Fusing ant colony optimization with Lagrangian relaxation for the multiple-choice multidimensional knapsack problem

Ren, Zhigang; Feng, Zuren; Zhang, Aimin

doi:10.1016/j.ins.2011.07.033

Cited by 36 publications

(10 citation statements)

References 20 publications

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“…Importantly the combined matheuristic performs better than either heuristic on its own. Ren et al (2012) previously investigated a hybrid of LR and ACO. They made use of the LR solution information as heuristic information in ACO.…”

Section: Resultsmentioning

confidence: 99%

“…These include integrations with other meta-heuristics (Blum 2005;Blum et al 2008;López-Ibáñez et al 2009) and exact methods such as integer programming (Anghinolfi et al 2011) and constraint programming (Khichane et al 2008;Meyer and Ernst 2004;Thiruvady et al 2009Thiruvady et al , 2012). An LR-ACO hybrid was examined on the multi-choice multi-dimensional problem (Ren et al 2012). This study used the LR solution information as a guide in the ACO procedure through the heuristic information.…”

Section: Introductionmentioning

confidence: 99%

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A Lagrangian-ACO matheuristic for car sequencing

Thiruvady

Ernst

Wallace

2014

EURO Journal on Computational Optimization

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In this study, we investigate a hybrid Lagrangian relaxation ant colony optimisation for optimisation version of the car sequencing problem. Several cars are required to be scheduled on an assembly line and each car requires a number of options such as sunroof and/or air conditioning. These cars are required to be sequenced such that sub-sequences of specific sizes may only include a limited number of any option. While this is usually a hard constraint, in this study we treat it as a soft constraint and further require the utilisation of options be modulated across the sequence leading to the optimisation problem. We investigate various Lagrangian heuristics, ant colony optimisation (ACO) and hybrids of these methods. The results show that the Lagrangian-ACO hybrid is the best performing method for up to 300 cars.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Lagrangian-ACO matheuristic for car sequencing

Thiruvady

Ernst

Wallace

2014

EURO Journal on Computational Optimization

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“…Hence, pheromone is designed to be associated with the number of coding nodes a solution owns. This idea is similar to the pheromone scheme in TSP and 0-1 knapsack problems [14], [43], where pheromone is associated with the total distance and the total number of bins, respectively.…”

Section: B the Pheromone Maintenance Mechanismmentioning

confidence: 99%

“…In ACOs, the heuristic information is of vital importance for guiding the construction of the global best solutions, e.g., the distance between two cities in TSP and the weight and value of goods in the 0-1 knapsack problem [14], [43]. In those problems, such information can be easily extracted and defined to better explore the neighboring areas.…”

Section: The Heuristic Maintenance Mechanismmentioning

confidence: 99%

A Modified Ant Colony Optimization Algorithm for Network Coding Resource Minimization

Wang

Xing

et al. 2016

IEEE Trans. Evol. Computat.

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Abstract-The paper presents a modified ant colony optimization approach for the network coding resource minimization problem. It is featured with several attractive mechanisms specially devised for solving the network coding resource minimization problem: 1) a multi-dimensional pheromone maintenance mechanism is put forward to address the issue of pheromone overlapping; 2) problem-specific heuristic information is employed to enhance the heuristic search (neighboring area search) capability; 3) a tabu-table based path construction method is devised to facilitate the construction of feasible (link-disjoint) paths from the source to each receiver; 4) a local pheromone updating rule is developed to guide ants to construct appropriate promising paths; 5) a solution reconstruction method is presented, with the aim of avoiding prematurity and improving the global search efficiency of proposed algorithm. Due to the way it works, the ant colony optimization can well exploit the global and local information of routing related problems during the solution construction phase. The simulation results on benchmark instances demonstrate that with the five extended mechanisms integrated, our algorithm outperforms a number of existing algorithms with respect to the best solutions obtained and the computational time.

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“…Truong et al [25] described chemical reaction optimization using greedy strategy for the binary knapsack problem. Ren et al [32] presented ACO algorithm with Lagrangian relaxation for the multiple-choice multidimensional knapsack problem. Different types of binary knapsack problems have been solved by different optimization techniques.…”

Section: Introductionmentioning

confidence: 99%