A new version of ant system for subset problems

Leguizamón, Guillermo; Michalewicz, Zbigniew

doi:10.1109/cec.1999.782655

Cited by 137 publications

(100 citation statements)

References 5 publications

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“…Also, ACO solutions have been developed for many other combinatorial optimisation problems. Algorithms have been proposed for the quadratic assignment problem [20,21], scheduling problems [22,23], the vehicle routing problem (VRP) [24], the graph colouring problem [25], the shortest common super-sequence problem [26], the multiple knapsack problem [27], and many others.…”

Section: Extensions and Other Applicationsmentioning

confidence: 99%

Ant colony optimization and local search for bin packing and cutting stock problems

Levine

Ducatelle

2004

Journal of the Operational Research Society

164

105

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The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimisation problems. Exact solution methods can only be used for very small instances, so for real-world problems we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimisation (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can outperform some existing solution methods, whereas the hybrid approach can compete with the best known solution methods. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO.

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Section: Extensions and Other Applicationsmentioning

confidence: 99%

Ant colony optimization and local search for bin packing and cutting stock problems

Levine

Ducatelle

2004

Journal of the Operational Research Society

164

105

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“…Also, we consider that all requirements are independent. It is the same type of representation that has been previously used by Leguizamon and Michalewicz (1999);Fidanova (2005);Shi (2006) to tackle the 0/1 knapsack problem. This special case, denoted by Bagnall et al (2001) as basic NRP, allows us to reformulate any other NRP problem by simply preprocessing requirements, it is shown in Section 2.…”

Section: Datasetsmentioning

confidence: 99%

Multi-objective ant colony optimization for requirements selection

Sagrado

Orellana

2013

Empir Software Eng

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The selection of a set of requirements between all the requirements previously defined by customers is an important process, repeated at the beginning of each development step when an incremental or agile software development approach is adopted. The set of selected requirements will be developed during the actual iteration. This selection problem can be reformulated as a search problem, allowing its treatment with metaheuristic optimization techniques. This paper studies how to apply Ant Colony Optimization algorithms to select requirements. First, we describe this problem formally extending an earlier version of the problem, and introduce a method based on Ant Colony System to find a variety of efficient solutions. The performance achieved by the Ant Colony System is compared with that of Greedy Randomized Adaptive Search Procedure and Non-dominated Sorting Genetic Algorithm, by means of computational experiments carried out on two instances of the problem constructed from data provided by the experts.

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“…The first ten MKP problems in file "mknapcb1" of the OR-Library are solved by the IWD-MKP and the results of ten runs of the algorithm are shown in Table 4. For comparison, the results of the two Ant Colony Optimization-based algorithms of Leguizamon and Michalewicz (for short, L & M) (Leguizamon and Michalewicz, 1999) and Fidanova (Fidanova, 2002) are mentioned. Moreover, the results obtained by the LP relaxation method that exist in the file "mkcbres.txt" of the OR-Library are also included.…”

Section: And M Fidanovamentioning

confidence: 99%

Optimization with the Nature-Inspired Intelligent Water Drops Algorithm

Shah‐Hosseini¹

2009

Evolutionary Computation

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A new version of ant system for subset problems

Cited by 137 publications

References 5 publications

Ant colony optimization and local search for bin packing and cutting stock problems

Ant colony optimization and local search for bin packing and cutting stock problems

Multi-objective ant colony optimization for requirements selection

Optimization with the Nature-Inspired Intelligent Water Drops Algorithm

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