Industrial Process Control Optimization and Automation Engineering Research Center, School of Electronic Engineering, Chaohu University, Chaohu, Anhui 238024, China.
Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China.
Comput Intell Neurosci. 2022 Apr 30;2022:5699472. doi: 10.1155/2022/5699472. eCollection 2022.
Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.
人类学习优化（HLO）是一种高效的元启发式算法，它通过模拟人类的学习行为，开发了三种学习算子，即随机学习算子、个体学习算子和社会学习算子，以搜索最优解。事实上，人们不仅从全局优化中学习，还从现实生活中其他个体的最佳解决方案中学习，并且差分进化的算子是基于其他个体的最优解进行更新的。受这些事实的启发，本文提出了两种新颖的差分人类学习优化算法（DEHLOs），其中引入了差分进化策略来增强算法的优化能力。这两种基于从个体和种群两个方面改进 HLO 的优化算法，分别命名为 DEHLO1 和 DEHLO2。采用多维背包问题作为基准问题来验证 DEHLOs 的性能，并将结果与标准 HLO、改进二进制差分进化（MBDE）以及其他先进的元启发式算法进行比较。实验结果表明，所提出的 DEHLOs 显著优于其他算法，并且 DEHLO2 在各种问题上均取得了最佳的整体性能。
