| dc.description.abstract | The 0/1 Multiple Knapsack Problem (01MKP) is the extension of the classical 0/1 Knapsack Problem (01KP), which is the well-known NP-hard problem in combinatorial optimization. In lieu of considering a single knapsack with a lot of items, 01MKP deals with making decision to put n items, each item contains weight w and profit p, into m knapsacks. The objective function of 01MKP is alike 01KP which optimizes the total profit. A metaheuristic method named Grey Wolf Optimizer (GWO) was employed to find the optimal solution. GWO was built by Mirjalili (2014). The algorithm mimics the social hierarchy of grey wolves (alpha, beta, delta, and gamma) as well as their hunting behavior. There was a modification in this method to deal with the problem called Discrete Grey Wolf Optimization (DGWO). DGWO uses a formula to transform the method from running on continuous space to discrete domain. The problem was, besides, solved by using Genetic Algorithm (GA) for later comparison. Some datasets were used to prove the enhancement of GWO global optimal solution over GA best-fitted solution.
Keywords: 0/1 Multiple Knapsack Problem, Grey Wolf Optimization, Genetic Algorithm. | en_US |
