Single objective real-parameter optimization: Algorithm jSO

TLDR

Solving single objective real‑parameter optimization problems, also known as bound‑constrained optimization, remains challenging due to their complexity and computational expense, and they arise in engineering, scientific, and other real‑world applications. The paper introduces a new algorithm, jSO, for single objective real‑parameter optimization. jSO is an improved variant of iL‑SHADE, featuring a new weighted mutation strategy, and was evaluated on the CEC 2017 benchmark functions. Experiments show that jSO outperforms L‑SHADE and achieves better or competitive results against iL‑SHADE, attaining the best final score among the three algorithms on the CEC 2017 benchmark.

Abstract

Solving single objective real-parameter optimization problems, also known as a bound-constrained optimization, is still a challenging task. We can find such problems in engineering optimization, scientific applications, and in other real-world problems. Usually, these problems are very complex and computationally expensive. A new algorithm, called jSO, is presented in this paper. The algorithm is an improved variant of the iL-SHADE algorithm, mainly with a new weighted version of mutation strategy. The experiments were performed on CEC 2017 benchmark functions, which are different from previous competition benchmark functions. A comparison of the proposed jSO algorithm and the L-SHADE algorithm is presented first. From the obtained results we can conclude that jSO performs better in comparison with the L-SHADE algorithm. Next, a comparison of jSO and iL-SHADE is also performed, and jSO obtained better or competitive results. Using the CEC 2017 evaluation method, jSO obtained the best final score among these three algorithms.