Improved Whale Optimization Algorithm with Multi-Strategy Fusion

Aiming at the problems of poor initial population diversity, slow convergence speed and easy to fall into local optimization of traditional WOA algorithm, a whale optimization algorithm improved by multi-strategy fusion is proposed. First, the initial population is improved by using Latin hypercubic sampling method to make the initial solution distribution more uniform; then, the local search ability of the algorithm is improved by introducing adaptive weights, and the global search ability of the algorithm is enhanced by combining the Lévy flight strategy; finally, the Cauchy variation is introduced to enhance the ability of the algorithm to jump out of the local optimum. Through optimization experiments on 13 benchmark functions, the results show that the whale algorithm improved by multi-strategy fusion has obvious improvement in both the search accuracy and convergence speed.