An island model genetic algorithm for unequal area facility layout problems

Palomo-Romero, Juan M.; Salas-Morera, Lorenzo; García-Hernández, Laura

doi:10.1016/j.eswa.2016.10.004

Cited by 83 publications

(46 citation statements)

References 77 publications

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“…The IMGA model is possible to explore different regions of the search space, so as to improve issues of premature convergence or convergence to local optimal solutions in the standard GA using a single large population. In the latest literature, an IMGA for UA-FLP was proposed by J. M. Palomo-Romero et al [21]. This is the first time to propose a parallel GA based on the island model to solve UA-FLP using the flexible bay structure (FBS) formulation.…”

Section: Imgamentioning

confidence: 99%

“…The sequential IMGA consists of the following processes [21]. (1) n individuals as the initial population is randomly generated and distributed into N islands randomly.…”

Section: Parallel Imga On Gpumentioning

confidence: 99%

“…In the experiments, for comparison with the performance of the different versions, we adopt performance ratio between the multiple methods for measurement. Our proposed algorithms are tested using 26 well-known problem sets taken from the literature [21] as shown in Table 2. For testing larger scale facilities, we have added 8 problem data sets.…”

Section: Parallel Imga On Gpumentioning

confidence: 99%

“…However, approaches based on standard GAs usually have premature convergence, which means a population for an optimization problem converged too early to get optimal solution. Moreover, all the individuals in a population should be selected, evaluated, crossed, and mutated in each generation, which requires a long CPU execution time [21].…”

Section: Introductionmentioning

confidence: 99%

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On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

et al. 2018

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Facility layout problem (FLP) is one of the hottest research areas in industrial engineering. A good facility layout can achieve efficient production management, improve production efficiency, and create high economic values. Because FLP is an NP-hard problem, meaning it is impossible to find the optimal solution when problem becomes sufficiently large, various evolutionary algorithms (EAs) have been proposed to find a sub-optimal solution within a reasonable time interval. Recently, a genetic algorithm (GA) was proposed for unequal area FLP (UA-FLP), where the areas of facilities are not identical. More precisely, the GA is an island model based, which is called IMGA. Since EAs are still very time consuming, many efforts have been devoted to how to parallelize various EAs including IMGA. In recent work, Steffen and Dietmar proposed how to parallelize island models of EAs. However, their parallelization approaches are preliminary because they focused mainly on comparing the performances between different parallel architectures. In addition, they used one mathematical function to model the problem. To further investigate on how to parallelize the IMGA by GPU, in this paper we propose multiple parallel algorithms, for each individual step in the IMGA when solving the industrial engineering problem, UA-FLP, and conduct experiments to compare their performances. After integrating better algorithms for all steps into the IMGA, our GPU implementation outperforms the CPU counterpart and the best speedup can be as high as 84.

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Section: Imgamentioning

confidence: 99%

“…The sequential IMGA consists of the following processes [21]. (1) n individuals as the initial population is randomly generated and distributed into N islands randomly.…”

Section: Parallel Imga On Gpumentioning

confidence: 99%

Section: Parallel Imga On Gpumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

et al. 2018

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“…During the last years, the parallel island model has been successfully used, showing its robustness and applicability in different problems and areas of knowledge such as: Unequal Area Facility 22 , Job Shop Scheduling 23 , Cluster Geometry Optimization 15 , Parallel Harmony Search 24 , Multi-Objective problems 25 and problems of the molecular biology domain 26 .…”

Section: Related Workmentioning

confidence: 99%

A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

Arellano‐Verdejo¹,

Godoy-Calderón²,

Alonso-Pecina³

et al. 2017

IJCIS

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In this paper a coarse-grain execution model for evolutionary algorithms is proposed and used for solving numerical and combinatorial optimization problems. This model does not use migration as the solution dispersion mechanism, in its place a more efficient population-merging mechanism is used that dynamically reduces the population size as well as the total number of parallel evolving populations. Even more relevant is the fact that the proposed model incorporates an entropy measure to determine how to merge the populations such that no valuable information is lost during the evolutionary process. Extensive experimentation, using genetic algorithms over a well-known set of classical problems, shows the proposed model to be faster and more accurate than the traditional one.

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Genetic Algorithm for the Optimization of the Unequal-Area Facility Layout Problem

Zapata-Cortés

Serna

Cáceres-Gelvez

2022

Handbook on Decision Making

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An island model genetic algorithm for unequal area facility layout problems

Cited by 83 publications

References 77 publications

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

Genetic Algorithm for the Optimization of the Unequal-Area Facility Layout Problem

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