A parallel between differential evolution and genetic algorithms with exemplification in a microfluidics optimization problem

Codreanu, I.

doi:10.1109/smicnd.2005.1558816

“…Furthermore, DE is easy to apply to a wide variety of problems regardless of noisy, multi-modal, multi-dimensional spaces, which typically make problems difficult to optimize. Although DE consists of two important parameters, Cr and F, those parameters do not require the same amount of tuning as those associated with other evolutionary algorithms [101]. Liao has proposed a hybridization of DE and a local search algorithm modeled after the harmony search (HS) algorithm to find the global optimum [102].…”

Section: Hybridization Of De With Other Evolution Algorithmsmentioning

confidence: 99%

Differential Evolution: A Survey and Analysis

Eltaeib

¹

,

Mahmood

²

2018

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Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.

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“…The advantage of DE is that, in general, it frequently shows better solutions than those yielded by GA and other evolutionary algorithms [97][98][99]. Population-Based Incremental Learning (RCPBIL) algorithm [106].…”

Section: Hybridization Of De With Other Evolution Algorithmsmentioning

confidence: 99%

Large-Scale Evolutionary Optimization Using Multi-Layer Strategy Differential Evolution

Eltaeib

¹

,

Mahmood

²

2018

Computational Collective Intelligence

0

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Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. The Multi-Layer Strategies Differential Evolution (MLSDE) algorithm, which finds optimal solutions for large scale problems. To solve large scale problems were grouped different strategies together and applied them to date set. Furthermore, these strategies were applied to selected vectors to strengthen the exploration ability of the algorithm. Extensive computational analysis was also carried out to evaluate the performance of the proposed algorithm on a set of well-known CEC 2015 benchmark v functions. This benchmark was utilized for the assessment and performance evaluation of the proposed algorithm. vi ACKNOWLEDGEMENTS Special appreciations to my family. Words cannot express how thankful I am to my wife, my daughters, my mother, my mother-in law, and father-in-law for all of the sacrifices and prayers for me that you've made on my behalf. I would like to express my special thanks to my advisor Professor Ausif Mahmood and his efforts, encouraging and guiding me to grow as a research scientist.. vii

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“…The DEA is much improved as compared to GA and other algorithms of the same class and is quoted in [22][23][24]. [20].…”

Section: The Differential Evolution Algorithm (Dea)mentioning

confidence: 99%

Application of Differential Evolution for Wind Turbine Micrositing

Massan¹,

Wagan²,

Shaikh³

et al. 2017

Mehran Univ. res. j. eng. technol.

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WTM (Wind Turbine Micrositing) has been an important topic of discussion in recent times. A number of Evolutionary Algorithms have been applied to the WTM problem. The DEA (Differential Evolution Algorithm) is used for a bi-constrained optimization for getting maximum power production at the least cost from a 2x2 km space. It is shown that the DEA performs comparably to the GA (Genetic Algorithms) for wind farm optimization. The optimal configuration obtained enlists the number of turbines, the cost of power generated as well as the power produced. Moreover, this study is augmented by comparison with past approaches by using the GA for the same purpose.

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A parallel between differential evolution and genetic algorithms with exemplification in a microfluidics optimization problem

Cited by 7 publications

References 8 publications

Differential Evolution: A Survey and Analysis

Differential Evolution: A Survey and Analysis

Large-Scale Evolutionary Optimization Using Multi-Layer Strategy Differential Evolution

Application of Differential Evolution for Wind Turbine Micrositing

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