全局最优引导的差分演化二进制人工蜂群算法

针对基本二进制人工蜂群算法开采能力弱、收敛速度慢的缺点，提出一种全局最优引导的差分二进制人工蜂群算法。算法仿照粒子群优化，将全局最优参数引入二进制人工蜂群算法中以提高开采能力；同时受差分演化算法中“交叉”操作的启发，提出多维邻域搜索方式，加快收敛速度。采用0-1背包问题进行仿真，实验结果表明与传统算法相比，提出算法不仅寻优能力增强且收敛速度明显提高。对于10维背包问题，提出算法的收敛速度比基本二进制人工蜂群算法提高近10倍。

Differential evolution binary artificial bee colony algorithm based on global best

The Basic Binary Artificial Bee Colony（BABC） algorithm has the disadvantages of poor exploitation and slow convergence speed. According to the defects, a differential evolution binary artificial bee colony algorithm based on global best is proposed. Referring to particle swarm optimization, global best parameter is incorporated into BABC algorithm to raise the exploitation capacity. Inspired by crossover operation in differential evolution algorithm, multidimensional neighborhood search strategy is applied to improve convergence speed. The 0-1 knapsack problem is simulated. The simulation results show that compared with the traditional algorithm, the proposed algorithm’s search ability is enhanced and its convergence speed is improved obviously. For 10-dimension knapsack problem, the convergence speed of the proposed algorithm is faster than that of basic BABC algorithm nearly 10 times.