Central force optimization: A new deterministic gradient-like optimization metaheuristic

被引：77
作者：
Formato R.A. [1 ]
机构：
[1] Registered Patent Attorney and Consulting Engineer, Harwich, MA 02645
来源：
OPSEARCH | 2009年 / 46卷 / 1期
关键词：
Central force optimization; CFO; Metaheuristic; Multidimensional search; Deterministic; Gradient-like; Optimization;
D O I：
10.1007/s12597-009-0003-4
中图分类号：
学科分类号：
摘要：
This paper introduces central force optimization as a new, nature-inspired metaheuristic for multidimensional search and optimization based on the metaphor of gravitational kinematics. CFO is a "gradient-like" deterministic algorithm that explores a decision space by "fl ying" a group of "probes" whose trajectories are governed by equations analogous to the equations of gravitational motion in the physical universe. This paper suggests the possibility of creating a new "hyperspace directional derivative" using the Unit Step function to create positive-defi nite "masses" in "CFO space." A simple CFO implementation is tested against several recognized benchmark functions with excellent results, suggesting that CFO merits further investigation. © Operational Research Society of India.
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收藏
页码：25 / 51
页数：26
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