【作者】 吴斌； 钱存华； 崔志勇；

【Author】 Wu Bin Qian Cun-hua Cui Zhi-yong Department of Industrial Engineering, Nanjing University of Technology, Jiangsu Nanjing, 210009

【机构】 南京工业大学工业工程系；

【摘要】 针对人工蜂群算法中搜索策略单一,容易陷入局部极值点的问题,依据社会心理学的思想,借鉴粒子群算法中新个体产生机制,对觅食蜂和观察蜂的搜索策略进行改进。提出两种改进人工蜂群算法,通过对经典测试函数的实验仿真,结果表明改进算法提高了蜂群搜索能力,优化结果均优于标准人工蜂群算法,适合工程应用中的复杂函数优化问题。更多还原

【Abstract】 Artificial bee colony (ABC) algorithm is the one of the newest nature inspired heuristics for optimization problems. In order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions, the modified algorithms based on social cognition thinking are proposed. The new candidate solutions are generated through mimicking the search mechanism of particle swarm optimization. To compare and analyze the performance of our proposed modified algorithms, a number of experiments are carried out on a set of well-known benchmark continuous optimization problems. Simulation results and comparisons with the standard ABC and several meta-heuristics show that the proposed algorithms can effectively enhance the searching efficiency and greatly improve the searching quality.更多还原