Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
The quadratic multiple knapsack problem and three heuristic approaches to it
Proceedings of the 8th annual conference on Genetic and evolutionary computation
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem
Applied Soft Computing
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
A new grouping genetic algorithm for the quadratic multiple knapsack problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
A genetic algorithm for the quadratic multiple knapsack problem
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
A swarm intelligence approach to the quadratic minimum spanning tree problem
Information Sciences: an International Journal
A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem
Information Sciences: an International Journal
Generalized quadratic multiple knapsack problem and two solution approaches
Computers and Operations Research
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In this paper we present an artificial bee colony (ABC) algorithm to solve the quadratic multiple knapsack problem (QMKP) which can be considered as an extension of two well known knapsack problems viz. multiple knapsack problem and quadratic knapsack problem. In QMKP, profit values are associated not only with individual objects but also with pairs of objects. Profit value associated with a pair of objects is added to the total profit if both objects of the pair belong to the same knapsack. The objective of this problem is to assign each object to at most one knapsack in such a way that the total weight of the objects in each knapsack should not exceed knapsack's capacity and the total profit of all the objects included into the knapsacks is maximized. We have compared our approach with three genetic algorithms and a stochastic hill climber. Computational results show the effectiveness of our approach.