A genetic algorithm for the unbounded knapsack problem
ABSTRACT In this paper a new evolutionary algorithm is presented for the unbounded knapsack problem, which is a famous NP-complete combinatorial optimization problem. The proposed genetic algorithm is based on two techniques. One is a heuristic operator, which utilizes problem-specific knowledge, and the other is a preprocessing technique. Computational results show that the proposed algorithm is capable of obtaining high-quality solutions for problems of standard randomly generated knapsack instances, while requiring only a modest amount of computational effort.
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Conference Paper: A novel artificial bee colony algorithm for the knapsack problem[Show abstract] [Hide abstract]
ABSTRACT: 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.Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium on; 01/2012
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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.Int. J. of Reasoning-based Intelligent Systems. 01/2013; 5(2):88 - 95.
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ABSTRACT: Knapsack problem is regarded as a difficult NP problem in computer algorithms. According to the characteristics of knapsack problems, an algorithm (called KP-KEA) for solving knapsack problems utilizing knowledge evolution principle is proposed. In this algorithm, an initial knowledge base is formed at first. The next work is to inherit excellent knowledge individuals by inheritance operator, produce novel knowledge individuals by innovation operator, update knowledge-base by update operator, and accordingly realize knowledge evolution. At last, the optimal solution of knapsack problems can be gained from the optimal knowledge individual. The successful experimental results show that it is an effective algorithm for solving knapsack problems. Compared with genetic algorithm, the convergence speed and the optimal solution of this algorithm are all better. This algorithm is also suited to solve other constraint optimization problems.01/2011;