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Ant Colony Optimization and Multiple Knapsack Problem

S. Fidanova

Source Title: Handbook of Research on Nature-Inspired Computing for Economics and Management

ISBN13: 9781591409847|ISBN10: 1591409845|EISBN13: 9781591409854

DOI: 10.4018/978-1-59140-984-7.ch033

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MLA

Fidanova, S. "Ant Colony Optimization and Multiple Knapsack Problem." Handbook of Research on Nature-Inspired Computing for Economics and Management, edited by Jean-Philippe Rennard, IGI Global, 2007, pp. 498-509. https://doi.org/10.4018/978-1-59140-984-7.ch033

APA

Fidanova, S. (2007). Ant Colony Optimization and Multiple Knapsack Problem. In J. Rennard (Ed.), Handbook of Research on Nature-Inspired Computing for Economics and Management (pp. 498-509). IGI Global. https://doi.org/10.4018/978-1-59140-984-7.ch033

Chicago

Fidanova, S. "Ant Colony Optimization and Multiple Knapsack Problem." In Handbook of Research on Nature-Inspired Computing for Economics and Management, edited by Jean-Philippe Rennard, 498-509. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59140-984-7.ch033

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Abstract

The ant colony optimization algorithms and their applications on the multiple knapsack problem (MKP) are introduced. The MKP is a hard combinatorial optimization problem with wide application. Problems from different industrial fields can be interpreted as a knapsack problem including financial and other management. The MKP is represented by a graph, and solutions are represented by paths through the graph. Two pheromone models are compared: pheromone on nodes and pheromone on arcs of the graph. The MKP is a constraint problem which provides possibilities to use varied heuristic information. The purpose of the chapter is to compare a variety of heuristic and pheromone models and different variants of ACO algorithms on MKP.

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Ant Colony Optimization and Multiple Knapsack Problem

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