Research On Improved Whale Optimization Algorithm

Whale Optimization Algorithm(WOA)is a metaheuristic algorithm that achieves the optimization of the target problem by simulating the predatory behavior of humpback whales,which is characterized by fewer parameters,simple structure and easy programming implementation,and has been widely used in various fields.Although the humpback whale optimization algorithm has obvious advantages over other metaheuristic algorithms,it still has the problems of insufficient global search ability and easy to fall into local extremes.To address the above problems,a dynamic Laplace crossover and Parameter adjustment Whale Optimization Algorithm(LPWOA)is proposed.Firstly,in the population initialization stage,the initialization strategy combining Fuch chaos mapping and optimized contrastive learning is used to generate a high-quality chaotic initial population with good diversity by using the higher search efficiency of Fuch mapping,and then combined with the optimized contrastive learning strategy to ensure the population diversity while generating a good whale population to lay the foundation for the global search of the algorithm;secondly,in the global search stage,the parameter A is adjusted to improve the position update formula to help the whale population search the solution space more effectively,balance the global search and local development while avoiding premature convergence;finally,in the local development stage,the Laplace operator is introduced to perform dynamic crossover operations on the optimal individuals,which generates children farther away from the parents in the early iteration to improve the global search ability and get rid of local extreme value constraints,and generates children closer to the parents in the late iteration to refine the search range and improve the local extreme value constraints.In the later iteration,the children closer to the parents are generated to refine the search range and improve the solution accuracy.In order to verify the effectiveness of each improvement strategy of the LPWOA,the WOA with each improvement strategy is set up for comparison experiments with the WOA,and the results demonstrate the feasibility of each improvement strategy to improve the solving ability of the WOA.In order to verify the performance of the LPWOA,10 standard test functions are selected for comparison experiments in low and high dimensions,and the results show that the LPWOA outperforms the WOA in terms of optimization accuracy,convergence speed and stability.There are 33 figures,7 tables and 76 references in this paper.