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

Soto, Ricardo; Crawford, Broderick; Almonacid, Boris; Paredes, Fernando

doi:10.1155/2016/9402503

Cited by 11 publications

(4 citation statements)

References 53 publications

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“…For future research, a more extensible configuration could be developed to cover a wider set of problems. It would also be interesting to implement this technique in conjunction with other recent metaheuristics where limited work on Autonomous Search exists such as cuckoo search, firefly optimization, or bat algorithms [75]. Finally, hybridization with learning techniques is another interesting research line to pursue, where feedback gathered for the self-tune phase could be processed with machine learning in order to better track the complete solving process.…”

Section: Discussionmentioning

confidence: 99%

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios

Soto

Crawford

Toledo

et al. 2019

Computational Intelligence and Neuroscience

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In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima.

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Section: Discussionmentioning

confidence: 99%

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios

Soto

Crawford

Toledo

et al. 2019

Computational Intelligence and Neuroscience

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“…More recent metaheuristics have also been employed to solve the this problem, for instance, a migrating birds algorithm is used to solve 90 classic MPCF problems efficiently [40]. 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

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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.

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“…For instance, the authors may cite the following works, among many others. In [39], a migrating bird algorithm was applied, which was later parallelized in [40]. The artificial fish swarm algorithm and the shuffled frog leaping algorithm were applied in [41,42].…”

Section: Related Workmentioning

confidence: 99%

Solving the Manufacturing Cell Design Problem through an Autonomous Water Cycle Algorithm

et al. 2019

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Metaheuristics are multi-purpose problem solvers devoted to particularly tackle large instances of complex optimization problems. However, in spite of the relevance of metaheuristics in the optimization world, their proper design and implementation to reach optimal solutions is not a simple task. Metaheuristics require an initial parameter configuration, which is dramatically relevant for the efficient exploration and exploitation of the search space, and therefore to the effective finding of high-quality solutions. In this paper, the authors propose a variation of the water cycle inspired metaheuristic capable of automatically adjusting its parameter by using the autonomous search paradigm. The goal of our proposal is to explore and to exploit promising regions of the search space to rapidly converge to optimal solutions. To validate the proposal, we tested 160 instances of the manufacturing cell design problem, which is a relevant problem for the industry, whose objective is to minimize the number of movements and exchanges of parts between organizational elements called cells. As a result of the experimental analysis, the authors checked that the proposal performs similarly to the default approach, but without being specifically configured for solving the problem.

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

Cited by 11 publications

References 53 publications

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios

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

Solving the Manufacturing Cell Design Problem through an Autonomous Water Cycle Algorithm

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