Population diversity maintenance is a crucial task for preventing a particle swarm optimization (PSO) algorithm from being trapped in local optima. A learning strategy is an effective means of improving population diversity. However, for the canonical PSO algorithm, the learning strategy focuses mainly on the global best particle, which leads to a loss of diversity. To increase the population diversity and strengthen the global search ability in PSO, this paper proposes a PSO algorithm with an adaptive learning strategy (PSO-ALS). To better promote the performance of the learning strategy, the swarm is adaptively grouped into several subswarms. The particles in each subswarm are further classified into ordinary particles and the locally best particle, and two different learning strategies without an explicit velocity are devised for updating the particles to increase the population diversity. Thus, the global optimum is determined by comparing the fitness values of the updated best particles in each subswarm. The proposed algorithm is compared with state-of-the-art PSO variants. The experimental results illustrate that the performance of PSO-ALS is promising and competitive in terms of enhanced population diversity and global search ability.

维持种群多样性是防止粒子群优化（PSO）算法陷入局部最优的关键任务。学习策略是改善人口多样性的有效手段。但是，对于经典的PSO算法，学习策略主要集中在全局最佳粒子上，这导致了多样性的损失。为了增加种群多样性并增强PSO的全局搜索能力，本文提出了一种具有自适应学习策略的PSO算法（PSO-ALS）。为了更好地提高学习策略的性能，将群体自适应地分为几个子群。每个亚群中的粒子进一步分为普通粒子和局部最佳粒子，设计了两种没有明确速度的不同学习策略来更新粒子以增加种群多样性。因此，通过比较每个子群中更新的最佳粒子的适合度值来确定全局最佳。将该算法与最新的PSO变体进行了比较。实验结果表明，就增强的人口多样性和全球搜索能力而言，PSO-ALS的性能令人鼓舞且具有竞争力。