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Robust coordinated design of excitation and TCSC-based stabilizers using genetic algorithms

ELECTRIC POWER SYSTEMS RESEARCH 2004年第2-3期69卷 129-141页

作者： Abdel-Magid, YL Abido, MA King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

Power system stability enhancement via robust coordinated design of a power system stabilizer (PSS) and a thyristor-controlled series capacitor (TCSC)-based stabilizer is thoroughly investigated in this paper. The coordinated design problem of robust excitation and TCSC-based controllers over a wide range of loading conditions and system configurations is formulated as an optimization problem with an eigenvalue-based objective function. The real-coded genetic algorithm (RCGA) is employed to search for optimal controller parameters. This study also presents a singular value decomposition (SVD)-based approach to assess and measure the controllability of the poorly damped electromechanical modes by different control inputs. The damping characteristics of the proposed control schemes are also evaluated in terms of the damping torque coefficient over a wide range of loading conditions. The proposed stabilizers were tested on a weakly connected power system. The damping torque coefficient analysis, nonlinear simulation results, and eigenvalue analysis show the effectiveness and robustness of the proposed approach over a wide range of loading conditions. (C) 2003 Elsevier B.V. All rights reserved.

关键词： power system stabilizer FACTS devices TCSC real-coded genetic algorithms

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Development of plotting position for the general extreme value distribution

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JOURNAL OF HYDROLOGY 2012年 475卷 259-269页

作者： Kim, Sooyoung Shin, Hongjoon Joo, Kyoungwon Heo, Jun-Haeng Yonsei Univ Sch Civil & Environm Engn Seoul 120749 South Korea

Probability plotting positions are used to graphically display the annual maximum rainfall or flood and to estimate the exceedance probabilities of those values. Therefore, the graphical approach using plotting positions has been applied in many hydrology and water resource engineering fields. The definition of unbiased plotting positions by Cunnane (1978) as the mean of the order statistics from reduced variates has influenced researchers to develop the plotting position of the probability distribution containing shape parameters. In this study, the plotting position formula for the general extreme value (GEV) distribution was derived by using the theoretical reduced variates of the GEV distribution for various sample sizes and shape parameters. To choose an appropriate plotting position formula, we examined eight plotting position formula types containing coefficients of skewness or squared coefficients of skewness in the numerator and/or denominator. In addition, the parameters of the plotting position formula for the GEV distribution were estimated by using a genetic optimization method known as the real-coded genetic algorithm (RGA). The accuracy of the derived plotting position formula for the GEV distribution was examined on the basis of the root mean square errors and relative bias between the theoretical reduced variates and those calculated from the derived and existing plotting position formulas. The derived plotting formula was found to be useful if the range of the shape parameter was within +/- 0.2. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Plotting position formula GEV distribution real-coded genetic algorithms

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Bolstering efficient SSGAs based on an ensemble of probabilistic variable-wise crossover strategies

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SOFT COMPUTING 2016年第6期20卷 2149-2176页

作者： Gwak, Jeonghwan Jeon, Moongu Pedrycz, Witold Gwangju Inst Sci & Technol Sch Informat & Commun Gwangju 500712 South Korea Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G6 Canada Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21413 Saudi Arabia

A crossover operator in genetic algorithms (GAs) plays an essential role as the main search operator to breed offspring by exchanging information between individuals. Although different types of crossover operators have been developed for real-coded GAs (RCGAs), there has been very little research on combining different crossover operators to build more effective and efficient RCGAs. In this work, we propose new steady-state generation alternation-based RCGAs (SSGAs) ameliorated with (i) an ensemble of different probabilistic variable-wise crossover strategies, which is realized by the corresponding parallel populations, to utilize synergetic and complementary effect with their efficient operations, and (ii) efficient operation at each evolution step to obtain further performance enhancement. To investigate the performance of this ensemble with respect to search abilities and computation time, we compare the proposed algorithms against various SSGAs when running 27 benchmark functions. Empirical studies showed that the proposed algorithms exhibit better performance than the contestant SSGAs on these functions. Moreover, a comparison with the state-of-the-art evolutionary algorithms on eight difficult benchmark functions clearly demonstrated outperformance of the proposed algorithms.

