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An adaptive differential evolution algorithm
2011 IEEE Congress of Evolutionary Computation (CEC), 2011The performance of Differential Evolution (DE) algorithm is significantly affected by its parameter setting. But the choice of parameters is heavily dependent on the problem characteristics. Therefore, recently a couple of adaptation schemes that automatically adjust DE parameters have been proposed.
Nasimul Noman +2 more
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Cellular Differential Evolution Algorithm
2010This paper presents a cellular version of Differential Evolution (DE) algorithm. The notion behind the geographical distribution of DE population with local interaction is to study the influence of slow diffusion of information throughout the population.
Nasimul Noman, Hitoshi Iba
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Chaotic Immune Differential Evolution Algorithm
2007 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2007A novel chaotic immune differential evolution algorithm (CIDE) is presented. In CIDE, weighted difference is added to the best individual. Using randomness and space ergodicity of chaotic mapping, the best individual is processed by chaotic immune clone operation; In each iteration process, the weighting factor is changed dynamically based on the ...
Zhenyu Guo, Zhifeng Bai, Binggang Cao
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Multi-search differential evolution algorithm
Applied Intelligence, 2017The differential evolution algorithm (DE) has been shown to be a very simple and effective evolutionary algorithm. Recently, DE has been successfully used for the numerical optimization. In this paper, first, based on the fitness value of each individual, the population is partitioned into three subpopulations with different size.
Xiangtao Li, Shijing Ma, Jiehua Hu
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Parameter Selection of Differential Evolution by another Differential Evolution Algorithm
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019The performance of differential evolution (DE) highly depends on its control parameters, especially for the first proposed simple or standard DE. Control parameters suitable for one objective function are generally not beneficial to another. To automatically find out optimal control parameter settings for different objective functions, we propose a ...
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On the rotational variance of the differential evolution algorithm
Advances in Engineering Software, 2019Abstract In this study we examine the rotational (in)variance of the differential evolution (DE) algorithm. We show that the classic DE/rand/1/bin algorithm, which uses constant mutation and standard crossover, is rotationally variant. We then study a previously proposed rotationally invariant formulation in which the crossover operation takes place ...
Marthinus N. Ras +3 more
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Differential Evolution Algorithms with Cellular Populations
2010Differential Evolution (DE) algorithms are efficient Evolutionary Algorithms (EAs) for the continuous optimization domain. There exist a large number of DE variants in the literature. In this paper, we analyze the effect of adding a cellular structure to the population of some of the most outstanding existing ones.
Bernabé Dorronsoro, Pascal Bouvry
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Realization of the differential evolution algorithm on FPGA
2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014In this work, a hardware implementation of differential evolution algorithm (DEA) on field-programmable gate array (FPGA) is realized. The implementation of DEA on FPGA is carried out to find minimum value of an equation with two unknowns. This study is a preparatory work for the training of neural networks with DEA on FPGA which is more complicated.
Ali Riza Yilmaz +2 more
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A Discrete Differential Evolution Algorithm for Carpooling
2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 2018Carpooling is an effective transport model that can significantly reduce transportation costs. The problem to match passengers with drivers is a difficult problem due to complex constraints to be satisfied in the solution processes. The goals of this paper are to propose a model and a solution methodology that is seamlessly integrated with existing ...
Fu-Shiung Hsieh, Fu-Min Zhan
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The Modified Differential Evolution Algorithm (MDEA)
2012Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms. DE has drawn the attention of many researchers resulting in a lot of variants of the classical algorithm with improved performance. This paper presents a new modified differential evolution algorithm for minimizing continuous space.
Fatemeh Ramezani, Shahriar Lotfi
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