Array pattern optimization using electromagnetism-like algorithm

Jhang, Jhen Yan; Lee, Kun Chou

doi:10.1016/j.aeue.2008.04.001

Cited by 43 publications

(20 citation statements)

References 13 publications

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“…Compared with the other methods, EM is a much more powerful algorithm for global optimization [26]. EM has been used in the areas of function optimization, resource constraint project scheduling problems [29], flowshop scheduling problem [30], array pattern optimization [31] and FOPID control optimization [32]. EM includes four phases: initialization of the algorithm, local search, calculation of total force vector, and movement according to the total force [26−28].…”

Section: Original Electromagnetism-like Algorithmmentioning

confidence: 99%

Modified electromagnetism-like algorithm and its application to slope stability analysis

Zhang

Cao

2011

J. Cent. South Univ. Technol.

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In the view of the disadvantages of complex method (CM) and electromagnetism-like algorithm (EM), complex electromagnetism-like hybrid algorithm (CEM) was proposed by embedding complex method into electromagnetism-like algorithm as local optimization algorithm. CEM was adopted to search the minimum safety factor in slope stability analysis and the results show that CEM holds advantages over EM and CM. It combines the merits of two and is more stable and efficient. For further improvement, two CEM hybrid algorithms based on predatory search (PS) strategies were proposed, both of which consist of modified algorithms and the search area of which is dynamically adjusted by changing restriction. The CEM-PS1 adopts theoretical framework of original predatory search strategy. The CEM-PS2 employs the idea of area-restricted search learned from predatory search strategy, but the algorithm structure is simpler. Both the CEM-PS1 and CEM-PS2 have been demonstrated more effective and efficient than the others. As for complex method which locates in hybrid algorithm, the optimization can be achieved at a convergence precision of 1×10 −3 , which is recommended to use.

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Section: Original Electromagnetism-like Algorithmmentioning

confidence: 99%

Modified electromagnetism-like algorithm and its application to slope stability analysis

Zhang

Cao

2011

J. Cent. South Univ. Technol.

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“…Such decision variables represent a different threshold point th that is used for the segmentation. Therefore, the complete population is represented as: (26) Where t represents the iteration number, T refers to the transpose operator, N is the size of the population and 1, 2,3 c  is set for RGB images while 1 c  is chosen for gray scale images. For this problem, the boundaries of the search space are set to 0 l  and 255 u  , which correspond to image intensity levels.…”

Section: Particle Representationmentioning

confidence: 99%

“…Although the EMO algorithm shares some characteristics to PSO and ACO, recent works have exhibited its better accuracy regarding optimal parameters [18 -21], yet showing convergence [22]. 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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“…It has been effectively used in different sorts of research and engineering problems, such as neural network training [6], vehicle routing problems [7] flow shop scheduling problems [8], communication [9], array pattern optimization in circuits [10], image processing [11], intelligent forecasting [12], and control systems [13] and so on. But EMA exist some defects such as premature convergence etc.…”

Section: Introductionmentioning

confidence: 99%

Quantum-behaved Electromagnetism-like Mechanism Algorithm for Economic Load Dispatch of Power System

Zhang¹,

Gong²,

Duan³

2015

Journal of Electrical Engineering and Technology

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-This paper presents a new algorithm called Quantum-behaved Electromagnetism-like Mechanism Algorithm which is used to solve economic load dispatch of power system. Electromagnetismlike mechanism algorithm simulates attraction and repulsion mechanism for particles in the electromagnetic field. Every solution is a charged particle, and it move to optimum solution according to certain criteria. Quantum-behaved electromagnetism-like mechanism algorithm merges quantum computing theory with electromagnetism-like mechanism algorithm. Superposition characteristic of quantum methodology can make a single particle present several states, and the characteristic potentially increases population diversity. Probability representation of quantum methodology is to make particle state be presented according to a certain probability. And the quantum rotation gates are used to realize update operation of particles. The algorithm is tested for 13-generator system and 40-generator system, which validates it can effectively solve economic load dispatch problem. Through performance comparison, it is obvious the solution is superior to other optimization algorithm.

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Array pattern optimization using electromagnetism-like algorithm

Cited by 43 publications

References 13 publications

Modified electromagnetism-like algorithm and its application to slope stability analysis

Modified electromagnetism-like algorithm and its application to slope stability analysis

A Multilevel Thresholding algorithm using electromagnetism optimization

Quantum-behaved Electromagnetism-like Mechanism Algorithm for Economic Load Dispatch of Power System

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