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 methodolog...
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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 oil 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. (c) 2007 Elsevier Ltd. All rights reserved.
In the past two decades many manufacturing companies applied Cellular Manufacturing Systems (CMS) to improve their production. Cellular manufacturing (CM) is recognized as an application of Group Technology (GT). This...
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ISBN:
(纸本)9781424441358
In the past two decades many manufacturing companies applied Cellular Manufacturing Systems (CMS) to improve their production. Cellular manufacturing (CM) is recognized as an application of Group Technology (GT). This paper deals with CM scheduling problem which is considered as a major challenge in implementation of the CMS. Through previous studies many researchers have attempted to develop effective models and algorithms to solve this problem. Most of these researches consider algorithms which schedule part families and the part of each family separately in two steps. In this paper an integrated approach is considered which schedules all parts from all part families in one step and it is demonstrated how this reduces the idle time of machines and consequently decreases the makespan. To solve this scheduling problem an electromagnetism-like (EM-like) algorithm is employed. In addition, as EM-likealgorithm was originally designed for problems with real value data, modification of the algorithm was necessary to use it with integer data. The results of the proposed algorithm show a major improvement when compared with the results of one of the so far best algorithms presented by other researchers.
A data-mining approach is applied to optimize the energy consumption of an air handling unit. A multi-perceptron ensemble algorithm is used to model a chiller, a pump, and the supply and return fans. A non-linear mode...
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A data-mining approach is applied to optimize the energy consumption of an air handling unit. A multi-perceptron ensemble algorithm is used to model a chiller, a pump, and the supply and return fans. A non-linear model is developed to minimize the total energy consumption of the air-handling unit while maintaining the temperature of the supply air and the static pressure in a predetermined range. A dynamic, penalty-based, electromagnetism-like algorithm is designed to solve the proposed model. In all, 200 test data points are used to validate the proposed algorithm. The computational results show that the energy consumed by the air-handling unit is reduced by almost 23%. (C) 2013 Elsevier B.V. All rights reserved.
This paper presents modifications of the electromagnetism-like (EM) algorithm for solving global optimization problems with box constraints. The modifications are concerned with the charges associated with each point ...
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ISBN:
(纸本)9789955282839
This paper presents modifications of the electromagnetism-like (EM) algorithm for solving global optimization problems with box constraints. The modifications are concerned with the charges associated with each point in the population. The purpose here is to improve efficiency and solution accuracy by exploring the attraction-repulsion mechanism of the EM algorithm. Several widely used benchmark problems were solved in a performance evaluation of the new algorithm when compared with the original one. The modified algorithm has also been compared with other heuristic population-based methods.
This article introduces a novel hybrid evolutionary algorithm for recurrent fuzzy neural systems design in applications of nonlinear systems. The hybrid learning algorithm, IEMBP-improved electromagnetism-like (EM) wi...
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This article introduces a novel hybrid evolutionary algorithm for recurrent fuzzy neural systems design in applications of nonlinear systems. The hybrid learning algorithm, IEMBP-improved electromagnetism-like (EM) with back-propagation (BP) technique, combines the advantages of EM and BP algorithms which provides high-speed convergence, higher accuracy and less computational complexity (computation time in seconds). In addition, the IEMBP needs only a small population to outperform the standard EM that uses a larger population. For a recurrent neural fuzzy system, IEMBP simulates the 'attraction' and 'repulsion' of charged particles by considering each neural system parameters as a charged particle. The EM algorithm is modified in such a way that the competition selection is adopted and the random neighbourhood local search is replaced by BP without evaluations. Thus, the IEMBP algorithm combines the advantages of multi-point search, global optimisation and faster convergence. Finally, several illustration examples for nonlinear systems are shown to demonstrate the performance and effectiveness of IEMBP.
We consider a cross-docking system in which there is no temporary storage and trucks are permissible not to load or unload all of their products but are held and come back to the dock to continue their tasks in case t...
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We consider a cross-docking system in which there is no temporary storage and trucks are permissible not to load or unload all of their products but are held and come back to the dock to continue their tasks in case they are needed. Scheduling the trucks in such systems is a nondeterministic polynomial time-hard problem that motivates us to apply two well-known meta-heuristics-named genetic algorithm and electromagnetism-like algorithm for scheduling trucks with the objective of minimizing total flow time of the system, which is achieved through the best sequence of truck pairs. To attain the best robustness of these algorithms Taguchi's robust design method is employed. To demonstrate the effectiveness of the proposed methods especially for large-sized problems, various test problems are solved, and the computational results show that our proposed methods perform better than Yu (2002)'s methods.
This paper presents an algorithm for solving global optimization problems with bounded variables. The algorithm is a modification of the electromagnetism-like mechanism proposed by Birbil and Fang [An electromagnetism...
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This paper presents an algorithm for solving global optimization problems with bounded variables. The algorithm is a modification of the electromagnetism-like mechanism proposed by Birbil and Fang [An electromagnetism-like mechanism for global optimization, J. Global Optim. 25 (2003), pp. 263-282]. The differences are mainly on the local search procedure and on the force vector used to move each point in the population. Several widely-used benchmark problems were solved in a performance evaluation of the new algorithm when compared with the original one. A comparison with other stochastic methods is also included. The algorithm seems appropriate for large dimension problems.
Differential evolution (DE) has gained a lot of attention from the global optimization research community. It has proved to be a very robust algorithm for solving non-differentiable and non-convex global optimization ...
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Differential evolution (DE) has gained a lot of attention from the global optimization research community. It has proved to be a very robust algorithm for solving non-differentiable and non-convex global optimization problems. In this paper, we propose some modifications to the original algorithm. Specifically, we use the attraction-repulsion concept of electromagnetism-like (EM) algorithm to boost the mutation operation of the original differential evolution. We carried out a numerical study using a set of 50 test problems, many of which are inspired by practical applications. Results presented show the potential of this new approach.
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