A novel Constraint-Handling Technique (CHT) for evolutionary algorithms (EAs) applied to constrained optimization problems is proposed. It is assumed that the feasible region of the constrained optimization problem is...
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A novel Constraint-Handling Technique (CHT) for evolutionary algorithms (EAs) applied to constrained optimization problems is proposed. It is assumed that the feasible region of the constrained optimization problem is defined by a convex-hull of multiple vertices. On the other hand, without loss of generality, the search space of EA is given by a hyper-cube. The proposed CHT called Convex-Hull Mapping (CHM) transforms the real vector in the search space of EA into the solution in the feasible region. It is also proven that CHM performs a surjective mapping from the search space of EA to the feasible region. Although the proposed CHM can be applied to any EAs, one of the latest EAs, or Adaptive Differential Evolution (ADE), is used in this paper. By using ADE, CHM is compared with conventional CHTs in a real-world optimization problem in the field of finance, namely the portfolio optimization problem. Portfolio optimization is the process of determining the best proportion of investment in different assets according to some objective. Specifically, to reveal the characteristic of CHM depending on the number of the above vertices, three different formulations of the portfolio optimization problem are employed to evaluate the performance of ADE using CHM. Numerical experiments show that CHM is better than conventional CHTs in most cases. Moreover, the hybrid method combining CHM with a conventional CHT outperforms the original CHT.
evolutionary algorithms, including evolutionary programming and evolution strategies, have often been applied to real-valued function optimization problems. These algorithms generally operate directly on the real valu...
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evolutionary algorithms, including evolutionary programming and evolution strategies, have often been applied to real-valued function optimization problems. These algorithms generally operate directly on the real values to be optimized, in contrast with genetic algorithms which usually operate on a separately coded transformation of the objective variables, evolutionary algorithms often rely on a second-level optimization of strategy parameters, tunable variables that in part determine how each parent will generate offspring. Two alternative methods for performing this second-level optimization have been proposed and are compared across a series of function optimization tasks, The results appear to favor the approach offered originally in evolution strategies, although the applicability of the findings may be limited to the case where each parameter of a parent solution is perturbed independently of all others.
Scheduling is a critical and challenging task in manufacturing systems, especially in large-scale complex systems like wafer fabrication facilities. Although evolutionary algorithms (EAs) have demonstrated many succes...
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Scheduling is a critical and challenging task in manufacturing systems, especially in large-scale complex systems like wafer fabrication facilities. Although evolutionary algorithms (EAs) have demonstrated many successful applications in the field of manufacturing scheduling, there are very few studies on scheduling of wafer fabs using EAs. Dispatching rules are one of the most common techniques for fab scheduling. In this paper, we present six ways of applying EAs for enhancing the rule-based scheduling system. We provide potential EA-based solutions and review relevant literature. Many of the mentioned viewpoints can serve as new research topics for both researchers in the fields of scheduling and evolutionary computation (EC). Several general EC techniques including multiobjective optimization, expensive optimization, and parallelization are also introduced and shown to be helpful to fab scheduling. (C) 2012 Elsevier Ltd. All rights reserved.
Reduced and ordered binary decision diagrams (ROBDDs) are compact data structures used for representing logic functions. The ROBDD size is very sensitive to the chosen variable ordering of the logical function. The au...
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Reduced and ordered binary decision diagrams (ROBDDs) are compact data structures used for representing logic functions. The ROBDD size is very sensitive to the chosen variable ordering of the logical function. The authors propose a methodology based on simulated annealing (SA) algorithms and on genetic algorithms (GAs) for optimising ROBDDs.
In this letter, a new evolutionary algorithm-based method for the optimization of intrinsic elements of small-signal model (SSM) of GaN HEMT devices is presented. The method uses a unique search space exploration stra...
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In this letter, a new evolutionary algorithm-based method for the optimization of intrinsic elements of small-signal model (SSM) of GaN HEMT devices is presented. The method uses a unique search space exploration strategy for evolutionary algorithms to obtain an optimized compact SSM from the extracted parameter and measured S-parameters. The validity of the method is verified by comparing the measured S-parameter data of a 2x0.1x50 mu m(2) GaN/Si HEMT and a 4x0.1x75 mu m(2) GaN/SiC HEMT in the frequency range of 1 to 30 GHz. The modeled data and measured data are in good agreement.
Parameters optimization is a research hotspot of SVM and has gained increasing interest from various research fields. Compared with other optimization algorithms, genetic-based evolutionary algorithms that have achiev...
