Recently,genetic algorithms(GAs) have been applied to multi-modal dynamic optimization(MDO).In this kind of optimization,an algorithm is required not only to find the multiple optimal solutions but also to locate a dy...Recently,genetic algorithms(GAs) have been applied to multi-modal dynamic optimization(MDO).In this kind of optimization,an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum.Our fuzzy genetic sharing(FGS) approach is based on a novel genetic algorithm with dynamic niche sharing(GADNS).FGS finds the optimal solutions,while maintaining the diversity of the population.For this,FGS uses several strategies.First,an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS.Second,a modified tournament selection is used to control selection pressure.Third,a novel mutation with an adaptive mutation rate is used to locate unexplored search areas.The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator(GDBG).展开更多
文摘Recently,genetic algorithms(GAs) have been applied to multi-modal dynamic optimization(MDO).In this kind of optimization,an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum.Our fuzzy genetic sharing(FGS) approach is based on a novel genetic algorithm with dynamic niche sharing(GADNS).FGS finds the optimal solutions,while maintaining the diversity of the population.For this,FGS uses several strategies.First,an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS.Second,a modified tournament selection is used to control selection pressure.Third,a novel mutation with an adaptive mutation rate is used to locate unexplored search areas.The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator(GDBG).
文摘针对樽海鞘群优化算法(SSA:Salp Swarm Algorithm)在求解特征选择问题时存在易陷入局部最优、收敛速度慢的不足,基于樽海鞘群优化算法提出了新的改进算法差分进化樽海鞘群特征选择算法(DESSA:Differential Evolution Salp Swarm Algorithm)。DESSA中采用了差分进化策略替代平均算子作为新的粒子迁移方式以增强搜索能力,并加入进化种群动态机制(EPD:Evolution Population Dynamics),加强收敛能力。实验中以KNN(K-Nearest Neighbor)分类器作为基分类器,以UCI(University of California Irvine)数据库中的8组数据集作为实验数据,将DESSA与SSA同具有代表性的算法进行对比实验,实验结果表明,DESSA算法各考察指标较原算法有明显提升,较其他算法相对优越。