关键词： real-coded genetic algorithms Steady state genetic algorithms Blend crossover Parent centric crossover Simulated binary crossover Ensemble

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Theoretical Analysis of Phenotypic Diversity in real-Valued Evolutionary algorithms With More-Than-One-Element Replacement

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2011年第2期15卷 248-266页

作者： Someya, Hiroshi Inst Stat Math Tokyo 1908562 Japan

This paper investigates the evolutionary dynamics of steady-state real-valued evolutionary algorithms (RVEAs) with more-than-one-element replacement theoretically, whereas most theoretical studies of RVEAs have considered single- or all-element replacement. The subject RVEAs are of interest because they appear in various fashions, such as real-coded genetic algorithms (RCGAs) and island RVEAs. The analysis is conducted to deepen the understanding of how RVEA components and their parameters influence the phenotypic diversity in the parental pool. First, the diversity evolution is modeled mathematically and then a constraint of diversity control is derived from this model. The control method is demonstrated and the accuracy of the theoretical predictions is evaluated through experiments. The shortest convergence time is estimated. The analysis requires few assumptions about either the variation operators or selection schemes, and therefore is applicable to various RVEAs. As such an application in RCGAs, the influence on the diversity evolution of offspring-population size, parental-pool size, crossover-operator parameter, and selection-pressure parameters of two selection mechanisms is quantified. The computational efficiency, search stability, and selection-pressure controllability are then evaluated. The analysis results are discussed from a practical point of view in parameter settings for preventing premature convergence.

关键词： Diversity control parameter setting real-coded genetic algorithms selection pressure self-adaptive features

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real-coded genetic Algorithm Parameter Setting for Cognitive Radio Adaptation

Real-Coded Genetic Algorithm Parameter Setting for Cognitive...

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International Conference on Smart Communications in Network Technologies

作者： N. Ali Saoucha A. C. Lammari K. Benatchba University of M'sila L'Ecole nationale superieure d'informatique

ISBN: (纸本)9781479906956

Cognitive radio (CR) is an emerging promising technology for future wireless communication networks. For a given status of dynamic wireless channel environment, the radio uses intelligence to optimize user's QoS by adapting the transmission parameters of the radio. Transmission parameters adaptation in a dynamic multicarrier environment has been previously studied using genetic algorithms (GA). The main goal was to select the optimal transmission parameters. When genetic algorithms are applied to complex problems, such as in multicarrier (MC) one, their execution time, to find a solution, increases. In this paper, we propose a new algorithm based on a real-coded genetic algorithm. Several tests have been done to tune the different parameters of the algorithm. The new algorithm is compared to the standard GA, based on De Jong's standard parameter settings and binary coding, used for optimizing QoS for cognitive radio. The tests carried on show that our algorithm gives better performance in terms of convergence speed.

关键词： Cognitive radio Multiobjective optimization genetic algorithm parameter tuning real-coded genetic algorithms Convergence speed

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An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification

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INFORMATION SCIENCES 2009年第23期179卷 3998-4028页

作者： Ibanez, Oscar Ballerini, Lucia Cordon, Oscar Damas, Sergio Santamaria, Jose European Ctr Soft Comp Mieres Asturias Spain Univ Edinburgh Inst Percept Act & Behav Edinburgh Midlothian Scotland Univ Jaen Dpt Comp Sci Jaen Spain

Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. One of the crucial tasks throughout all this process is the craniofacial superimposition which tries to find a good fit between a 3D model of the skull and the 2D photo of the face. This photographic supra-projection stage is usually carried out manually by forensic anthropologists. It is thus very time consuming and presents several difficulties. In this paper, we aim to demonstrate that real-coded evolutionary algorithms are suitable approaches to tackle craniofacial superimposition. To do so, we first formulate this complex task in forensic identification as a numerical optimization problem. Then, we adapt three different evolutionary algorithms to solve it: two variants of a real-coded genetic algorithm and the state of the art evolution strategy CMA-ES. We also consider an existing binary-coded genetic algorithm as a baseline. Results on several superimposition problems of real-world identification cases solved by the Physical Anthropology lab at the University of Granada (Spain) are considered to test our proposals. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Forensic identification Craniofacial superimposition Image registration Evolutionary algorithms real-coded genetic algorithms CMA-ES

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GPAX: genetic PARABOLIC ADAPTIVE CROSSOVER OPERATOR

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2012年第3期18卷 225-236页

作者： Luis Alvarez, Jose Gegundez, Manuel E. Luis Arjona, Jose Univ Huelva Dept Informat Technol Huelva Spain Univ Huelva Dept Math Huelva Spain Univ Huelva Dept Comp Sci Huelva Spain

