Now showing items 1-7 of 7
Evolutionary dynamic optimization: test and evaluation environments.
Multi-population methods in unconstrained continuous dynamic environments: the challenges
The multi-population method has been widely used to solve unconstrained continuous dynamic optimization problems with the aim of maintaining multiple populations on different peaks to locate and track multiple changing ...
Adaptive learning particle swarm optimizer-II for global optimization.
This paper presents an updated version of the adaptive learning particle swarm optimizer (ALPSO), we call it ALPSO-II. In order to improve the performance of ALPSO on multi-modal problems, we introduce several new major ...
Maintaining diversity by clustering in dynamic environments.
Maintaining population diversity is a crucial issue for the performance of evolutionary algorithms (EAs) in dynamic environments. In the literature of EAs for dynamic optimization problems (DOPs), many studies have been ...
A directed mutation operator for real coded genetic algorithms.
Developing directed mutation methods has been an interesting research topic to improve the performance of genetic algorithms (GAs) for function optimization. This paper introduces a directed mutation (DM) operator for GAs ...
Benchmark generator for the IEEE WCCI-2012 competition on evolutionary computation for dynamic optimization problems. Technical Report 2011.
(Department of Information Systems and Computing, Brunel University., 2011)
A comparative study on particle swarm optimization in dynamic environments.