Although particle swarm optimization algorithm (PSO) has displayed promising performance on many optimization problems, how to balance contradictions between the exploration and the exploitation and rationally allocate computational resource are two crucial problems need to be dealt with in PSO study. In this paper, a PSO variant based on multiple adaptive strategies (MAPSO) is proposed. To efficiently maintain the population diversity, the entire population is split into multiple swarms, which can be regrouped during the evolutionary process. In each generation, different particles in a swarm adaptively select their learning exemplars (ALE) according to the performance of the particles. Thus, different particles in the same swarm can perform distinct search behaviors in each generation, as well as the same particle can conduct various search behaviors in different generations. In addition, aiming to rationally utilize computational resource, an adaptive strategy for population size (APS) is introduced. In APS, the population can adaptively delete unfavorable particles and add promising particles during the evolutionary process. Extensive experiments based on CEC2013 and CEC2017 test suites verify the superior performance of the multiple adaptive strategies on balancing the exploration and exploitation abilities. Furthermore, the performance of the newly introduced strategies is also testified by a set of experiments.

尽管粒子群优化算法（PSO）在许多优化问题上表现出了令人鼓舞的性能，但是如何平衡勘探与开发之间的矛盾以及合理分配计算资源却是PSO研究中需要解决的两个关键问题。本文提出了一种基于多重自适应策略（MAPSO）的PSO变体。为了有效地保持种群多样性，整个种群被分为多个种群，可以在进化过程中重新组合。在每一代中，群体中的不同粒子会根据粒子的性能自适应地选择其学习样本（ALE）。因此，同一群中的不同粒子可以在每一代中执行不同的搜索行为，相同的粒子可以在不同的世代中进行各种搜索行为。另外，为了合理利用计算资源，提出了一种人口规模自适应策略（APS）。在APS中，种群可以在进化过程中适应性地删除不利的粒子并添加有希望的粒子。基于CEC2013和CEC2017测试套件的大量实验证明了多种自适应策略在平衡勘探和开发能力方面的优越性能。此外，还通过一组实验证明了新引入策略的性能。种群可以在进化过程中自适应地删除不利的粒子并添加有希望的粒子。基于CEC2013和CEC2017测试套件的大量实验证明了多种自适应策略在平衡勘探和开发能力方面的优越性能。此外，还通过一组实验证明了新引入策略的性能。种群可以在进化过程中自适应地删除不利的粒子并添加有希望的粒子。基于CEC2013和CEC2017测试套件的大量实验证明了多种自适应策略在平衡勘探和开发能力方面的优越性能。此外，还通过一组实验证明了新引入策略的性能。