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Opposition-Based Differential Evolution Algorithms
2006 IEEE International Conference on Evolutionary Computation, 2006Evolutionary Algorithms (EAs) are well-known optimization approaches to cope with non-linear, complex problems. These population-based algorithms, however, suffer from a general weakness; they are computationally expensive due to slow nature of the evolutionary process.
Shahryar Rahnamayan +2 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 Differential Evolution Algorithm for Contrast Optimization
2020Image Enhancement is one of the most important phases of the image processing system. Contrast Enhancement plays a key role in this step. Histogram Equalization (HE) is one of the main tools used to improve the contrast of an image. However, the use of HE causes an increase in the natural brightness of the image, which is not desirable in many types of
Artur Leandro da Costa Oliveira +1 more
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An enhanced differential evolution optimization algorithm
2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP), 2014The Differential Evolution (DE) algorithm, introduced by Storn and Price in 1995, has become one of the most efficacious population-based optimization approaches. In this algorithm, use is made of the significant concepts of mutation, crossover, and selection.
M. Arafa, Elsayed A. Sallam, M. M. Fahmy
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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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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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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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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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Online algorithm configuration for differential evolution algorithm
Applied Intelligence, 2022Changwu Huang, Hao Bai, Xin Yao 0001
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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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