A differential evolution algorithm with intersect mutation operator

Zhou, Yinzhi; Li, Xinyu; Gao, Liang

doi:10.1016/j.asoc.2012.08.014

Cited by 97 publications

(31 citation statements)

References 21 publications

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“…NFC is the most common performance metric used in evolutionary algorithms [19] [33]. The results are generated using Exponential and binomial crossover schemes over 30 independent trials.…”

Section: Test Functions and Experimental Resultsmentioning

confidence: 99%

Fitness Proportionate Random Vector Selection based DE Algorithm (FPRVDE)

Abbas¹,

Ahmad²,

Jabeen³

2016

ijacsa

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“…NFC is the most common performance metric used in evolutionary algorithms [19] [33]. The results are generated using Exponential and binomial crossover schemes over 30 independent trials.…”

Section: Test Functions and Experimental Resultsmentioning

confidence: 99%

Fitness Proportionate Random Vector Selection based DE Algorithm (FPRVDE)

Abbas¹,

Ahmad²,

Jabeen³

2016

ijacsa

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“…These benchmark functions are commonly used multidimensional global optimization problems that are given in the appendix section of this paper containing all necessary detail. Performance parameter Number of Function Calls (NFC) is one the most commonly used metric in evolutionary algorithms (Islam et al, 2012;Rahnamayan et al, 2008;Zhou et al, 2013) is used to measure the convergence speed of conventional DE mutations strategies and TSRVDE strategies. The success rate (SR) and acceleration rate (AR) of all mutation strategies are also calculated for analysis.…”

Section: Test Functions and Experimental Resultsmentioning

confidence: 99%

Tournament selection mechanism based random vector selection in differential evolution algorithm

Abbas

2017

Int. j. adv. appl. sci.

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Differential Evolution (DE) is a simple, powerful and easy to use global optimization algorithm. Trial vector generation mechanism influences the performance of DE algorithm significantly. This research work explores that whether random vector selection in trial vector generation have any role in improving the performance of DE algorithm. A novel tournament selection framework in DE algorithm is proposed to enhance its convergence speed. The novel TSRVDE framework employs tournament selection criteria focuses on the selection of random vector in DE trial vector. We can get rid of worst performing individual selection by TSRVDE that will be helpful to enhance the searching capability of DE algorithm. TSRVDE advancement is applied on the set of frequently used DE variants. To evaluate the performance of TSRVDE a test suit of comprehensive set of well-known multidimensional global optimizations problems is used. The acceleration of TSRVDE can be observed in the experimental results.

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“…[2,5,6], too. These algorithms are still improved, see [7,8]. They are based on two essential principles -crossing and mutation.…”

Section: Evolution Algorithmsmentioning

confidence: 99%

Solving Steel Alloying Using Differential Evolution and SOMA

Holiš

Skanderová

Placěk

et al. 2013

Computer Information Systems and Industrial Management

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Abstract. This paper proposes method for solving steel alloying problem using evolution algorithms SOMA and differential evolution. Both algorithms belong to the family of the evolution algorithms but the main ideas of these algorithms are different. In differential evolution new offspring is created during the evolution, the individuals are crossed and mutated, while in SOMA the individuals move in the space of the possible solutions and the mutation is replaced by perturbation. The main goal of this paper is to discover how much these algorithms are usable and suitable to solve the problem of steel alloying.

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A differential evolution algorithm with intersect mutation operator

Cited by 97 publications

References 21 publications

Fitness Proportionate Random Vector Selection based DE Algorithm (FPRVDE)

Fitness Proportionate Random Vector Selection based DE Algorithm (FPRVDE)

Tournament selection mechanism based random vector selection in differential evolution algorithm

Solving Steel Alloying Using Differential Evolution and SOMA

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