A Hybrid Ant Colony Optimization Algorithm for Solving the Ring Arc-Loading Problem

Bernardino, Anabela Moreira; Bernardino, Eugénia Moreira; Sánchez‐Pérez, Juan M.; Gómez–Pulido, Juan A.; Vega‐Rodríguez, Miguel A.

doi:10.1007/978-3-642-12842-4_9

Cited by 7 publications

(23 citation statements)

References 11 publications

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“…Bernardino et al (2010d) proposed a Hybrid PBIL (HPBIL) algorithm to optimise TAP. The authors compare the performance of HPBIL with LSGA, TS, MHDE, HACO (Bernardino et al, 2009b) and HSS.…”

Section: Previous Workmentioning

confidence: 99%

“…It is based on the indirect communication of a colony of simple agents, called (artificial) ants, mediated by (artificial) pheromone trails (Dorigo, 1991;Dorigo et al, 1991Dorigo et al, , 1996. Bernardino et al (2009b) proposed a Hybrid ACO (HACO) algorithm to optimise TAP and compared the results with GA, TS and HDE. Bernardino et al (2011c) proposed two new HACO algorithms to optimise TAP.…”

Section: Previous Workmentioning

confidence: 99%

“…Diverse bio-inspired techniques have also been applied to optimise Synchronous Optical Network (SONET) ring loading problems (Bernardino et al, 2011a,b) and SONET design problems (Bernardino et al, 2012). In (Bernardino et al, 2009b;Bernardino et al, 2010e;Bernardino et al, 2011c) the authors considered the use of SI techniques to optimise TAP. Hybrid ACO (HACO) algorithms (Bernardino et al, 2009b;Bernardino et al, 2011c) and BA (Bernardino et al, 2010e) were applied with success to TAP.…”

Section: Introductionmentioning

confidence: 99%

“…In (Bernardino et al, 2009b;Bernardino et al, 2010e;Bernardino et al, 2011c) the authors considered the use of SI techniques to optimise TAP. Hybrid ACO (HACO) algorithms (Bernardino et al, 2009b;Bernardino et al, 2011c) and BA (Bernardino et al, 2010e) were applied with success to TAP. Currently, we consider a New improved HACO (NHACO) algorithm based in (Bernardino et al, 2011c) (see Section 4.1).…”

Section: Introductionmentioning

confidence: 99%

“…In NHACO we incorporate a new mechanism to modify the solutions. In DPSO and ABC we incorporate the LS method proposed by Bernardino et al (2009b).…”

Section: Introductionmentioning

confidence: 99%

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

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confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In NHACO we incorporate a new mechanism to modify the solutions. In DPSO and ABC we incorporate the LS method proposed by Bernardino et al (2009b).…”

Section: Introductionmentioning

confidence: 99%

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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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A Hybrid Ant Colony Optimization Algorithm for Solving the Ring Arc-Loading Problem

Cited by 7 publications

References 11 publications

Swarm optimisation algorithms applied to large balanced communication networks

Swarm optimisation algorithms applied to large balanced communication networks

An Elitist-Ant System for Solving the Post-Enrolment Course Timetabling Problem

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

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