BMR and BWR: Two simple metaphor-free optimization algorithms for solving real-life non-convex constrained and unconstrained problems

TL;DR

This paper introduces two simple, metaphor-free optimization algorithms, BMR and BWR, which are effective for solving a wide range of real-life non-convex constrained and unconstrained problems, outperforming many existing methods.

Contribution

The paper presents novel, parameter-free algorithms BMR and BWR, demonstrating their superior performance on diverse real-world and benchmark optimization problems.

Findings

BMR and BWR outperform existing algorithms on 26 real-life problems.

They show superior results on 12 constrained engineering problems.

The algorithms perform well on 30 unconstrained benchmark problems.

Abstract

Two simple yet powerful optimization algorithms, named the Best-Mean-Random (BMR) and Best-Worst-Random (BWR) algorithms, are developed and presented in this paper to handle both constrained and unconstrained optimization problems. These algorithms are free of metaphors and algorithm-specific parameters. The BMR algorithm is based on the best, mean, and random solutions of the population generated for solving a given problem, and the BWR algorithm is based on the best, worst, and random solutions. The performances of the proposed two algorithms are investigated by implementing them on 26 real-life nonconvex constrained optimization problems given in the Congress on Evolutionary Computation (CEC) 2020 competition, and comparisons are made with those of the other prominent optimization algorithms. The performances on 12 constrained engineering problems are also investigated, and the results are compared with those of very recent algorithms (in some cases, compared with more than 30 algorithms). Furthermore, computational experiments are conducted on 30 unconstrained standard benchmark optimization problems, including 5 recently developed benchmark problems with distinct characteristics. The results demonstrated the superior competitiveness and superiority of the proposed simple algorithms. The optimization research community may gain an advantage by adapting these algorithms to solve various constrained and unconstrained real-life optimization problems across various scientific and engineering disciplines. The codes of the BMR and BWR algorithms are available at https://sites.google.com/view/bmr-bwr-optimization-algorithm/home?authuser=0.