Improvement And Application Of Whale Optimization Algorithm

Whale optimization algorithm(WOA)is a new group heuristic algorithm proposed by simulating the unique foraging behavior of whales.The algorithm has the characteristics of simple operation,few parameters to set and strong searching performance.However,the study of WOA algorithm is still in its infancy,and the algorithm still has the defects of low convergence accuracy and easy to fall into local optimization.In this thesis,based on the standard whale optimization algorithm,several improved algorithms are proposed to solve the large-scale function optimization problem,the fuzzy soft set parameter reduction problem and the complex network controllability optimization problem.The effectiveness of the improved algorithm is verified by simulation.The main research contents are as follows:(1)In order to balance the global and local search capabilities of whale optimization algorithm and improve the convergence speed and accuracy of the algorithm,adaptive adjustment of weights and search strategies-based whale optimization algorithm(AWOA)is proposed.By comparing AWOA with other improved whale optimization algorithms in function optimization simulation,the results show that AWOA algorithm is generally superior to other improved WOA algorithms in terms of convergence accuracy,convergence speed and large-scale optimization problem solving.(2)In order to enhance the ability of whale optimization algorithm to solve large-scale problems,an adaptive chaotic whale optimization algorithm(ACWOA)is proposed by introducing the optimal individual chaotic search strategy.Aiming at the defect that the traditional reduction method of fuzzy soft set is not suitable for large amount of data analysis,a fuzzy soft set parameter reduction model is established.The ACWOA is applied to the optimization of high-dimensional functions and the reduction of fuzzy soft set parameters.(3)In order to enhance the ability of the optimization algorithm to jump out of the local optimum,the optimal individual reverse learning strategy is introduced,and an optimization algorithm based on the elite reverse learning strategy(RWOA)is proposed.(4)The RWOA is applied to complex network controllability optimization.Through the simulation experiment with genetic algorithm,the results show that the network controllability optimization algorithm based on RWOA has a great improvement in both time efficiency and optimization success rate.