A Linear Constrained Optimization Benchmark For Probabilistic Search Algorithms: The Rotated Klee-Minty Problem
This work addresses a benchmarking gap for researchers in evolutionary computation and probabilistic search algorithms, though it is incremental as it builds on existing benchmark concepts.
The paper tackles the lack of diverse benchmarks for constrained optimization in randomized search algorithms by proposing a scalable linear constrained optimization problem, specifically the Rotated Klee-Minty Problem, and demonstrates its utility by comparing two recent Evolutionary Algorithm variants.
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.