关键词: 进化算法, 量子进化算法, 自适应, 函数优化, 背包问题

Abstract: In order to overcome the limit that the traditional quantum-inspired evolutionary algorithms (QEA) only use the current best individuals to guide the evolution, which leads to the insufficient evolutionary capability and may often end up by providing sub-optimal solutions, this paper proposes a self-adaptive QEA combining the strategy of keeping away from the worst (KSQEA), which evolves individuals not only close to the best but also far away from the worst. In this way, KSQEA is able to acquire the evolutionary driving force even if the advantage of the best individuals is not obvious. In addition, this paper proposes a wavy rotation angle decaying method to balance the exploration and the exploitation of search. The experimental results both on the function optimization and the knapsack problem show that these measures successfully increase the searching capability of the algorithm and the quality of solutions.

Key words: evolutionary algorithm, quantum-inspired evolutionary algorithm, self-adaptation, function optimization, knapsack problem