Adaptive differential evolution and exponential crossover

Tvrdík, Josef

doi:10.1109/imcsit.2008.4747353

Cited by 28 publications

(26 citation statements)

References 8 publications

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“…In the binomial version, the components from the mutant individual are randomly selected, while in the exponential one they are organized into groups of compact sequences (Zaharie 2002). Analysis of the differences between these two variants showed that the use of exponential crossover to the detriment of the binomial one increases efficiency only for a small number of problems, and therefore, researchers did not focus on the crossover as a mean to improve performance (Tvrdik 2008).…”

Section: Crossovermentioning

confidence: 99%

The use of differential evolution algorithm for solving chemical engineering problems

Drăgoi

Curteanu

2016

Reviews in Chemical Engineering

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AbstractDifferential evolution (DE), belonging to the evolutionary algorithm class, is a simple and powerful optimizer with great potential for solving different types of synthetic and real-life problems. Optimization is an important aspect in the chemical engineering area, especially when striving to obtain the best results with a minimum of consumed resources and a minimum of additional by-products. From the optimization point of view, DE seems to be an attractive approach for many researchers who are trying to improve existing systems or to design new ones. In this context, here, a review of the most important approaches applying different versions of DE (simple, modified, or hybridized) for solving specific chemical engineering problems is realized. Based on the idea that optimization can be performed at different levels, two distinct cases were considered – process and model optimization. In both cases, there are a multitude of problems solved, from different points of view and with various parameters, this large area of successful applications indicating the flexibility and performance of DE.

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Section: Crossovermentioning

confidence: 99%

The use of differential evolution algorithm for solving chemical engineering problems

Drăgoi

Curteanu

2016

Reviews in Chemical Engineering

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“…This adaptive variant of DE is called competitive differential evolution (CDE) hereafter. Several variants of CDE were compared in different benchmark tests [23,24]. The CDE variant denoted b6e6rl was found as one of the most efficient among all the tested CDE variants.…”

Section: Cde Algorithm Version B6e6rlmentioning

confidence: 99%

Cooperation of Evolutionary Algorithms: A Comparison of Several Hierarchical Models

Poláková

Tvrdík

2015

Advances in Intelligent Systems and Computing

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The cooperation of evolutionary algorithms in a simple hierarchical model was proposed. Three adaptive variants of differential evolution and covariancematrix-adaptation evolutionary strategy were chosen for cooperation. The performance of the model was tested on the learning-based CEC 2015 suite of 15 functions at levels of the dimension 10 and 30. The results showed that the proposed cooperation is beneficial. The proposed model was superior significantly compared to any of individual algorithms included in the cooperation. The cooperation of the selected four algorithms appeared beneficial especially in the problems of dimension D = 30, where the cooperative model outperformed all the algorithms including other cooperative models in comparison.

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“…These four DE variants are also included in our study along with a variant of competitive DE [26] and two variants of DE based on composite trial vector generation strategies and control parameters that has been published recently [27].…”

Section: De Variants In Experimental Comparisonmentioning

confidence: 99%

“…b6e6rl) [26], and CoDE [27] with evolutionary approach to surviving the strategies successful in previous generation applied in jDE [3]. The advantage of EPSDE algorithm is its clear motivation and simplicity.…”

Section: Epsdementioning

confidence: 99%

“…Several variants of competitive DE differing both in the pool of DE strategies and in the set of control-parameters values were tested [25]. We use a variant of competitive DE that appeared well-performing and robust in different benchmark tests [26]. In this variant, denoted b6e6rl hereafter, 12 strategies are in competition (H = 12), six of them using the binomial crossover, rest of them using the exponential crossover.…”

Section: Competitive Dementioning

confidence: 99%

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Adaptive Variants of Differential Evolution: Towards Control-Parameter-Free Optimizers

Tvrdík

Poláková

Veselský

et al. 2013

Handbook of Optimization

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Abstract. Seven up-to-date adaptive variants of differential evolution were compared in six benchmark problems of two levels of dimension (D = 30 and D = 100). The opposition-based optimization was also implemented to each adaptive variant and compared in experiments. It was found that all the algorithms perform very reliably in the problems of D = 30, whereas their reliability rate in the problems of D = 100 differs substantially among the test problems. Only two algorithms (JADE and b6e6rl variant of competitive DE) operate with acceptable reliability in all the problems. Considering the computational costs, the rank of the algorithms is different in various problems. When the average performance over all the problems is taken into account, JADE was the most efficient and b6e6rl the most reliable. The implementation of opposition-based optimization into adaptive variants of differential evolution does not increase the reliability and its positive influence on the efficiency is rare. Based on the results, recommendations to application of adaptive algorithms are formed and the source code of the algorithms is available online.

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Adaptive differential evolution and exponential crossover

Cited by 28 publications

References 8 publications

The use of differential evolution algorithm for solving chemical engineering problems

The use of differential evolution algorithm for solving chemical engineering problems

Cooperation of Evolutionary Algorithms: A Comparison of Several Hierarchical Models

Adaptive Variants of Differential Evolution: Towards Control-Parameter-Free Optimizers

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