In this paper we present self-adaptive differential evolution algorithm jDElsgo on large scale global optimization. The experimental results obtained by our algorithm on benchmark functions provided for the CEC 2010 c...
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ISBN:
(纸本)9781424481262
In this paper we present self-adaptive differential evolution algorithm jDElsgo on large scale global optimization. The experimental results obtained by our algorithm on benchmark functions provided for the CEC 2010 competition and special session on Large Scale Global Optimization are presented. The experiments were performed on 20 benchmark functions with high dimension D = 1000. Obtained results show that our algorithm performs highly competitive in comparison with the DECC-G*, DECC-G and MLCC algorithms.
A new transient stability-constrained optimal power flow (TSCOPF) model for power system optimal operations is presented in this study. The proposed model is a non-linear, non-convex, discrete, and non-differentiable ...
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A new transient stability-constrained optimal power flow (TSCOPF) model for power system optimal operations is presented in this study. The proposed model is a non-linear, non-convex, discrete, and non-differentiable optimisation problem, and very difficult to be solved by classic programming algorithms. In this study, an effective optimisation framework is developed to address the TSCOPF problem. The self-adaptivedifferentialevolution (SaDE) algorithm is employed in the framework, which can effectively eliminate the needs for the strategy selection process and at the same time improve the robustness of the DE search process. The benchmark New England 10-generator, 39-bus system is used to verify the efficacy of this algorithm. The simulation result obtained using SaDE is compared with both conventional mathematical method and the recently published heuristic methods for OPF problems. The simulation results reveal that the SaDE optimisation technique outperforms other algorithms in both terms of robustness and solution quality.
In this paper, we propose a novel self-adaptive differential evolution algorithm (SaDE), where the choice of learning strategy and the two control parameters F and CR are not required to be pre-specified. During evolu...
详细信息
ISBN:
(纸本)0780393635
In this paper, we propose a novel self-adaptive differential evolution algorithm (SaDE), where the choice of learning strategy and the two control parameters F and CR are not required to be pre-specified. During evolution, the suitable learning strategy and parameter settings are gradually self-adapted according to the learning experience. The performance of the SaDE is reported on the set of 25 benchmark functions provided by CEC2005 special session on real parameter optimization.
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