A novel artificial bee colony algorithm for the knapsack problem

Knapsack Problem (KP) is a most popular subset selection problem. The aim is to assign an optimal subset among all original items to a knapsack, such that the overall profit of the selected items be maximized, while the total weight of them does not exceed the capacity of the knapsack. Artificial Bee Colony (ABC) algorithm is a new metaheuristic with a stochastic search strategy. In ABC, the neighborhood of the best found food sources is searched in order to achieve better food sources. This paper presents a binary version of ABC algorithm for the KP. In this approach a hybrid probabilistic mutation scheme is performed for searching the neighborhood 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 efficiency greatly.