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An Analysis on the Effect of Selection on Exploration in Particle Swarm Optimization and Differential Evolution
- Source :
- CEC
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- The goal of exploration to produce diverse search points throughout the search space can be countered by the goal of selection to focus search around the fittest current solution(s). In the limit, if all exploratory search points are rejected by selection, then the behaviour of the metaheuristic will be equivalent to one which performs no exploration at all (e.g. hill climbing). The effects of selection on exploration are clearly important, but our review of the literature indicates limited coverage. To address this deficit, we introduce new experiments which can specifically highlight the occurrence of “failed exploration” and its effects through selection that can trap a metaheuristic in a less promising part of the search space. We subsequently propose new lines of research to reduce the effects of selection and failed exploration which we believe are distinctly different from traditional lines of research to increase (pre-selection) exploration.
- Subjects :
- business.industry
Survival of the fittest
Particle swarm optimization
Exploratory search
02 engineering and technology
Machine learning
computer.software_genre
020204 information systems
Differential evolution
0202 electrical engineering, electronic engineering, information engineering
Trajectory
020201 artificial intelligence & image processing
Artificial intelligence
business
Hill climbing
Metaheuristic
computer
Selection (genetic algorithm)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 IEEE Congress on Evolutionary Computation (CEC)
- Accession number :
- edsair.doi...........7f051b0b9c5a3a5d25b566530d3bb878
- Full Text :
- https://doi.org/10.1109/cec.2019.8790200