随机最优化的Eagle策略及其Lévy Walk和Firefly算法
Eagle Strategy Using Lévy Walk and Firefly Algorithms For Stochastic Optimization
摘要：
大多数全局最优化问题是非线性的，因此难以求解，并且当目标函数和约束中存在不确定性时，它们变得更具挑战性。本文提出了一种新的求解随机最优化的两阶段混合搜索法--Eagle策略。该策略旨在以迭代方式将使用Lévy行走的随机搜索与萤火虫算法相结合。数值研究和结果表明，所提出的鹰策略是非常有效的随机最优化。最后将讨论实际意义和进一步研究的潜在课题。
英文摘要：
Most global optimization problems are nonlinear and thus difficult to solve, and they become even more challenging when uncertainties are present in objective functions and constraints. This paper provides a new two-stage hybrid search method, called Eagle Strategy, for stochastic optimization. This strategy intends to combine the random search using Lévy walk with the firefly algorithm in an iterative manner. Numerical studies and results suggest that the proposed Eagle Strategy is very efficient for stochastic optimization. Finally practical implications and potential topics for further research will be discussed.
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