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

被引：81

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.

引用

页码：25 / 51

页数：26

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