Results 241 to 250 of about 442,456 (299)

Exploited Differential Evolution Algorithm

2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015
Evolutionary algorithms are efficient algorithms for solving the most complex optimization problems of the current era. Differential Evolution (DE) is a simple population based evolutionary algorithm under this category. As shown in literature, c omparative to exploration of the search space, DE is less capable of exploiting the existing solutions ...
Aakanksha Bhatnagar, Kavita Sharma, Manoj Singh   +2 more
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A fuzzy adaptive differential evolution algorithm

2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM '02. Proceedings., 2004
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Liu, J., Lampinen, J.
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Rethinking the differential evolution algorithm

Service Oriented Computing and Applications, 2020
Selection operation plays a significant role in differential evolution algorithm. A new differential evolution algorithm based on an improved selection process is presented in this work. It was studied that there was neither a practical method to maintain the distribution of population nor a correction to the variables out of bounds in mutation process
Hongwei Liu, Xiang Li, Wenyin Gong
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Interval Differential Evolution Algorithm

2020
By 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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Chaotic Immune Differential Evolution Algorithm

2007 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2007
A 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 ...
null Guo Zhenyu, null Bai Zhifeng, null Cao Binggang   +2 more
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THE PARETO DIFFERENTIAL EVOLUTION ALGORITHM

International Journal on Artificial Intelligence Tools, 2002
The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto-optimal solutions in a single run.
H. A. ABBASS, R. SARKER
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Opposition-Based Differential Evolution Algorithms

2006 IEEE International Conference on Evolutionary Computation, 2006
Evolutionary 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.
S. Rahnamayan, H.R. Tizhoosh, M.M.A. Salama   +2 more
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Cellular Differential Evolution Algorithm

2010
This 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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Multi-search differential evolution algorithm

Applied Intelligence, 2017
The 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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An adaptive differential evolution algorithm

2011 IEEE Congress of Evolutionary Computation (CEC), 2011
The 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, Danushka Bollegala, Hitoshi Iba   +2 more
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