Self-adaptive Hybrid differential evolution with simulated annealing algorithm for numerical optimization

A self-adaptive hybrid differential evolution with simulated annealing algorithm, termed SaDESA, is proposed. In the novel SaDESA, the choice of learning strategy and several critical control parameters are not required to be pre-specified. During evolution, the suitable learning strategy and parame...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Zhong-bo Hu, Qing-hua Su, Sheng-wu Xiong, Fu-gao Hu
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Accuracy Algorithm design and analysis Benchmark testing Chromium Evolution (biology) Optimization Simulated annealing
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