A series of modified cognitive-only particle swarm optimization (PSO) algorithms effectively mitigate premature convergence by constructing distinct vectors for different particles. However, the underutilization of these constructed vectors hampers convergence accuracy. In this paper, an adaptive balance search based complementary heterogeneous PSO architecture is proposed, which consists of a complementary heterogeneous PSO (CHxPSO) framework and an adaptive balance search (ABS) strategy. The CHxPSO framework mainly includes two update channels and two subswarms. Two channels exhibit nearly heterogeneous properties while sharing a common constructed vector. This ensures that one constructed vector is utilized across both heterogeneous update mechanisms. The two subswarms work within their respective channels during the evolutionary process, preventing interference between the two channels. The ABS strategy precisely controls the proportion of particles involved in the evolution in the two channels, and thereby guarantees the flexible utilization of the constructed vectors, based on the evolutionary process and the interactions with the problem's fitness landscape. Together, our architecture ensures the effective utilization of the constructed vectors by emphasizing exploration in the early evolutionary process while exploitation in the later, enhancing the performance of a series of modified cognitive-only PSOs. Extensive experimental results demonstrate the generalization performance of our architecture.

翻译：一系列改进的仅认知粒子群优化（PSO）算法通过为不同粒子构建不同的向量，有效缓解了早熟收敛问题。然而，这些构建向量的利用不足阻碍了收敛精度。本文提出了一种基于自适应平衡搜索的互补异构PSO架构，该架构包含互补异构PSO（CHxPSO）框架和自适应平衡搜索（ABS）策略。CHxPSO框架主要包括两个更新通道和两个子群。两个通道展现出近乎异构的特性，同时共享一个公共的构建向量。这确保了一个构建向量能在两种异构更新机制中得到利用。两个子群在进化过程中分别在各自的通道内工作，防止了两个通道间的相互干扰。ABS策略根据进化过程以及与问题适应度地形的交互，精确控制参与两个通道进化的粒子比例，从而保证构建向量的灵活利用。总体而言，我们的架构通过在进化早期强调探索、在后期强调利用，确保了对构建向量的有效利用，从而提升了一系列改进的仅认知PSO算法的性能。大量实验结果证明了我们架构的泛化性能。