群体智能优化中的虚拟碰撞：雨林算法

启发式优化算法中寻优代理过早收敛易陷入局部最优. 本文对此进行机理分析并发现, 虚拟碰撞作为一种隐性过早收敛现象将直接影响群体智能优化算法的准确性与快速性, 而采样过程的无约束性和样本分布信息的缺失是导致虚拟碰撞的根本原因. 为解决上述问题, 本文提出雨林优化算法. 该算法仿照植物生长模式, 利用规模可变种群代替规模限定种群进行分区分级寻优采样, 并结合均匀与非均匀采样原则来权衡优化算法的探索与挖掘, 可以有效减少虚拟碰撞的发生, 在提高寻优效率的同时, 获取精准性和稳定性较高的全局最优解. 与遗传算法、粒子群算法对标称函数的寻优对比实验表明, 雨林算法在快速性、准确性以及泛化能力等方面均具有优势. 关键词： 优化算法 群体智能 进化计算 计算智能

Pseudo-collision in swarm optimization algorithm and solution:rain forest algorithm

Pseudo-collision (Pc) as a common but neglected phenomenon in swarm optimization algorithm is revealed in this paper. Mechanism analysis on the inevitability of Pc indicates that both the lack of relation among samples and the unconstrained behavior of sampling are the inherent character of agent operation causing Pc in state-of-the-art swarm algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO). Based on the result of mechanism analysis, a novel partition management and classification sampling strategy is proposed to reduce Pc. In addition, both uniform and non-uniform principles are designed to facilitate the trade-off between exploration and exploitation during optimization. Rain forest algorithm (RFA), of which the evolution mechanism is identical with the above strategy and the principles, is proposed in this paper. By examining the rapidity, accuraty, and generalization capability across six benchmark nonconvex functions, RFA is found to be competitive with or even superior to GA and PSO in dealing with complex multi-peak optimization.
Keywords： ptimization algorithm swarm intelligence evolutionary computation computational intelligence