Ghadi Yazeed Yasin, AlShloul Tamara, Nezami Zahid Iqbal, Ali Hamid, Asif Muhammad, Aljuaid Hanan, Ahmad Shahbaz
Department of Computer Science/Software Engineering, Al Ain University, Al Ain, UAE.
Collage of General Education, Liwa College of Technology, Abu Dhabi, UAE.
PeerJ Comput Sci. 2023 Apr 14;9:e1315. doi: 10.7717/peerj-cs.1315. eCollection 2023.
The field of optimization is concerned with determining the optimal solution to a problem. It refers to the mathematical loss or gain of a given objective function. Optimization must reduce the given problem's losses and disadvantages while maximizing its earnings and benefits. We all want optimal or, at the very least, suboptimal answers because we all want to live a better life. Group counseling optimizer (GCO) is an emerging evolutionary algorithm that simulates the human behavior of counseling within a group for solving problems. GCO has been successfully applied to single and multi-objective optimization problems. The 0/1 knapsack problem is also a combinatorial problem in which we can select an item entirely or drop it to fill a knapsack so that the total weight of selected items is less than or equal to the knapsack size and the value of all items is as significant as possible. Dynamic programming solves the 0/1 knapsack problem optimally, but the time complexity of dynamic programming is O(n). In this article, we provide a feature analysis of GCO parameters and use it to solve the 0/1 knapsack problem (KP) using GCO. The results show that the GCO-based approach efficiently solves the 0/1 knapsack problem; therefore, it is a viable alternative to solving the 0/1 knapsack problem.
优化领域关注的是确定问题的最优解。它指的是给定目标函数的数学损失或收益。优化必须减少给定问题的损失和劣势，同时最大化其收益和好处。我们都想要最优解，或者至少是次优解，因为我们都想过上更好的生活。群体咨询优化器（GCO）是一种新兴的进化算法，它模拟群体内部的咨询人类行为来解决问题。GCO已成功应用于单目标和多目标优化问题。0/1背包问题也是一个组合问题，在这个问题中，我们可以完全选择一个物品或者舍弃它来装满一个背包，使得所选物品的总重量小于或等于背包容量，并且所有物品的价值尽可能大。动态规划能最优地解决0/1背包问题，但动态规划的时间复杂度是O(n)。在本文中，我们对GCO参数进行了特征分析，并使用GCO来解决0/1背包问题（KP）。结果表明，基于GCO的方法能有效地解决0/1背包问题；因此，它是解决0/1背包问题的一种可行替代方法。
