The balance between exploration (Er) and exploitation (Ei) determines the generalization performance of the particle swarm optimization (PSO) algorithm on different problems. Although the insufficient balance caused by global best being located near a local minimum has been widely researched, few scholars have systematically paid attention to two behaviors about personal best position (P) and global best position (G) existing in PSO. 1) P's uncontrollable-exploitation and involuntary-exploration guidance behavior. 2) G's full-time and global guidance behavior, each of which negatively affects the balance of Er and Ei. With regards to this, we firstly discuss the two behaviors, unveiling the mechanisms by which they affect the balance, and further pinpoint three key points for better balancing Er and Ei: eliminating the coupling between P and G, empowering P with controllable-exploitation and voluntary-exploration guidance behavior, controlling G's full-time and global guidance behavior. Then, we present a dual-channel PSO algorithm based on adaptive balance search (DCPSO-ABS). This algorithm entails a dual-channel framework to mitigate the interaction of P and G, aiding in regulating the behaviors of P and G, and meanwhile an adaptive balance search strategy for empowering P with voluntary-exploration and controllable-exploitation guidance behavior as well as adaptively controlling G's full-time and global guidance behavior. Finally, three kinds of experiments on 57 benchmark functions are designed to demonstrate that our proposed algorithm has stronger generalization performance than selected state-of-the-art algorithms.

翻译：探索（Er）与利用（Ei）之间的平衡决定了粒子群优化（PSO）算法在不同问题上的泛化性能。尽管全局最优解位于局部最优附近所导致的平衡不足已被广泛研究，但很少有学者系统地关注PSO中存在的关于个体最优位置（P）和全局最优位置（G）的两种行为：1）P的不可控利用与被动探索引导行为；2）G的全时段全局引导行为。这两种行为均对Er与Ei的平衡产生负面影响。对此，我们首先讨论了这两种行为，揭示了它们影响平衡的机制，并进一步指出了实现更好平衡的三个关键点：消除P与G之间的耦合、赋予P可控利用与主动探索的引导能力、控制G的全时段全局引导行为。随后，我们提出了一种基于自适应平衡搜索的双通道PSO算法（DCPSO-ABS）。该算法采用双通道框架以减弱P与G的相互作用，有助于调控P和G的行为；同时引入一种自适应平衡搜索策略，赋予P主动探索与可控利用的引导能力，并自适应地控制G的全时段全局引导行为。最后，我们在57个基准函数上设计了三类实验，证明所提算法相比所选前沿算法具有更强的泛化性能。