An Efficient Particle Swarm Optimization with Fusion of Figurate Opposition-based learning and Grey Wolf Optimizer

Efficient metaheuristics are required to solve real-world optimization problems and find an optimal solution while maintaining the Exploration-Exploitation balance. This paper presents a novel hybrid algorithm called Opp-PSOGWO, which combines the exploitation capabilities of particle swarm optimization (PSO) and the exploration capabilities of grey wolf optimization (GWO). Figurate opposition-based Learning (OBL) is added to the hybrid to improve performance. The dimensions of particles that fall in the Fibonacci sequence positions are generated in opposite directions to achieve an optimal initial population with increased population diversity. In this way, the hybrid algorithm can avoid the local optima trap, and Figurate opposition avoids unnecessary exploration and gives the optimal solution quickly. To evaluate the performance and the solution quality, Opp-PSOGWO is tested on 9 classical benchmark functions (unimodal and multimodal). The experimental results demonstrate that, in terms of performance metrics, the proposed algorithm outperforms the state-of-art metaheuristic algorithm.