Approximating the Pareto-front of a planar bi-objective competitive facility location and design problem

Redondo, Juana López; Fernández, José María Pérez; Álvarez, José Domingo; Arrondo, Aránzazu Gila; Ortigosa, Pilar M.

doi:10.1016/j.cor.2014.02.013

Cited by 33 publications

(11 citation statements)

References 19 publications

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“…Another improving method which makes use of the differentiability of the objective functions can be found in [50]. …”

Section: The Improving Methodsmentioning

confidence: 99%

“…In this sense, FEMOEA can be considered a memetic algorithm [45]. Furthermore, this means that FEMOEA can easily be adapted to solve any multi-objective problem, only adapting the improving technique (applications to competitive facility location problems can be found in [24,50]). …”

Section: Femoea: a Fast And Efficient Multi-objective Evolutionary Almentioning

confidence: 99%

“…In fact, many optimization methods are evaluated by making use of location problems [57], mainly due to their high complexity and required computational effort. FEMOEA was indeed initially designed to cope with a competitive facility location and design problem, with an improving method which made use of differentiability (see [2,50]). It proved in [50] to be superior to other state-ofthe-art MOEAs.…”

mentioning

confidence: 99%

“…FEMOEA was indeed initially designed to cope with a competitive facility location and design problem, with an improving method which made use of differentiability (see [2,50]). It proved in [50] to be superior to other state-ofthe-art MOEAs. The aim of this paper is to investigate whether FEMOEA is still a competitive algorithm when applied to general MOPs with two or three objectives.…”

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confidence: 99%

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FEMOEA: a fast and efficient multi-objective evolutionary algorithm

2016

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A multi-objective evolutionary algorithm which can be applied to many nonlinear multi-objective optimization problems is proposed. Its aim is to quickly obtain a fixed size set approximating the complete Pareto-front. It adapts ideas from different multi-objective optimization evolutionary algorithms, but also incorporates new devices. In particular, the search in the space is carried out on promising areas (hyperspheres) determined by a radius value, which decreases as the optimization procedure evolves. This mechanism helps to maintain a balance between exploration and exploitation of the search space. Additionally, a new local search method which accelerates the convergence of the population towards the optimal Pareto-front, has been incorporated. It is an extension of the local optimizer SASS and improves a given solution along a search direction (no gradient information is used). Finally, a termination criteria has also been proposed, which stops the algorithm if during three consecutive iterations the changes experimented in the candidate Pareto-front are negligible (in terms of the objective function values). To know how far two sets are from each other, a modification of the well-known Hausdorff distance is proposed. In order to analyze the algorithm performance, it has been compared to the reference algorithms NSGA-II and SPEA2 and the state-of-the-art Keywords Nonlinear multi-objective optimization · evolutionary algorithm · quality indicators · computational study 1 Introduction

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“…Another improving method which makes use of the differentiability of the objective functions can be found in [50]. …”

Section: The Improving Methodsmentioning

confidence: 99%

Section: Femoea: a Fast And Efficient Multi-objective Evolutionary Almentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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FEMOEA: a fast and efficient multi-objective evolutionary algorithm

2016

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“…An interval branch and bound method were proposed to obtain an outer approximation of the whole set of efficient solutions. Redondo et al (2014) and Arrondo et al (2015) implemented a parallelization of multi-objective evolutionary algorithm, called FEMOEA-Paral, for a similar bi-objective competitive facility location and design problem. Fernandez et al (2007a) proposed another bi-objective optimization problem in which the entrance of the new facility could have a detrimental effect on the market shares of the existing chain-owned facilities, and this cannibalization should be minimized as a secondary objective.…”

Section: Fernandez Et Al (2007b) Developed Two Solution Methods For mentioning

confidence: 99%

Competitive location: a state-of-art review

Ashtiani¹

2016

10.5267/j.ijiec

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This paper provides a review on recent works in the field of competitive facility location models based on the following seven components: 1) Variables, 2) Competition type, 3) Solution space, 4) Customer behavior, 5) Demand type, 6) Number of new facilities and 7) Relocation and redesign possibility. First, the components are introduced and then based on these components; different studies are compared with each other via a proposed taxonomy and finally a review on work of each paper is provided.

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