Balancing Exploration-Exploitation Using Switching Mechanism Integrated with Adaptive Heterogeneous Comprehensive Learning PSO and L-SHADE

This chapter proposes a method for improving the balance between exploration and exploitation in optimization algorithms. This method is particularly useful for combining algorithms to achieve a seamless exploration-exploitation equilibrium. The proposed hybrid algorithm combines two well-known optimization methodologies: Linear Population Size Reduction (L-Shade), which is known for its excellent convergence properties, and Adaptive Heterogeneous Comprehensive Particle Swarm Optimization (AHPSO), which is capable of both exploration and exploitation. The hybrid algorithm capitalizes on the strengths of each constituent algorithm via a meticulously designed workflow, ensuring robust performance across a wide range of optimization tasks. The algorithm adapts flexibly to changing optimization landscapes by combining adaptive mechanisms and Dynamic Phase Switching (DPS), demonstrating versatility and efficiency. The performance of the proposed algorithm is verified by ten benchmark functions consisting of 2 unimodal functions, 3 basic functions, 2 hybrid functions and 3 composition functions in the CEC2020 Competition on Single Objective Bound Constrained Optimization. The proposed algorithm is compared with multi-population L-Shade (mlL-SHADE) and L-Shade. The results show the competitiveness of the proposed algorithm.