A Note on Bound Constraints Handling for the IEEE CEC’05 Benchmark Function Suite
- 1 June 2014
- journal article
- Published by MIT Press in Evolutionary Computation
- Vol. 22 (2), 351-359
- https://doi.org/10.1162/evco_a_00120
Abstract
The benchmark functions and some of the algorithms proposed for the special session on real parameter optimization of the 2005 IEEE Congress on Evolutionary Computation (CEC’05) have played and still play an important role in the assessment of the state of the art in continuous optimization. In this article, we show that if bound constraints are not enforced for the final reported solutions, state-of-the-art algorithms produce infeasible best candidate solutions for the majority of functions of the IEEE CEC’05 benchmark function suite. This occurs even though the optima of the CEC’05 functions are within the specified bounds. This phenomenon has important implications on algorithm comparisons, and therefore on algorithm designs. This article's goal is to draw the attention of the community to the fact that some authors might have drawn wrong conclusions from experiments using the CEC’05 problems.Keywords
This publication has 4 references indexed in Scilit:
- Memetic Algorithms for Continuous Optimisation Based on Local Search ChainsEvolutionary Computation, 2010
- Particle Swarm CMA Evolution Strategy for the optimization of multi-funnel landscapesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of CombustionIEEE Transactions on Evolutionary Computation, 2008
- A Restart CMA Evolution Strategy With Increasing Population SizePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005