Optimization problems can be found in many aspects of our lives. An optimization problem can be approached as searching problem where an algorithm is proposed to search for the value of one or more variables that minimizes or maximizes an optimization function depending on an optimization goal. Multi-objective optimization problems are also abundant in many aspects of our lives with various applications in different fields in applied science. To solve such problems, evolutionary algorithms have been utilized including genetic algorithms that can achieve decent search space exploration. Things became even harder for multi-objective optimization problems when the algorithm attempts to optimize more than one objective function. In this paper, we propose a hybrid genetic algorithm (HGA) that utilizes a genetic algorithm (GA) to perform a global search supported by the particle swarm optimization algorithm (PSO) to perform a local search. The proposed HGA achieved the concept of rehabilitation of rejected individuals. The proposed HGA was supported by a modified selection mechanism based on the K-means clustering algorithm that succeeded to restrict the selection process to promising solutions only and assured a balanced distribution of both the selected to survive and selected for rehabilitation individuals. The proposed algorithm was tested against 4 benchmark multi-objective optimization functions where it succeeded to achieve maximum balance between search space exploration and search space exploitation. The algorithm also succeeded in improving the HGA’s overall performance by limiting the average number of iterations until convergence.

在我们生活的许多方面都可以发现优化问题。可以将优化问题作为搜索问题来解决，其中提出了一种算法来搜索一个或多个变量的值，该变量根据优化目标最小化或最大化一个优化函数。在应用科学中不同领域的各种应用中，多目标优化问题在我们生活的许多方面也很丰富。为了解决这些问题，已经利用包括遗传算法的进化算法，该遗传算法可以实现体面的搜索空间探索。当算法尝试优化多个目标函数时，对于多目标优化问题而言，事情变得更加艰难。在本文中，我们提出了一种混合遗传算法（HGA），该算法利用遗传算法（GA）进行由粒子群优化算法（PSO）支持的全局搜索来执行局部搜索。拟议的HGA实现了被拒绝人员康复的概念。提出的HGA由基于K-means聚类算法的改进选择机制支持，该机制成功地将选择过程限制为仅适用于有前途的解决方案，并确保了要生存的人和康复者的平衡分配。该算法针对4个基准多目标优化函数进行了测试，成功地实现了搜索空间探索与搜索空间利用之间的最大平衡。