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

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