A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems
Top Cited Papers
- 22 February 2005
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Evolutionary Computation
- Vol. 9 (1), 1-17
- https://doi.org/10.1109/tevc.2004.836819
Abstract
This work presents a simple multimembered evolution strategy to solve global nonlinear optimization problems. The approach does not require the use of a penalty function. Instead, it uses a simple diversity mechanism based on allowing infeasible solutions to remain in the population. This technique helps the algorithm to find the global optimum despite reaching reasonably fast the feasible region of the search space. A simple feasibility-based comparison mechanism is used to guide the process toward the feasible region of the search space. Also, the initial stepsize of the evolution strategy is reduced in order to perform a finer search and a combined (discrete/intermediate) panmictic recombination technique improves its exploitation capabilities. The approach was tested with a well-known benchmark. The results obtained are very competitive when comparing the proposed approach against other state-of-the art techniques and its computational cost (measured by the number of fitness function evaluations) is lower than the cost required by the other techniques compared.Keywords
This publication has 21 references indexed in Scilit:
- A Simple Evolution Strategy to Solve Constrained Optimization ProblemsLecture Notes in Computer Science, 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
- Handling Constraints in Genetic Algorithms Using Dominance-based TournamentsPublished by Springer Science and Business Media LLC ,2002
- Theory of Evolution Strategies — A TutorialPublished by Springer Science and Business Media LLC ,2001
- Stochastic ranking for constrained evolutionary optimizationIEEE Transactions on Evolutionary Computation, 2000
- An efficient constraint handling method for genetic algorithmsComputer Methods in Applied Mechanics and Engineering, 2000
- Finding low energy conformations of atomic clusters using evolution strategiesLecture Notes in Computer Science, 1998
- Optimization of Road Networks Using Evolutionary StrategiesEvolutionary Computation, 1997
- Evolutionary strategies of optimizationPhysical Review E, 1997
- Evolutionary Algorithms for Constrained Parameter Optimization ProblemsEvolutionary Computation, 1996