A Constraint-Handling Technique for Genetic Algorithms using a Violation Factor

TL;DR

This paper introduces a new constraint-handling method for genetic algorithms called VCH, which uses a violation factor to improve performance on constrained optimization problems without the need for parameter tuning.

Contribution

The paper proposes the VCH technique that simplifies constraint handling in GAs by eliminating the need for penalty parameter tuning, demonstrating consistent performance on benchmark problems.

Findings

VCH outperforms traditional penalty methods in several benchmarks

VCH provides stable and consistent results across different constrained problems

The method matches or exceeds the performance of existing GA-based constraint techniques

Abstract

Over the years, several meta-heuristic algorithms were proposed and are now emerging as common methods for constrained optimization problems. Among them, genetic algorithms (GA's) shine as popular evolutionary algorithms (EA's) in engineering optimization. Most engineering design problems are difficult to resolve with conventional optimization algorithms because they are highly nonlinear and contain constraints. In order to handle these constraints, the most common technique is to apply penalty functions. The major drawback is that they require tuning of parameters, which can be very challenging. In this paper, we present a constraint-handling technique for GA's solely using the violation factor, called VCH (Violation Constraint-Handling) method. Several benchmark problems from the literature are examined. The VCH technique was able to provide a consistent performance and match results from other GA-based techniques.