Solving Manufacturing Cell Design Problems Using an Artificial Fish Swarm Algorithm

Soto, Ricardo; Crawford, Broderick; Vega, Emanuel; Paredes, Fernando

doi:10.1007/978-3-319-27060-9_23

Cited by 10 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we use a Migrating Birds Optimization algorithm by a parallel sorting to solve Machine-Part Cell Formation Problems. Computational experiments using conventional hardware resources have been conducted in 90 test instances, 2 well-known sorting algorithms, 1 sequential configuration, and 5 thread configurations (1,4,8,16, and 32), giving a total of 1080 benchmarks. Among the main results, we highlight a new alignment of the MBO parameters to solve MPCFP.…”

Section: Discussionmentioning

confidence: 99%

“…Several investigations that have been carried out for the problem MPCFP are as follows: linear programming [5], a goal programming model [6], a hybrid genetic algorithm [7], boolean satisfiability [8], constraint programming [9], and using an artificial fish swarm algorithm [4]. An analysis of the evolution of cell formation problem can be found in [10,11].…”

Section: Related Workmentioning

confidence: 99%

“…The MPCFP is known to be NP-Hard, so there is always the challenge of producing high quality solutions in a limited time interval. During the last years, several techniques have successfully been applied to this problem, from mathematical methods such as linear programming [2] to classic metaheuristics such as particle swarm optimization [3] and more modern ones such as artificial fish swarms [4].…”

Section: Introductionmentioning

confidence: 99%

“…It results in a notable improvement in terms of performance of the whole solving process. We perform computational experiments by using 90 well-known MPCFP instances, 2 well-known sorting algorithms, 1 sequential configuration, and 5 thread configurations (1,4,8,16, and 32) resulting in a total of 1080 benchmarks. The obtained results are encouraging where the proposal is able to reach the global optimum in all instances, while the solving time with respect to a nonparallel approach is notably reduced.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems

Soto

Crawford

Almonacid

et al. 2016

Scientific Programming

Self Cite

View full text Add to dashboard Cite

The Machine-Part Cell Formation Problem (MPCFP) is a NP-Hard optimization problem that consists in grouping machines and parts in a set of cells, so that each cell can operate independently and the intercell movements are minimized. This problem has largely been tackled in the literature by using different techniques ranging from classic methods such as linear programming to more modern nature-inspired metaheuristics. In this paper, we present an efficient parallel version of the Migrating Birds Optimization metaheuristic for solving the MPCFP. Migrating Birds Optimization is a population metaheuristic based on the V-Flight formation of the migrating birds, which is proven to be an effective formation in energy saving. This approach is enhanced by the smart incorporation of parallel procedures that notably improve performance of the several sorting processes performed by the metaheuristic. We perform computational experiments on 1080 benchmarks resulting from the combination of 90 well-known MPCFP instances with 12 sorting configurations with and without threads. We illustrate promising results where the proposal is able to reach the global optimum in all instances, while the solving time with respect to a nonparallel approach is notably reduced.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems

Soto

Crawford

Almonacid

et al. 2016

Scientific Programming

Self Cite

View full text Add to dashboard Cite

show abstract

“…An extended version of this work, where the several sorting processes of the metaheuristic are paralleled, has also been published [41]. Finally, another group of modern metaheuristics is proposed to solve this same set of problems efficiently, some examples are artificial fish swarm algorithms [42], shuffled frog leaping algorithms [43], bat algorithms [44], and flower and pollination algorithms [45].…”

Section: Literature Reviewmentioning

confidence: 99%

A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

et al. 2020

Self Cite

View full text Add to dashboard Cite

In this paper, we integrate the autonomous search paradigm on a swarm intelligence algorithm in order to incorporate the auto-adjust capability on parameter values during the run. We propose an independent procedure that begins to work when it detects a stagnation in a local optimum, and it can be applied to any population-based algorithms. For that, we employ the autonomous search technique which allows solvers to automatically re-configure its solving parameters for enhancing the process when poor performances are detected. This feature is dramatically crucial when swarm intelligence methods are developed and tested. Finding the best parameter values that generate the best results is known as an optimization problem itself. For that, we evaluate the behavior of the population size to autonomously be adapted and controlled during the solving time according to the requirements of the problem. The proposal is testing on the dolphin echolocation algorithm which is a recent swarm intelligence algorithm based on the dolphin feature to navigate underwater and identify prey. As an optimization problem to solve, we test a machine-part cell formation problem which is a widely used technique for improving production flexibility, efficiency, and cost reduction in the manufacturing industry decomposing a manufacturing plant in a set of clusters called cells. The goal is to design a cell layout in such a way that the need for moving parts from one cell to another is minimized. Using statistical non-parametric tests, we demonstrate that the proposed approach efficiently solves 160 well-known cell manufacturing instances outperforming the classic optimization algorithm as well as other approaches reported in the literature, while keeping excellent robustness levels.

show abstract

Solving the MCDP Using a League Championship Algorithm

Soto

Crawford

Olivares

et al. 2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Solving Manufacturing Cell Design Problems Using an Artificial Fish Swarm Algorithm

Cited by 10 publications

References 21 publications

Efficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems

Efficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems

A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

Solving the MCDP Using a League Championship Algorithm

Contact Info

Product

Resources

About