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Parameters optimization is a research hotspot of SVM and has gained increasing interest from various research fields. Compared with other optimization algorithms, genetic-based evolutionary algorithms that have achieved optimization according to the laws of separation and free combination in genetics are gradually attracted much attention. Also, due to the characteristics of self-organization and self-adaptation, these algorithms often enable SVM to obtain appropriate parameters, so that the model can be applied to more applications. Additionally, many improvements have been proposed in the past two decades in order to allow the optimized SVM model to obtain better performance. This work focuses on reviewing the current state of genetic-based evolutionary algorithms used to optimize parameters of SVM and its variants. First, we introduce the principles of SVM and provide a survey on optimization methods of its parameters. Then we propose a taxonomy of improving genetic-based evolutionary algorithms according to code mechanism, parameters control, population structure, evolutionary strategy, operation mechanism, operators, and many other hybrid approaches. Furthermore, this paper analyzes and compares the advantages and disadvantages of the above algorithms explicitly, and provides their applicable scenarios as well. Finally, we highlight the existing problems of genetic-based evolutionary algorithms used for parameters optimization of SVM and prospect development trends of this field in the future.
The present paper presents an alternative methodology to determine frequency-dependent network equivalents from the frequency response of electric power networks. According to this methodology, one can substitute part...
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The present paper presents an alternative methodology to determine frequency-dependent network equivalents from the frequency response of electric power networks. According to this methodology, one can substitute part of the power network through a simplified electric circuit, reducing the number of elements to be modeled without introducing any inaccuracy to the precise evaluation of large power networks. The main challenge relays on determining a rational function capable of producing the same frequency response characteristic of the original circuit that would be replaced by the equivalent. The rational function determines the impedance to be used in the network equivalent. The methodology is based on evolutionary algorithms (EAs). Basically, different set of values for the rational function parameters are evaluated. During the convergence process, the EAs modify the values for the function's parameters suitably, reducing the RMS error between the original frequency response and the one produced by the equivalent circuit. The results obtained through this methodology were compared with those obtained through the "Vector Fitting" methodology, which is the methodology commonly used to solve similar problems.
Sparse large-scale multiobjective problems (LSMOPs) are characterized as an NP-hard issue that undergoes a significant presence of zero-valued variables in Pareto optimal solutions. In solving sparse LSMOPs, recent st...
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Sparse large-scale multiobjective problems (LSMOPs) are characterized as an NP-hard issue that undergoes a significant presence of zero-valued variables in Pareto optimal solutions. In solving sparse LSMOPs, recent studies typically employ a specialized two-layer encoding, where the low-level layer undertakes the optimization of zero variables and the high-level layer is in charge of non-zero variables. However, such an encoding usually puts the low-level layer in the first place and thus cannot achieve a balance between optimizing zero and non-zero variables. To this end, this paper proposes to build a two-way association between the two layers using a mutual preference calculation method and a two-way matching strategy. Essentially, the two-way association balances the influence of two layers on the encoded individual by relaxing the control of the low-level layer and enhancing the control of the high-level layer, thus reaching the balance between the optimizations of zero and non-zero variables. Moreover, we propose a new evolutionary algorithm equipped with the modules and compare it with several state-of-the-art algorithms on 32 benchmark problems. Extensive experiments verify its effectiveness, as the proposed modules can improve the two-layer encoding and help the algorithm achieve superior performance on sparse LSMOPs.
This paper presents a comparative analysis of three evolutionary algorithms, namely, Backtracking Search Algorithm, Cuckoo Search Algorithm and Artificial Bee Colony algorithms for synthesis of a scanned linear array ...
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This paper presents a comparative analysis of three evolutionary algorithms, namely, Backtracking Search Algorithm, Cuckoo Search Algorithm and Artificial Bee Colony algorithms for synthesis of a scanned linear array of uniformly spaced parallel half wavelength dipole antennas. Here, antenna parameters, namely Side Lobe Level, reflection coefficient and wide null depth are taken into consideration for comparison between algorithms. In addition to it, statistical parameters, namely best fitness value, mean and standard deviation of the fitness values obtained from algorithms are compared. Mutual coupling that exists among the antenna elements is included in obtaining radiation patterns and the self-impedances along with the mutual impedances are calculated by induced Electro-Motive Force method. Two different examples are shown in this paper to validate the effectiveness of the utilized approach. Although, this approach is applied to a linear array of dipole antennas;this can be utilized for other array geometries as well.
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