An Ant Colony Heuristic for the Design of Two-Edge Connected Flow Networks

Rappos, Efstratios; Hadjiconstantinou, Eleni

doi:10.1007/978-3-540-28646-2_24

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Although initially applied to solve the TSP, ACO algorithms have become very popular and have been applied to solve a broad set of combinatorial optimization problems, mainly due to their versatility and easiness of adaptation. Rappos and Hadjiconstantinou (2004) use ACO to solve two-edge connected network flow design problems making use of flow ants to construct the network and of reliability ants to deal with the reliability of the network. While solving degree-constrained Minimum Spanning Trees, Bui and Zrncic (2006) define maximum and minimum allowed pheromone values, based on the differences between the cost of the most expensive and of the least expensive arcs.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

“…Stützle and Hoos, by proving the convergence of the pheromone trail, in certain conditions, developed lower and upper bounds for the pheromone trail. This approach has been found helpful in other works, such as in Rappos and Hadjiconstantinou (2004), Venables and Moscardini (2006), and Altiparmak and Karaoglan (2007). We now describe the method that is used in this work.…”

Section: Updating Pheromonesmentioning

confidence: 99%

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Concave minimum cost network flow problems solved with a colony of ants

Monteiro

Fontes

2012

J Heuristics

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In this work we address the Single-Source Uncapacitated Minimum Cost Network Flow Problem with concave cost functions. This problem is NP-hard, therefore we propose a hybrid heuristic to solve it. Our goal is not only to apply an Ant Colony Optimization (ACO) algorithm to such a problem, but also to provide an insight on the behaviour of the parameters in the performance of the algorithm. The performance of the ACO algorithm is improved with the hybridization of a local search procedure. The core ACO procedure is used to mainly deal with the exploration of the search space, while the Local Search is incorporated to further cope with the exploitation of the best solutions found. The method we have developed has proven to be very efficient while solving both small and large size problem instances. The problems we have used to test the algorithm were previously solved by other authors using other population based heuristics. Our algorithm was able to improve upon some of their results in terms of solution quality, proving that the HACO algorithm is a very good alternative approach to solve these problems. In addition, our algorithm is substantially faster at achieving these improved solutions. Furthermore, the magnitude of the reduction of the computational requirements grows with problem size.

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Section: Ant Colony Optimizationmentioning

confidence: 99%

Section: Updating Pheromonesmentioning

confidence: 99%

Concave minimum cost network flow problems solved with a colony of ants

Monteiro

Fontes

2012

J Heuristics

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“…Ant colony optimization (ACO), a population-based metaheuristic, can be used to roughly resolve difficult optimization problems. The ACO metaheuristic was described as a collection of general recommendations that could be easily applied to nearly any type of combinatorial optimization problem, which increased the number of researchers and publications in the field.Since then, many problems have been solved utilizing ACO techniques, including network flow concerns (Monteiro et al, 2012), network design issues and more (Rappos, 2004), assignment issues (Shyu et al, 2006), location issues (Musa et al, 2010, Santos et al, 2010 that address transportation problems, and covering problems (Chen and Ting, 2008), citing a few publications in the field of combinatorial optimization (Mehrabi et al, 2009). Surprisingly, the TSP still inspires researchers, as shown by Tavares and Pereira (2011), who utilize the TSP to test an evolutionary method to update pheromone trails, or Garc 'a-Martnez et al (2007), who recently used ACO to solve a bi-criteria TSP.…”

Section: Introductionmentioning

confidence: 99%

To Use an Ant Colony Technique to Solve a Crispy Type Bi- and Tri-Objective Transportation Problem

Ekanayake

2023

JCCE

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Transportation Problem (TP), which aims to reduce the entire transportation cost of moving resources from various supply hubs to various demand hubs. However, in real life situations, all organizations want to achieve numerous objectives while making transportation of goods. The degree of deterioration may vary depending on the mode, route, and time of transport. In some cases, the multi-objective could be to reduce the use of a scarce resource, such as energy. As a result, the proposed approach was discovered to be an algorithm that improves bi-& tri-objective TP techniques. This is an innovative way for solving the new bi-& tri-objective Transportation Algorithm using a modified Ant Colony Optimization (ACO) algorithm. According to the literature, various strategies have been developed in the past to tackle the multi-objective transportation problem (MOTP). The MOTP is solved using goal programming, fuzzy programming, interactive solution algorithms, and other techniques. These strategies occasionally good or bad of achieving better results in a reasonable amount of time. The heuristic technique used in this work is the improved ACO algorithm, which is based on the Ant Colony Algorithm (ACA) and has been found to provide solutions with a reasonable degree of satisfaction for two and three objective TPs. When the findings are compared, the solution achieved using the proposed method has delivered the best performance and provides a case study to show the new strategy.

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An ant colony optimization algorithm to solve the minimum cost network flow problem with concave cost functions

Monteiro

Fontes

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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An Ant Colony Heuristic for the Design of Two-Edge Connected Flow Networks

Cited by 4 publications

References 7 publications

Concave minimum cost network flow problems solved with a colony of ants

Concave minimum cost network flow problems solved with a colony of ants

To Use an Ant Colony Technique to Solve a Crispy Type Bi- and Tri-Objective Transportation Problem

An ant colony optimization algorithm to solve the minimum cost network flow problem with concave cost functions

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