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.

翻译：粒子群优化（PSO）在过去三十年中已成为一种强大的元启发式全局优化方法。其吸引力在于能够解决传统算法难以处理的复杂多维问题。然而，PSO面临挑战，例如单目标场景中的过早停滞以及需要在探索与利用之间取得平衡。通过将PSO的协作特性与来自不同范式的成熟优化技术相结合来混合PSO，提供了一种有前景的解决方案。本文研究了将基于梯度的优化器与PSO协同结合的各种策略。我们引入了不同的混合原理，并探索了多种方法，包括序列解耦混合、耦合混合和自适应混合。这些策略旨在提高PSO的效率和有效性，最终增强其应对复杂优化景观的能力。通过结合梯度方法的优势与PSO固有的社会动力学特性，我们力求在复杂优化任务中实现智能探索与利用的关键目标。本研究深入探讨了这些混合技术的比较优势，并提供了关于它们在不同问题领域中应用的见解。