Single objective real-parameter optimization: Algorithm jSO

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.