loading page

Self-Adaptive Spherical Search with Constrained Multi-Operator Differential Evolution (SASS-CMODE) for nonlinear programming problems
  • +1
  • Jongmin Lee ,
  • Renier Mendoza ,
  • Victoria May P. Mendoza ,
  • Eunok Jung
Jongmin Lee
Author Profile
Renier Mendoza
Author Profile
Victoria May P. Mendoza
Author Profile
Eunok Jung
Department of Mathematics

Corresponding Author:[email protected]

Author Profile

Abstract

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
Metaheuristic algorithms for constrained optimiza- tion problems have become popular because of their ease of use and capability to obtain global solutions. However, these population-based algorithms can be computationally expensive and may suffer from low accuracy due to the difficulty in obtaining feasible points. We present a novel algorithm, re- ferred to as SASS-CMODE, by integrating a modified Improved Multi-Operator Differential Evolution (IMODE) algorithm with the Self-Adaptive Spherical Search (SASS) method. IMODE is modified to make it suitable for solving constrained problems, leading to a new algorithm termed Constrained Multi-Operator Differential Evolution (CMODE). SASS-CMODE is capable of achieving solutions with high feasibility rate and high accuracy by utilizing SASS to identify good feasible points and CMODE to achieve accurate solutions with fewer function evaluations. To evaluate its performance, we test SASS-CMODE to 57 engi- neering problems. The results demonstrate its superiority over other state-of-the-art optimization algorithms. SASS-CMODE is also employed to solve a constrained optimization problem on identifying optimal levels of non-pharmaceutical interventions to control an epidemic, showcasing its versatility and applicability in real-world scenarios.