2 research outputs found
Gradient Based Hybridization of PSO
Particle Swarm Optimization (PSO) has emerged as a powerful metaheuristic
global optimization approach over the past three decades. Its appeal lies in
its ability to tackle complex multidimensional problems that defy conventional
algorithms. However, PSO faces challenges, such as premature stagnation in
single-objective scenarios and the need to strike a balance between exploration
and exploitation. Hybridizing PSO by integrating its cooperative nature with
established optimization techniques from diverse paradigms offers a promising
solution. In this paper, we investigate various strategies for synergizing
gradient-based optimizers with PSO. We introduce different hybridization
principles and explore several approaches, including sequential decoupled
hybridization, coupled hybridization, and adaptive hybridization. These
strategies aim to enhance the efficiency and effectiveness of PSO, ultimately
improving its ability to navigate intricate optimization landscapes. By
combining the strengths of gradient-based methods with the inherent social
dynamics of PSO, we seek to address the critical objectives of intelligent
exploration and exploitation in complex optimization tasks. Our study delves
into the comparative merits of these hybridization techniques and offers
insights into their application across different problem domains