Handling equality constraints in evolutionary optimization

Abu S.S.M. Barkat Ullah, Ruhul Sarker, Chris Lokan

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


Over the last few decades several methods have been proposed for handling functional constraints while solving optimization problems using evolutionary algorithms (EAs). However, the presence of equality constraints makes the feasible space very small compared to the entire search space. As a consequence, the handling of equality constraints has long been a difficult issue for evolutionary optimization methods. This paper presents a Hybrid Evolutionary Algorithm (HEA) for solving optimization problems with both equality and inequality constraints. In HEA, we propose a new local search technique with special emphasis on equality constraints. The basic concept of the new technique is to reach a point on the equality constraint from the current position of an individual solution, and then explore on the constraint landscape. We believe this new concept will influence the future research direction for constrained optimization using population based algorithms. The proposed algorithm is tested on a set of standard benchmark problems. The results show that the proposed technique works very well on those benchmark problems.

Original languageEnglish
Pages (from-to)480-490
Number of pages11
JournalEuropean Journal of Operational Research
Issue number3
Publication statusPublished - 16 Sept 2012
Externally publishedYes


Dive into the research topics of 'Handling equality constraints in evolutionary optimization'. Together they form a unique fingerprint.

Cite this