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On the improvement of opposition-based differential evolution
2010 Sixth International Conference on Natural Computation, 2010Opposition-based Learning (OBL) is a new concept in machine intelligence, and has been proven to be an effective method to Differential Evolution (DE), Particle Swarm Optimization (PSO) and other population-based algorithms in solving many optimization problems.
Jun Tang, Xiaojuan Zhao
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An improved differential evolution for parameter optimisation
International Journal of Wireless and Mobile Computing, 2015In this paper, we propose an improved differential evolution DE to solve parameter optimisation problems. The new approach is called ICBBDE, which is an enhanced version of bare bones DE BBDE. The ICBBDE employs an adaptive strategy to dynamically adjust the crossover rate.
Xiuqin Pan, Ruixiang Li
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Improved differential evolution for noisy optimization
Swarm and Evolutionary Computation, 2020Abstract A novel approach is proposed in this paper to improve the optimization proficiency of the differential evolution (DE) algorithm in the presence of stochastic noise in the objective surface by utilizing the composite benefit of four strategies.
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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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An Improved Trigonometric Differential Evolution
International Journal of Advancements in Computing Technology, 2011Differential evolution is an efficient and powerful population-based stochastic technique capable of handling non-differentiable, non-linear and multi-modal objective functions. In order to improve its performance, this paper introduces a best-trigonometric mutation strategy and applies a crossover rate update strategy to the proposed algorithm.
Shuzhen Wan - +2 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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An Improved Differential Evolution with Efficient Parameters Adjustment
2013 First International Symposium on Computing and Networking, 2013Due to the real-world optimization problems have grown ever more complex. Solution solving capability and efficiency of optimization algorithms are confronted with serious challenge. In this paper, an Efficient Parameters Adjustment is proposed for Differential Evolution to solve optimization problems.
Sheng-Ta Hsieh, Tse Su, Huang-Lyu Wu
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Optimization of Wave Energy Converter Arrays by an Improved Differential Evolution Algorithm
Energies, 2018 Since different incident waves will cause the same array to perform differently with respect to the wave energy converter (WEC), the parameters of the incident wave, including the incident angle and the incident wave number, are taken into account for ...
Hongwei Fang, Guo-Ping Li
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Improved Differential Evolution for Function Optimization
2010 International Conference on Machine Vision and Human-machine Interface, 2010This paper presents an improved differential evolution (DE) algorithm to enhance the performance of DE. The proposed approach is called MPTDE which employs a novel mutation operator. The main idea of MPTDE is to conduct a mutation on each individual and select a fitter one between the current one and the mutated one as the new current individual.
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Improving the Performance and Scalability of Differential Evolution
2008Differential Evolution (DE) is a powerful optimization procedure that self-adapts to the search space, although DE lacks diversity and sufficient bias in the mutation step to make efficient progress on non-separable problems. We present an enhancement to Differential Evolution that introduces greater diversity.
Antony W. Iorio, Xiaodong Li 0001
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