimization problems.

Keywords

• |whale optimization algorithm (woa)|laplace crossing|chaotic oppsition-based learning strategy