A complex-valued encoding wind driven optimization for the 0-1 knapsack problem

摘要

This paper presents a complex-valued encoding wind driven optimization (CWDO) with a greedy strategy for the 0-1 knapsack problem. We introduce a complex-value encoding method, which can be viewed as an effective global optimization strategy, and a greedy strategy, which can be viewed as an enhanced local search strategy into wind driven optimization. These strategies increase the diversity of the population and avoid premature convergence. This paper presents three types of test cases for evaluation: standard, small-scale, and large-scale test cases. The experimental results show that the proposed algorithm is suitable for these three cases. Compared to the complex valued cuckoo search algorithm, greedy genetic algorithm, wind driven optimization, binary cuckoo search algorithm, bat algorithm and particle swarm optimization, the performance, stability, and robustness of the CWDO algorithm is better. The simulation results show that the CWDO algorithm is an effective and feasible method for solving the 0-1 knapsack problem.