大挤压
嚼
数学优化
大爆炸(金融市场)
计算机科学
人口
随机性
水准点(测量)
宇宙的最终命运
大数据
算法
数学
宇宙
统计
物理
宇宙論
数据挖掘
经济
财务
物理疗法
社会学
人口学
医学
天体物理学
稳态理论
德西特宇宙
地理
大地测量学
作者
Osman Kaan Erol,İbrahim Eksin
标识
DOI:10.1016/j.advengsoft.2005.04.005
摘要
Nature is the principal source for proposing new optimization methods such as genetic algorithms (GA) and simulated annealing (SA) methods. All traditional evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. The main contribution of this study is that it proposes a novel optimization method that relies on one of the theories of the evolution of the universe; namely, the Big Bang and Big Crunch Theory. In the Big Bang phase, energy dissipation produces disorder and randomness is the main feature of this phase; whereas, in the Big Crunch phase, randomly distributed particles are drawn into an order. Inspired by this theory, an optimization algorithm is constructed, which will be called the Big Bang–Big Crunch (BB–BC) method that generates random points in the Big Bang phase and shrinks those points to a single representative point via a center of mass or minimal cost approach in the Big Crunch phase. It is shown that the performance of the new (BB–BC) method demonstrates superiority over an improved and enhanced genetic search algorithm also developed by the authors of this study, and outperforms the classical genetic algorithm (GA) for many benchmark test functions.
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