In this paper we propose a new crossover operator for real coded evolutionary algorithms that is based on a parabolic probability density function. This density function depends on two real parameters a and p which have the capacity to achieve exploration and exploitation dynamically during the evolutionary process in relation to the best individuals. In other words, the proposed crossover operator is able to handle the generational diversity of the population in such a way that it can either generate additional population diversity, therefore allowing exploration to take effect, or use the diversity previously generated to exploit the better solutions. In order to test the performance of this crossover, we have used a set of test functions and have made a comparative study of the proposed crossover against other classic crossover operators. The analysis of the results allows us to affirm that the proposed operator displays a very suitable behavior, although, it should be noted that it offers a better behavior applied to complex search spaces than simple ones.

关键词： real-coded genetic algorithms Crossover Operator Adaptive Operators

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genetic algorithm designed for solving portfolio optimization problems subjected to cardinality constraint

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2018年第1期9卷 294-305页

作者： Jalota, Hemant Thakur, Manoj Indian Inst Technol Mandi Mandi 175001 Himachal Prades India

In the present study, a new algorithm named BEXPM-RM is proposed which require no constraint handling techniques to solve portfolio optimization problems subjected to budget, cardinality, and lower/upper bound constraints. The algorithm presented combines the BEX-PM (Thakur et al. in Appl Math Comput 235:292-317, 2014) genetic algorithm (GA) together with repair mechanism (RM) proposed by Chang et al. (Comput Oper Res 27(13):1271-1302, 2000). BEXPM GA tries to efficiently explore the search space whereas repair method suggested by Chang et al. (2000) ensures that a solution string is always feasible subject to the budget, cardinality, and lower/upper bound constraints. To analyze the performance of BEXPM-RM, six portfolio optimization problems are considered from the literature (Chang et al. 2000;Barak et al. in Eur J Oper Res 228(1):141-147, 2013). Among these one problem uses fuzzy set theory and others used probability theory to quantify attributes of a portfolio. In addition to these problems, a new portfolio model is formulated in fuzzy environment to analyze the effect of providing different sets of lower or/and upper bound to an asset.

关键词： real-coded genetic algorithms Bounded exponential crossover Swap mutation Budget Cardinality and lower/upper bound constraints

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Analyzing the statistical features of CIXL2 crossover offspring

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SOFT COMPUTING 2005年第4期9卷 270-279页

作者： Hervás-Martínez, C Ortiz-Boyer, D Univ Cordoba Dept Comp Sci E-14071 Cordoba Spain

We cannot deny the effort that the scientific community is devoting to the explanation of the features of the crossover operator of real-coded genetic algorithms and its effect over the evolutive process. This paper is another step in that direction, we analyze the behaviour of the Confidence Interval Based Crossover using L-2 Norm (CIXL2). This crossover is based on the learning of the statistical features of localization and dispersion of the best individuals of the population. The crossover obtains, by means of a L-2 norm, the estimators of the parameters of localization and dispersion of the distributions of the fittest individuals. From this estimation three virtual parents are created using the localization parameter and the lower and upper bounds of the bilateral confidence intervals of the gene values of the best individuals of the population. This paper studies the statistical features of the offspring generated by this crossover and corroborates this study showing the behaviour of the crossover in a set of test functions.

关键词： real-coded genetic algorithms statistical features learning confidence intervals L-2-norm

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A novel hybrid genetic algorithm for constrained optimization

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2013年第1期4卷 86-93页

作者： Deep, Kusum Das, Kedar Nath Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttar Pradesh India Natl Inst Technol Silchar Dept Math Silchar 788010 Assam India

Finding global optimal solution of a non-linear constrained optimization problem with high complexity is a challenge for the researchers. Now days, real coded genetic algorithm (GA) becomes popular to solve them, due to their diversity preserving mechanism. In recent literature it is proved that for solving constrained optimization problem, the real coded GA (LX-PM) that uses Laplace Crossover and Power Mutation, is much efficient. In this paper an attempt is made to improve the performance of LX-PM, hybridizing with Quadratic Approximation. The efficiency and reliability of the design hybrid algorithm is realized through a set of 15 benchmark test problems.

关键词： real-coded genetic algorithms Hybrid genetic algorithms Quadratic approximation Constrained optimization Penalty function

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