极值个体引导的人工蜂群算法

Extreme Individual Guided Artificial Bee Colony Algorithm

针对目前人工蜂群算法(ABC)在求解函数优化问题时存在开发能力差、易陷入局部最优、收敛速度慢等问题,提出了一种极值个体引导的人工蜂群算法(EABC)。首先,该算法在雇佣蜂和跟随蜂的搜索中利用全局极值个体和邻域极值个体引导搜索,全局极值个体引导搜索有利于种群中优良个体的保留和发展,使算法跳出局部极值,避免早熟收敛。邻域极值个体引导搜索有利于增强搜索精度,提高算法的收敛速度,并通过随机数r平衡两种搜索机制。其次,在搜索过程中引入小概率变异算子,对蜜蜂个体的各维度以较小的概率进行变异,克服算法陷入局部极值并出现早熟收敛的现象。最后,采用基于目标函数值的贪婪选择策略,提高算法的优化性能;采用28个测试函数进行仿真实验,并与其他几种算法进行比较,实验结果表明改进算法具有较高的优化性能和较快的收敛速度。

To overcome the drawbacks of poor development ability,easy to fall into local optimum,slow convergence speed of artificial bee colony(ABC)algorithm in solving function optimization problems,an extreme individual guided artificial bee colony(EABC)algorithm is proposed.Firstly,global extremum and neighborhood extremum individuals are used to guide search for employed bees and following bees.The global extremum individual guided search is good for the retention and development of excellent individuals in the population,so that the algorithm jumps out of local extremum and avoids premature convergence.The neighborhood extremum individual guided search is good for enhancing the search accuracy and improving the convergence speed of the algorithm,and the random number r is used to balance two search mechanisms.Secondly,the small probability mutation operator is introduced into search process,and each dimension of bee individual is mutated with a small probability to overcome local extremum and premature convergence of the algorithm.Finally,the greedy selection strategy based on the value of objective function is adopted to improve the optimization performance of the algorithm.Simulation experiments are carried out with 28 test functions and the algorithm proposed in this paper is compared with other algorithms.Experimental results show that the improved algorithm has higher optimization performance and faster convergence speed.