An iterated greedy algorithm for the node placement problem in bidirectional Manhattan street networks

Toyama, Fubito; Shoji, K.; Miyamichi, Juichi

doi:10.1145/1389095.1389207

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…For the moment we concentrate our attention on the variation of the number of colonies. We used the benchmark set proposed by [6] (also used by [9]). The set consists of 80 instances of 4 problem sizes (n = 4 × 4, n = 8 × 8, n = 16 × 16, n = 32 × 32) with 20 matrices for each given size.…”

Section: Methodsmentioning

confidence: 99%

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Multi-colony ant colony optimization for the node placement problem

Melo

2009

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

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In this paper we propose a multi-colony ant colony optimization architecture. In this approach several colonies try to simultaneously solve a problem. Each colony has its own trail and set of control parameters, ensuring that different search strategies are used throughout the optimization. Moreover, the most successful colonies contribute with information that help others with poor behavior to enhance their performance.A first version of the multi-colony approach was applied to the optimal node placement, an important optimization problem from the network communication field. We report some preliminary results obtained with several benchmark instances. They show that the proposed algorithm is promising, as it was able to outperform a single-colony approach. We conclude this paper with a discussion about research directions for the near future, which are related to a more detailed analysis of the existing architecture and to a number of extensions we are planning to implement and analyze.

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Section: Methodsmentioning

confidence: 99%

“…Most of them use a combination of greedy methods, local search, tabu search, genetic algorithm, simulated annealing, multi-start local search and variable depth search. The best performing one at the moment is [9].…”

Section: Node Placement Problem (Npp)mentioning

confidence: 99%

Multi-colony ant colony optimization for the node placement problem

Melo

2009

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

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“…Many works have been done later with IG; Ruiz and Stützle [23] used IG to solve FSP with sequence dependent setup times, and it's been used for node placement in street networks by Toyama et al [24], and for single machine scheduling problems by Tasgetiren et al [25], and as a local search method for unrelated parallel machine scheduling by Fanjul-Peyro and Ruiz [21].…”

Section: Classical Iterated Greedymentioning

confidence: 99%

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

Aqel

Gao

2019

Chin. J. Mech. Eng.

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The flexible job shop scheduling problem (FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them difficult to code and not easy to reproduce. This paper proposes a modified iterated greedy (IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. This is done by separating the classical IG into two phases. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. The simplicity of the proposed IG provides an effective method that is also easy to apply and consumes less CPU time in solving the FJSP problem.

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“…An iterated greedy (IG) algorithm is a heuristic search algorithm making local optimal choices at each iteration (Cormen et al, 2001;Neapolitan and Naimipour, 2010). The standard IG algorithms have been applied to a wide variety of problems (Ruiz and Stützle, 2007;Pan et al, 2008;Ribas et al, 2011;Tuffery et al, 2005;Benedettini et al, 2010;Toyama et al, 2008;Lozano et al, 2011) and only operate with one solution, but the PBIG algorithms extend that behavior using a population of solutions with the aim of improve them in a parallel way (Rodriguez et al, 2012), a technique of more recent use (Bouamama et al, 2012;Rodriguez et al, 2012;Ballestín et al, 2007). In the IG algorithms, the so-called destruction-and-construction operators is typically used to improve the solutions.…”

Section: Algorithm Overview and Pre-processing Stagementioning

confidence: 99%