Comparing bio-inspired algorithms in constrained optimization problems
- 1 September 2007
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 662-669
- https://doi.org/10.1109/cec.2007.4424534
Abstract
This paper presents a comparison of four bio- inspired algorithms (all seen as search engines) with a similar constraint-handling mechanism (Deb's feasibility rules) to solve constrained optimization problems. The aim is to analyze the performance of traditional versions of each algorithm based on both, final results and on-line behavior. A set of 24 well- known benchmark problems are used in the experiments. Quality and consistency of results per each algorithm are investigated. Furthermore, two performance measures (number of evaluations to reach a feasible solution and progress ratio inside the feasible region) are utilized to compare the on-line behavior of each approach. Based on the obtained results, some conclusions are established.Keywords
This publication has 16 references indexed in Scilit:
- A Population-Based, Parent Centric Procedure for Constrained Real-Parameter OptimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter OptimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Constrained Single-Objective Optimization Using Differential EvolutionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- PESO+for Constrained OptimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Identifying On-Line Behavior and Some Sources of Difficulty in Two Competitive Approaches for Constrained OptimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- A constraint-handling mechanism for particle swarm optimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- A constraint handling approach for the differential evolution algorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the artComputer Methods in Applied Mechanics and Engineering, 2002
- Stochastic ranking for constrained evolutionary optimizationIEEE Transactions on Evolutionary Computation, 2000
- Evolutionary Algorithms for Constrained Parameter Optimization ProblemsEvolutionary Computation, 1996