Authors: Shima Sabet; Mohammad Shokouhifar; Fardad Farokhi
Addresses: Department of Electrical Engineering, Islamic Azad University of Central Tehran Branch, Tehran P.C.1469669191, Iran ' Electrical and Computer Engineering Department, ShahidBeheshti University, Tehran P.C.6461157419, Iran ' Department of Electrical Engineering, Islamic Azad University of Central Tehran Branch, Tehran P.C.1469669191, Iran
Abstract: Multiple Knapsack problem (MKP) is a most popular multiple subset selection problem that belongs to the class of NP-Complete problems. The aim is to assign optimal subsets among all original items to some knapsacks, such that the overall profit of all selected items be maximised, while the total weight of all assigned items to any knapsack does not exceed the allowable capacity of it. Artificial bee colony (ABC) algorithm is a new meta-heuristic with a stochastic search strategy. In ABC, the neighbourhood area of any best-found solution is searched by the employed bees to achieve better solutions. This paper presents a discrete ABC algorithm for the MKP. In this approach, a hybrid probabilistic mutation scheme is performed for searching the neighbourhood of food sources. The proposed algorithm can guide the search space quickly and improve the local search ability. Experimental results demonstrate that the presented approach has improved the quality and convergence speed than other evolutionary algorithms.
Keywords: swarm intelligence; hybrid probabilistic mutation; artificial bee colony; ABC algorithm; neighbourhood search; MKP; multiple knapsack problem.
International Journal of Reasoning-based Intelligent Systems, 2013 Vol.5 No.2, pp.88 - 95
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 20 Oct 2013 *