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An Improved Differential Evolution with a Novel Restart Mechanism
2016 12th International Conference on Computational Intelligence and Security (CIS), 2016This paper presents a novel differential evolution (DE) algorithm for solving global optimization problems by developing a combined local mutation strategy and a restart mechanism. To alleviate the premature convergence and stagnation, a combined local mutation strategy is first developed to improve the exploitation of DE by using two local mutation ...
Mengnan Tian +2 more
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An Improved Differential Evolution for Multi-objective Optimization
2009 WRI World Congress on Computer Science and Information Engineering, 2009Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper proposes an improved differential evolution algorithm (CDE). On the one hand CDE combines the advantages
Ke Li 0001 +3 more
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A new differential evolution with improved mutation strategy
IEEE Congress on Evolutionary Computation, 2010The paper employs Lagrange's mean value theorem of differential Calculus to design a new strategy for the selection of parameter vectors in the Differential Evolution (DE) algorithm. Classical differential evolution selects parameter vectors randomly to obtain the donor vectors.
Pavel Bhowmik +4 more
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Differential Evolution with Improved Mutation Strategy
2011Differential evolution is a powerful evolution algorithm for optimization of real valued and multimodal functions. To accelerate its convergence rate and enhance its performance, this paper introduces a top-p-best trigonometric mutation strategy and a self-adaptation method for controlling the crossover rate (CR).
Shuzhen Wan +3 more
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Improving differential evolution with impulsive control framework
2015 IEEE Congress on Evolutionary Computation (CEC), 2015Differential evolution (DE) is a simple but powerful evolutionary algorithm, which has been widely and successfully used in many areas. In this paper, an impulsive control method is introduced to the DE framework, and the impulsive DE (IpDE) is proposed for improving the performance of DE. The impulsive control operation instantly moves the individuals
Wei Du 0003 +3 more
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An improved multi-population ensemble differential evolution
Neurocomputing, 2018Abstract Differential evolution (DE) is a population-based stochastic optimization technique that can be applied to solve global optimization problems. The selected mutation strategies and the control parameters can affect the performance of DE.
Lyuyang Tong, Minggang Dong, Chao Jing
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Improving the Convergence of Differential Evolution
2017A new variant of differential evolution (DE) algorithm with a selection of mutation strategy based on the mutant point distance (DEMD) is proposed. Three DEMD variants are compared with state-of-the-art DE variants on CEC 2015 problems at four dimension levels.
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Improved Differential Evolution with Dynamic Population Size
2006As a novel evolutionary computing technique, recently Differential Evolution (DE) has attracted much attention and wide applications due to its simple concept and easy implementation. However, all the control parameters of the classic DE (crossover rate, scaling factor, and population size) keep fixed during the searching process.
Fuzhuo Huang +2 more
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Self-adaptive improved differential evolution algorithm
2010 Sixth International Conference on Natural Computation, 2010A new self-adaptive improved differential evolution algorithm is presented. In order to improve the population's diversity and the ability of breaking away from the local optimum, according to the value of the variance of the population's fitness during the evolution process, a new mutation operator is adapted to mutate the population.
Liangdong Qu +2 more
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Improved differential evolution for dynamic optimization problems
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008This article reports improvements on DynDE, a approach to using Differential Evolution to solve dynamic optimization problems. Three improvements are suggested, namely favored populations, migrating individuals and a combination of these approaches. The effects of varying the change frequency, peak widths and the number of dimensions of the dynamic ...
Mathys C. du Plessis +1 more
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