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Constrained evolution algorithm based on adaptive differential evolution
International Journal of High Performance Computing and Networking, 2018Solving constrained optimisation is widely used in science and engineering, but the slow convergence speed and premature are the biggest problems researchers face. Research on a constrained evolution algorithm (CO-JADE) based on adaptive differential evolution (JADE) for solving the constrained optimisation problems is proposed in this paper. According
Kangshun Li +3 more
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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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HyGADE: Hybrid of Genetic Algorithm and Differential Evolution Algorithm
2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2019In the area of evolutionary algorithm, Genetic algorithm and Differential evolution algorithms are the most popular algorithms. Both algorithms used to determine different types of optimization problems. One of them is finding a solution which is close to global minima.
Damini Chaudhary +3 more
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Differential Evolution Algorithm for Motion Estimation
2011Motion Estimation (ME) is computationally expensive step in video encoding. Exhaustive search technique for ME yields maximum accuracy at the cost of highest execution time. To overcome the computational burden, many fast search algorithms are reported that limit the number of locations to be searched.
Samrat L. Sabat +2 more
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A Modified Binary Differential Evolution Algorithm
2010Differential evolution (DE) is a simple, yet efficient global optimization algorithm. As the standard DE and most of its variants operate in the continuous space, this paper presents a modified binary differential evolution algorithm (MBDE) to tackle the binary-coded optimization problems. A novel probability estimation operator inspired by the concept
Ling Wang 0009 +3 more
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Improved differential evolution algorithms
2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE), 2012Before improving the differential evolution (DE), the premature convergence feature of the differential evolution must be analyzed, which demonstrates that the differential evolution is not able to guarantee the global convergence. In order to improve the searching ability of differential evolution, two modified differential evolution are introduced by
Chengfo Sun, Haiyan Zhou, Liqing Chen
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Research on Biogeography Differential Evolution Algorithm
2012Biogeography-based optimization (BBO) is a population-based evolutionary algorithm (EA) that is based on the mathematics of biogeography. It mainly uses the biogeography-based migration operator to share the information among solutions. Differential Evolution (DE) is a fast and robust evolutionary algorithm for global optimization.
Hongwei Mo, Zhenzhen Li, Luolin Zhang
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Interval Differential Evolution Algorithm
2020By introducing the interval model into the existing differential evolution, this chapter proposes a novel interval differential evolution algorithm, which can directly solve the original interval optimization problem rather than transforming it to a deterministic optimization problem first.
Chao Jiang, Xu Han, Huichao Xie
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Online algorithm configuration for differential evolution algorithm
Applied Intelligence, 2022Changwu Huang, Hao Bai, Xin Yao 0001
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Improved differential evolution algorithm
2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), 2017Differential Evolution (DE) is an evolutionary approach to unravel complex optimization problems. The DE is a straight forward and very popular population based stochastic Algorithm. DE outperformed other competitive evolutionary algorithms when measured over benchmark problem as well as actual optimization problems in terms of performance.
Sanjay Jain +3 more
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