A review of population initialization techniques for evolutionary algorithms

Kazimipour, Borhan; Li, Xiaodong; Qin, A. K.

doi:10.1109/cec.2014.6900618

Cited by 163 publications

(95 citation statements)

References 64 publications

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“…Recently, a large and growing body of literature are devoted to advanced EA population initialization techniques [12], [14]. Indeed, several studies claimed that it is possible to improve EAs (including DE) performance only by employing more advanced initialization techniques [22], [23].…”

Section: B Population Initialization Techniquesmentioning

confidence: 99%

“…Indeed, numerous techniques have been proposed and applied to different problems. Reviewing all published works on this topic demands a comprehensive survey paper which is out of the scope of this study [14].…”

Section: B Population Initialization Techniquesmentioning

confidence: 99%

“…Obviously, when improper parameter values are used, the performance of population initialization techniques may be affected and the resulting conclusions may not be precise and practical. This issue, which exists in studies on both low and high dimensional problems, is the source of some contradictions and confusions on the effectiveness of advanced initializers [13], [14].…”

Section: Introductionmentioning

confidence: 99%

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Effects of population initialization on differential evolution for large scale optimization

Kazimipour

Qin

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-This work provides an in-depth investigation of the effects of population initialization on Differential Evolution (DE) for dealing with large scale optimization problems. Firstly, we conduct a statistical parameter sensitive analysis to study the effects of DE's control parameters on its performance of solving large scale problems. This study reveals the optimal parameter configurations which can lead to the statistically superior performance over the CEC-2013 large-scale test problems. Thus identified optimal parameter configurations interestingly favour much larger population sizes while agreeing with the other parameter settings compared to the most commonly employed parameter configuration. Based on one of the identified optimal configurations and the most commonly used configuration, which only differ in the population size, we investigate the influence of various population initialization techniques on DE's performance. This study indicates that initialization plays a more crucial role in DE with a smaller population size. However, this observation might be the result of insufficient convergence due to the use of a large population size under the limited computational budget, which deserve more investigations.

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Section: B Population Initialization Techniquesmentioning

confidence: 99%

Section: B Population Initialization Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effects of population initialization on differential evolution for large scale optimization

Kazimipour

Qin

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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“…In initialization step, a temporary population (a.k.a. original population) is generated using conventional random number generators or more advanced initialization techniques [31], [32]. Then, the opposite population is produced and merged with the original population.…”

Section: Theorem 3 (Central Opposition Theorem)mentioning

confidence: 99%

A novel hybridization of opposition-based learning and cooperative co-evolutionary for large-scale optimization

Kazimipour

Omidvar

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-Opposition-based learning (OBL) and cooperative co-evolution (CC) have demonstrated promising performance when dealing with large-scale global optimization (LSGO) problems. In this work, we propose a novel framework for hybridizing these two techniques, and investigate the performance of simple implementations of this new framework using the most recent LSGO benchmarking test suite. The obtained results verify the effectiveness of our proposed OBL-CC framework. Moreover, some advanced statistical analyses reveal that the proposed hybridization significantly outperforms its component methods in terms of the quality of finally obtained solutions.

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“…EAs are robust and suitable for effectively obtaining optima, and with a smaller probability than other algorithms of falling into local optima. The methodology of the EAs has been discussed in many literatures [2][3][4][5][6][7][8][9], and EAs are also successfully used in various industrial applications [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A backtracking evolutionary algorithm for power systems

Chiou

Chang

2017

MATEC Web Conf.

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Abstract. This paper presents a backtracking variable scaling hybrid differential evolution, called backtracking VSHDE, for solving the optimal network reconfiguration problems for power loss reduction in distribution systems. The concepts of the backtracking, variable scaling factor, migrating, accelerated, and boundary control mechanism are embedded in the original differential evolution (DE) to form the backtracking VSHDE. The concepts of the backtracking and boundary control mechanism can increase the population diversity. And, according to the convergence property of the population, the scaling factor is adjusted based on the 1/5 success rule of the evolution strategies (ESs). A larger population size must be used in the evolutionary algorithms (EAs) to maintain the population diversity. To overcome this drawback, two operations, acceleration operation and migrating operation, are embedded into the proposed method. The feeder reconfiguration of distribution systems is modelled as an optimization problem which aims at achieving the minimum loss subject to voltage and current constraints. So, the proper system topology that reduces the power loss according to a load pattern is an important issue. Mathematically, the network reconfiguration system is a nonlinear programming problem with integer variables. One three-feeder network reconfiguration system from the literature is researched by the proposed backtracking VSHDE method and simulated annealing (SA). Numerical results show that the perfrmance of the proposed method outperformed the SA method.

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A review of population initialization techniques for evolutionary algorithms

Cited by 163 publications

References 64 publications

Effects of population initialization on differential evolution for large scale optimization

Effects of population initialization on differential evolution for large scale optimization

A novel hybridization of opposition-based learning and cooperative co-evolutionary for large-scale optimization

A backtracking evolutionary algorithm for power systems

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