An Electromagnetism Algorithm of Neural Network Analysis—an Application to Textile Retail Operation

Wu, Peitsang; Yang, Wen-Hung; Wei, Nai-Chieh

doi:10.1080/10170660409509387

Cited by 40 publications

(24 citation statements)

References 11 publications

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“…EM type algorithms are used to solve fuzzy relation equations (Birbil & Feyzioglu, 2003), and to train artificial neural network for textile retail operations (Wu, Yang, & Wei, 2004), and also to obtain fuzzy if-then rules (Wu, Yang, & Hung, 2005). Debels et al (2006) integrated a scatter search with EM for the solution of resource constraint project scheduling problems.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid electromagnetism-like algorithm for single machine scheduling problem

Chang

Chen

Fan

2009

Expert Systems with Applications

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Electromagnetism-like algorithm (EM) is a population-based meta-heuristic which has been proposed to solve continuous problems effectively. In this paper, we present a new meta-heuristic that applies the EM methodology to the single machine scheduling problem. To the best of our knowledge, there are only few researches in solving the combinatorial optimization problem (COP) by EM. This research attempts to employ the random-key concept combining with genetic operators in the hybrid algorithm to obtain the best/optimal schedule for the single machine problems. This new approach attempts to achieve the convergence and diversity effects when it is iteratively applied to solve the problem. This hybrid algorithm is tested on a set of standard test problems available in the literature. The computational results show that this hybrid algorithm performs better than the standard genetic algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid electromagnetism-like algorithm for single machine scheduling problem

Chang

Chen

Fan

2009

Expert Systems with Applications

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“…The results show a great saving on the computation memories and time, and indicate that EM performed much better than genetic algorithm in finding the global optimum. For its merit of simple concept and economic computational cost, EM has been used in the areas of function optimization, fuzzy neural network training, project scheduling, and other combinatorial optimization fields [5] but seldom have used in scheduling problems, so we were motivated to solve our problem with this method. Khalili and TavakkoliMoghadam [19] proposed a new multi-objective electromagnetism algorithm for a bi-objective flowshop scheduling problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The motivation behind this meta-heuristic approach has risen from the attraction-repulsion mechanism of electromagnetic theories and this basic idea that in metaheuristics we desire to bring our search closer to a region with the superior objective function and at same time, go away from the region with the inferior objective function to move the solution gradually toward the optimality. The EM shows very high performance than other meta-heuristics in NP-hard problems [4,5]. In our case, machine availability constraints and transportation times as well as skipping probability of some jobs from some stages are assumed.…”

Section: Introductionmentioning

confidence: 99%

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An electromagnetism-inspired method for a generalized flowshop problem

Khalili

2014

Manufacturing Rev.

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-There are two common simplifying assumptions made in the scheduling of operations. One is that machines are available continuously, and the other is that once a job completes one process it is immediately available for its next. Moreover, in flowshops, it is commonly assumed that jobs must visit all machines. In practice, however, these assumptions may not be justifiable in some applications. This paper deals with a generalized flow shop (GFS) problem with machine availability constraints, transportation times between stages, and machine skipping. The electromagnetism-like method (EM) has been successfully applied to some NP-hard problems and this has motivated us to apply and assess the effectiveness of an EM algorithm in the GFS scenarios. Simulated annealing (SA) and a number of other well-recognized heuristics to the given GFS scheduling problem that minimizes two independent objective functions, namely the total tardiness and the total completion time also has been applied. In order to evaluate the performance of the proposed EM and SA, a set of practical instances has been considered. The related results are analyzed in two parts; in term of the objective functions, and the observed effects on variables in our instances. Extensive experiments and statistical analyses demonstrate that our proposed EM is more efficient than SA and other heuristics with regards to the objective functions considered in this paper.

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“…In recent works, EMO has been used to solve different sorts of engineering problems such as flow-shop scheduling [23], communications [24], vehicle routing [25], array pattern optimization in circuits [26], neural network training [27], image processing [28] and control systems [29]. Although EMO algorithm shares several characteristics to other evolutionary approaches, recent works (see [18][19][20][21]) have exhibited a better EMO's performance in terms of computation time and precision when it is compared with other methods such as GA, PSO and ACO.…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Thresholding algorithm using electromagnetism optimization

et al. 2014

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Segmentation is one of the most important tasks in image processing. It consist in classify the pixels into two or more groups depending on their intensity levels and a threshold value. The quality of the segmentation depends on the method applied to select the threshold. The use of the classical implementations for multilevel thresholding is computationally expensive since they exhaustively search the best values to optimize the objective function. Under such conditions, the use of optimization evolutionary approaches has been extended. The Electro-magnetism-Like algorithm (EMO) is an evolutionary method which mimics the attraction-repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. In this paper, a multilevel thresholding (MT) algorithm based on the EMO is introduced. The approach combines the good search capabilities of EMO algorithm with objective functions proposed by the popular MT methods of Otsu and Kapur. The algorithm takes random samples from a feasible search space inside the image histogram. Such samples build each particle in the EMO context whereas its quality is evaluated considering the objective that is function employed by the Otsu's or Kapur's method. Guided by these objective values the set of candidate solutions are evolved through the EMO operators until an optimal solution is found. The approach generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image in a reduced number of iterations. Experimental results show performance evidence of the implementation of EMO for digital image segmentation.

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An Electromagnetism Algorithm of Neural Network Analysis—an Application to Textile Retail Operation

Cited by 40 publications

References 11 publications

A hybrid electromagnetism-like algorithm for single machine scheduling problem

A hybrid electromagnetism-like algorithm for single machine scheduling problem

An electromagnetism-inspired method for a generalized flowshop problem

A Multilevel Thresholding algorithm using electromagnetism optimization

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