Paper Info
Title
Combining multipopulation evolutionary algorithms with memory for dynamic optimization problems
Abstract
Both multipopulation and memory are widely used approaches in the field of evolutionary dynamic optimization. It would be interesting to examine the effect of the combinations of multipopulation algorithms (MPAs) and memory schemes. However, since most of the existing memory schemes are proposed with single population algorithms, straightforwardly applying them to MPAs may cause problems. By addressing the possible problems, a new memory scheme is proposed for MPAs in this paper. In the experiments, several existing memory schemes and the newly proposed scheme are combined with a MPA, i.e. the Species-based Particle Swarm Optimizer (SPSO), and these combinations are tested on cyclic and acyclic problems. The experimental results indicate that 1) straightforwardly using the existing memory schemes sometimes degrades the performance of SPSO even on cyclic problems; 2) the newly proposed memory scheme is very competitive.
Year
DOI
Venue
2014
10.1109/CEC.2014.6900492
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
evolutionary computation,multipopulation evolutionary algorithms,particle swarm optimisation,cyclic problem,mpa,evolutionary dynamic optimization,species-based particle swarm optimizer,acyclic problem,memory schemes,spso performance,memory management,statistics,optimization,benchmark testing,sociology,particle swarm optimization
Mathematical optimization,Evolutionary algorithm,Parallel metaheuristic,Computer science,Evolutionary computation,Multi-swarm optimization,Artificial intelligence,Imperialist competitive algorithm,Optimization problem,Machine learning,Metaheuristic
Conference
Citations 
PageRank 
References 
2
0.36
12
Authors
3
Name
Order
Citations
PageRank
Tao Zhu18214.36
Wenjian Luo235640.95
Lihua Yue334046.44