title: A cluster-based differential evolution algorithm with external archive for optimization in dynamic environments.
abstract: This paper presents a Cluster-based Dynamic Differential Evolution with external Archive (CDDE_Ar) for global optimization in dynamic fitness landscape. The algorithm uses a multipopulation method where the entire population is partitioned into several clusters according to the spatial locations of the trial solutions. The clusters are evolved separately using a standard differential evolution algorithm. The number of clusters is an adaptive parameter, and its value is updated after a certain number of iterations. Accordingly, the total population is redistributed into a new number of clusters. In this way, a certain sharing of information occurs periodically during the optimization process. The performance of CDDE_Ar is compared with six state-of-the-art dynamic optimizers over the moving peaks benchmark problems and dynamic optimization problem (DOP) benchmarks generated with the generalized-dynamic-benchmark-generator system for the competition and special session on dynamic optimization held under the 2009 IEEE Congress on Evolutionary Computation. Experimental results indicate that CDDE_Ar can enjoy a statistically superior performance on a wide range of DOPs in comparison to some of the best known dynamic evolutionary optimizers.
authors.affiliation: Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700 032, India. udithalder99@gmail.com
authors.forename: Udit
authors.initials: U
authors.lastname: Halder
authors.forename: Swagatam
authors.initials: S
authors.lastname: Das
authors.forename: Dipnakar
authors.initials: D
authors.lastname: Maity
mesh_terms: D000818:Animals; D005075:Biological Evolution; D016000:Cluster Analysis; D003198:Computer Simulation; D017753:Ecosystem; D005828:Genetics, Population; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation / Q000235:genetics*