Are Near Earth Objects the Key to Optimization Theory

@article{Formato2009AreNE,
  title={Are Near Earth Objects the Key to Optimization Theory},
  author={Richard A. Formato},
  journal={arXiv: Earth and Planetary Astrophysics},
  year={2009}
}
  • R. Formato
  • Published 8 December 2009
  • Physics
  • arXiv: Earth and Planetary Astrophysics
This note suggests that near earth objects and Central Force Optimization have something in common, that NEO theory may hold the key to solving some vexing problems in deterministic optimization: local trapping and proof of convergence. CFO analogizes Newton's laws to locate the global maxima of a function. The NEO-CFO nexus is the striking similarity between CFO's Davg and an NEO's Delta-V curves. Both exhibit oscillatory plateau-like regions connected by jumps, suggesting that CFO's… 
Parameter-Free Deterministic Global Search with Central Force Optimization
TLDR
A parameter-free implementation of Central Force Optimization for deterministic multidimensional search and optimization with hardwired internal parameters so that none is user-specified.
On the Utility of Directional Information for Repositioning Errant Probes in Central Force Optimization
TLDR
This note investigates the effect of adding directionality to the "repositioning factor" approach, and it appears that doing so does not improve convergence speed or accuracy.
A Convergence Proof and Parameter Analysis of Central Force
TLDR
A convergence proof of CFO on the stability theory of discrete-time-linear system reveals the necessary convergent conditions and a qualitative parameter analysis and a general strategy of selecting parameters of C FO are presented.
Parameter-Free Deterministic Global Search with Simplified Central Force Optimization
This note describes a simplified parameter-free implementation of Central Force Optimization for use in deterministic multidimensional search and optimization. The user supplies only the objective
New Techniques for Increasing Antenna Bandwidth with Impedance Loading
New methods are presented for increasing the bandwidth of wire antennas using impedance loading. This paper extends the seminal Wu-King theory of the internal impedance proflle that produces
Improved Cfo Algorithm for Antenna Optimization
TLDR
An improved Central Force Optimization algorithm for antenna optimization is presented and exhibits excellent performance against recognized antenna benchmark problem specifically designed to evaluate optimization evolutionary algorithms for antenna applications.
Central Force Optimization with variable initial probes and adaptive decision space

References

SHOWING 1-2 OF 2 REFERENCES
Threat Characterization: Trajectory Dynamics
TLDR
Critical factors not normally considered must be brought into play as one characterizes the threat of NEO impacts.
Nature Inspired Cooperative Strategies for Optimization, NICSO 2010, May 12-14, 2010, Granada, Spain
TLDR
The contributions collected in this book cover topics including nature-inspired techniques like Genetic Al algorithms, Evolutionary Algorithms, Ant and Bee Colonies, Swarm Intelligence approaches, Neural Networks, several Cooperation Models, Structures and Strategies, Agents Models, Social Interactions, as well as new algorithms based on the behaviour of fireflies or bats.