Human learning optimization (HLO) is a simple yet powerful metaheuristic developed based on a simplified human learning model. Competition and cooperation, as two basic modes of social cognition, can motivate individuals to learn more efficiently and improve their efficiency in solving problems by stimulating their competitive instincts and increasing interaction with each other. Inspired by this fact, this paper presents a novel human learning optimization algorithm with competitive and cooperative learning (HLOCC), in which a competitive and cooperative learning operator (CCLO) is developed to mimic competition and cooperation in social interaction for enhancing learning efficiency. The HLOCC can efficiently maintain the diversity of the algorithm as well as achieve the optimal values, demonstrating that the proposed CCLO can effectively improve algorithm performance. HLOCC has been compared with other heuristic algorithms on CEC2017 functions. In the second study, the uncapacitated facility location problems (UFLPs) which are one of the pure binary optimization problems are solved with HLOCC. The experimental results show that the developed HLOCC is superior to previous HLO variants and other metaheuristics with its improved exploitation and exploration abilities.

人类学习优化（HLO）是一种基于简化人类学习模型开发的简单而强大的元启发式算法。竞争与合作作为社会认知的两种基本模式，通过激发个体的竞争本能，增加彼此之间的互动，可以激励个体更有效地学习，提高解决问题的效率。受此启发，本文提出了一种新颖的具有竞争与合作学习（HLOCC）的人类学习优化算法，其中开发了一种竞争与合作学习算子（CCLO）来模拟社交互动中的竞争与合作，以提高学习效率。HLOCC 可以有效地保持算法的多样性并达到最优值，证明了所提出的CCLO可以有效地提高算法性能。HLOCC 已在 CEC2017 函数上与其他启发式算法进行了比较。在第二项研究中，使用 HLOCC 解决了作为纯二元优化问题之一的无容量设施位置问题 (UFLP)。实验结果表明，开发的 HLOCC 优于以前的 HLO 变体和其他元启发式算法，具有改进的开发和探索能力。