Active components of metaheuristics in cellular genetic algorithms

Villagra, Andrea; Leguizamón, Guillermo; Alba, Enrique

doi:10.1007/s00500-014-1341-6

Cited by 2 publications

(5 citation statements)

References 13 publications

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“…In this section we identify the active components of Scatter Search, by applying the methodology proposed in [11]. Before this, we describe the problems chosen and used in the experiments presented at the end of this paper.…”

Section: Identifying Active Components Of Scatter Searchmentioning

confidence: 99%

“…In this subsection we present an additional comparison of hySSA-rec with several state-of-the-art techniques for the same instances found in [11]. In Table 7 we show the percentage of successes obtained by

hyAS-Sel: a hybrid cGA that uses active components of Simulated Annealing (SA) as the mechanism of selection,

hySSA-rec: the hybrid cGA proposed in this paper,

hyAPM-local: a hybrid cGA with active components of a local Particle Swarm Optimization (PSO) as a mutation operator,

hyAS-APM-local: a hybrid cGA with active components of SA applied to the selection operator and active components of PSO used as a mutation operator,

cGA: the standard cGA.

…”

Section: Additional Analysismentioning

confidence: 99%

“…This classification establishes specific hybrid schemes where, in general, different algorithms are combined according to certain criteria. In contrast to the standard way of hybridization described in the above cited articles, the methodology used in this paper (firstly proposed in [11]) presents an unconventional way of hybridization. The aim of this hybrid approach is to create algorithms at which design follows similar criteria to those established for designing hybrid algorithms, but incorporating components of other algorithms rather than the algorithm as a whole.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this hybrid approach is to create algorithms at which design follows similar criteria to those established for designing hybrid algorithms, but incorporating components of other algorithms rather than the algorithm as a whole. Thus, the goal of this paper is to apply the methodology proposed in [11] in new domains and algorithms. In this work we identify active components (ACs) of Scatter Search [12].…”

Section: Introductionmentioning

confidence: 99%

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A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

Villagra

Alba²,

Leguizam

2016

Computational Intelligence and Neuroscience

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This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology.

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Section: Identifying Active Components Of Scatter Searchmentioning

confidence: 99%

hyAS-Sel: a hybrid cGA that uses active components of Simulated Annealing (SA) as the mechanism of selection,

hySSA-rec: the hybrid cGA proposed in this paper,

hyAPM-local: a hybrid cGA with active components of a local Particle Swarm Optimization (PSO) as a mutation operator,

hyAS-APM-local: a hybrid cGA with active components of SA applied to the selection operator and active components of PSO used as a mutation operator,

cGA: the standard cGA.

…”

Section: Additional Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

Villagra

Alba²,

Leguizam

2016

Computational Intelligence and Neuroscience

Self Cite

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“…In recent years, some scholars have begun to study the combination of CA and intelligent optimization algorithms. In 1993, Whitel first proposed the concept of cellular genetic algorithm(CGA), and then Tomassini et al conducted continuous study of CGA in many aspects [1][2][3]. CGA is a kind of genetic algorithm(GA) in which the population is structured in a specified topology so that individuals may only interact with their neighbors.…”

Section: Introductionmentioning

confidence: 99%

The Application Research of Cellular Genetic Algorithm in Vehicle Routing Problem

Jin¹,

Jiang²,

Yang³

et al. 2018

Proceedings of Information Science and Cloud Computing — PoS(ISCC 2017)

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In order to solve the Distance-constrained Capacitated Vehicle Routing Problem ( Distanceconstrained CVRP ), the cellular genetic algorithm(CGA) is used in this paper. A new crossover operator--SAX which can better reflect the self-adaptability of CGA is proposed, and three types of neighborhood are introduced to analyse the search performance of CGA in Distanceconstrained CVRP . Two instances are introduced to show the feasibility of CGA in solving Distance-constrained CVRP . The experimental results show that the performance of CGA is obviously better than that of the traditional genetic algorithm, and the optimization results of the vehicle routings are improved. Especially, the Moore neighbor structure indicates better search efficiency for SAX crossover operator. Because of the strong searching ability, CGA can effectively solve the optimization of vehicle routing problems. ISCC2017

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Active components of metaheuristics in cellular genetic algorithms

Cited by 2 publications

References 13 publications

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

The Application Research of Cellular Genetic Algorithm in Vehicle Routing Problem

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