JADE: Adaptive Differential Evolution With Optional External Archive
JADE: Adaptive Differential Evolution With Optional External Archive
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DOI：
10.1109/tevc.2009.2014613
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发表时间：
2009-10-01
影响因子：
14.3
通讯作者：
Sanderson, Arthur C.
中科院分区：
文献类型：
--
作者：
Zhang, Jingqiao;Sanderson, Arthur C.
提出了一种新的差异演化（DE）算法JADE，以通过可选的外部存档和以适应性方式更新控制参数来提高优化性能。 DE/Current-pert-Pbest是对经典“ DE/Current-toes-test”的概括，而可选的档案操作则利用历史数据来提供进度方向的信息。两项运营都使人口多样化并改善了融合绩效。参数适应会自动将控制参数更新为适当的值，并避免用户对参数设置之间的关系和优化问题特征之间的关系的知识。因此，提高算法的鲁棒性是有帮助的。仿真结果表明，Jade比其他经典或适应性的DE算法更好，或至少与其他经典或适应性的DE算法，规范的粒子群优化和其他来自文献的进化算法在20个基准问题的融合性能方面。带有外部档案的翡翠在相对较高的维度问题上显示出令人鼓舞的结果。此外，它清楚地表明，没有适用于各种问题的固定控制参数设置，甚至在单个问题的不同优化阶段。
A new differential evolution (DE) algorithm, JADE, is proposed to improve optimization performance by implementing a new mutation strategy "DE/current-to-pbest" with optional external archive and updating control parameters in an adaptive manner. The DE/current-to-pbest is a generalization of the classic "DE/current-to-best," while the optional archive operation utilizes historical data to provide information of progress direction. Both operations diversify the population and improve the convergence performance. The parameter adaptation automatically updates the control parameters to appropriate values and avoids a user's prior knowledge of the relationship between the parameter settings and the characteristics of optimization problems. It is thus helpful to improve the robustness of the algorithm. Simulation results show that JADE is better than, or at least comparable to, other classic or adaptive DE algorithms, the canonical particle swarm optimization, and other evolutionary algorithms from the literature in terms of convergence performance for a set of 20 benchmark problems. JADE with an external archive shows promising results for relatively high dimensional problems. In addition, it clearly shows that there is no fixed control parameter setting suitable for various problems or even at different optimization stages of a single problem.