PCOBL: A Novel Opposition-Based Learning Strategy to Improve Metaheuristics Exploration and Exploitation for Solving Global Optimization Problems

Abstract

Meta-heuristics are commonly applied to solve various global optimization problems. In order to make the meta-heuristics performing a global search, balancing their exploration and exploration ability is still an open avenue. This manuscript proposes a novel Opposition-based learning scheme, called “PCOBL” (Partial Centroid Opposition-based Learning), inspired by the partial centroid. PCOBL aims to improve meta-heuristics performance through maintaining an effective balance between the exploration and exploitation. PCOBL was incorporated in three different meta-heuristics, and a comparative study was conducted on 28 CEC2013 benchmark problems with 30, 50, and 100 dimensions. In addition, we assessed the PCOBL in the IEEE CEC2011 real-world problems. The empirical results demonstrate that PCOBL balances the exploration and exploitation ability of the meta-heuristics, positively impacting their performance and making them outperform the state-of-the-art algorithms in terms of best-error runs and convergence in most of the optimization problems. Moreover, the computational cost analysis illustrated that the inclusion of PCOBL in the meta-heuristic algorithm has a low impact on its efficiency.

Keywords

• Opposition-based learning
• optimization
• swarm intelligence
• meta-heuristic