A Linear Constrained Optimization Benchmark For Probabilistic Search Algorithms: The Rotated Klee-Minty Problem

TL;DR

This paper introduces a scalable linear constrained optimization benchmark, the Rotated Klee-Minty problem, designed to evaluate and compare evolutionary algorithms in constrained settings.

Contribution

It proposes a new benchmark problem for constrained optimization, addressing the lack of diverse and scalable test environments for evolutionary algorithms.

Findings

The benchmark effectively differentiates between EA variants.

It demonstrates the utility of the problem for assessing algorithm performance.

The environment is scalable and suitable for benchmarking evolutionary algorithms.

Abstract

The development, assessment, and comparison of randomized search algorithms heavily rely on benchmarking. Regarding the domain of constrained optimization, the number of currently available benchmark environments bears no relation to the number of distinct problem features. The present paper advances a proposal of a scalable linear constrained optimization problem that is suitable for benchmarking Evolutionary Algorithms. By comparing two recent EA variants, the linear benchmarking environment is demonstrated.