Whale Optimization Algorithm Based on Domain Search to Solve 0-1 Knapsack Problem

Abstract For the traditional whale optimization algorithm to solve the 0-1 knapsack combinatorial optimization problem, there are shortcomings of insufficient solution energy and easy to fall into local optima. A whale optimization algorithm based on domain search is proposed. This method focuses on the development of global search capabilities in the early stage of the evolution of the whale optimization algorithm, introduces the Lévy flight strategy, and uses a two-stage domain search method to enhance the diversity of the population and avoid the algorithm from falling into local optima; In the later stage, it focuses on the development of local search capabilities, and uses a greedy search method to perform fine search on some high-quality solutions to improve the accuracy of the solutions. Finally, the algorithm is used to conduct two sets of tests on the classic data set: the first group is a small and medium-scale data set, and the second group is a large-scale one. The test results show that the first group can find the optimal solution 100%, the second group has a strong advantage. The results show that the proposed algorithm has good robustness and optimization ability. It’s an effective method to solve the 0-1 knapsack problem.