基于小生境技术的改进引力搜索算法

Niching Behavior Based Advanced Gravitational Search Algorithm

针对引力搜索算法(Gravitational search algorithm,GSA)开发能力强而探索能力弱的特点,提出一种基于小生境技术的引力搜索算法(Niching behavior based advanced GSA,NAGSA)。首先分析了引力搜索算法的性能,为每个粒子定义质量吸引度和欧式距离吸引度两个属性,根据这两个属性计算出粒子吸引概率,取代原有的质量排序选择法。其次,运用吸引概率和小生境拥挤度技术引导粒子在邻域内搜索,平衡算法的收敛速度和多样性。此外,算法将kbest的取值按照指数函数递减,进一步提高收敛精度。10个标准测试函数的仿真结果表明,该算法能有效地提高最优解的精度,加快收敛速度。最后,采用4个标准柔性作业车间调度模型,验证了该算法在解决实际问题中的可行性和优越性。

According to the strong exploitation and poor exploration abilities of gravitational search algorithm(GSA),an niching behavior based advanced GSA(NAGSA)is proposed.After analyzing the performance of GSA,NAGSA defines the mass affinity and Euclidean-distance affinity for each particle,and then each particle affinity probability is calculated according to these two attributes instead of the original sorting mass method.The use of affinity probability and crowding niching behavior guides each particle to search in its neighboring field,thus NAGSA can make a balance between convergence rate and diversity maintaining.Besides,the value of kbest decreases according to exponential function,so that the convergence accuracy is improved.Simulations on ten benchmark functions indicate that NAGSA can improve the accuracy of optimum effectively and accelerate the convergence rate apparently.Furthermore,the algorithm is proved to be feasible and advantageous in the simulation of four standard flexible job shop scheduling modules.