Memetic Viability Evolution for Constrained Optimization

TL;DR

This paper introduces a novel memetic viability evolution approach combining Viability Evolution and Covariance Matrix Adaptation to effectively solve constrained optimization problems, outperforming existing methods in quality and efficiency.

Contribution

The paper presents a new algorithm that integrates Viability Evolution with Covariance Matrix Adaptation and adaptive scheduling to handle inequality constraints in optimization.

Findings

Outperforms state-of-the-art methods on benchmark problems

Achieves better solution quality with fewer computational resources

Effectively guides search towards feasible regions in constrained spaces

Abstract

The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adaptation Evolution Strategy is one of the most efficient algorithms for unconstrained optimization problems, it cannot be readily applied to constrained ones. Here, we used concepts from Memetic Computing, i.e. the harmonious combination of multiple units of algorithmic information, and Viability Evolution, an alternative abstraction of artificial evolution, to devise a novel approach for solving optimization problems with inequality constraints. Viability Evolution emphasizes elimination of solutions not satisfying viability criteria, defined as boundaries on objectives and constraints. These boundaries are adapted during the search to drive a population of local search units, based on Covariance Matrix Adaptation Evolution Strategy, towards feasible regions. These units can be recombined by means of Differential Evolution operators. Of crucial importance for the performance of our method, an adaptive scheduler toggles between exploitation and exploration by selecting to advance one of the local search units and/or recombine them. The proposed algorithm can outperform several state-of-the-art methods on a diverse set of benchmark and engineering problems, both for quality of solutions and computational resources needed.