Central force optimisation: a new gradient-like metaheuristic for multidimensional search and optimisation

@article{Formato2009CentralFO,
  title={Central force optimisation: a new gradient-like metaheuristic for multidimensional search and optimisation},
  author={Richard A. Formato},
  journal={Int. J. Bio Inspired Comput.},
  year={2009},
  volume={1},
  pages={217-238}
}
  • R. Formato
  • Published 1 April 2009
  • Business
  • Int. J. Bio Inspired Comput.
This paper introduces central force optimisation, a novel, nature-inspired, deterministic search metaheuristic for constrained multidimensional optimisation in highly multimodal, smooth, or discontinuous decision spaces. CFO is based on the metaphor of gravitational kinematics. The algorithm searches a decision space by 'flying' its 'probes' through the space by analogy to masses moving through physical space under the influence of gravity. Equations are developed for the probes' positions and… 
Central force optimization: A new deterministic gradient-like optimization metaheuristic
TLDR
The possibility of creating a new “hyperspace directional derivative” using the Unit Step function to create positive-definite “masses” in “CFO space” is suggested, suggesting that CFO merits further investigation.
Central Force Optimization Applied to the PBM Suite of Antenna Benchmarks
TLDR
CFO's gradient-like nature is discussed, and it is speculated that a "generalized hyperspace derivative" might be defined for optimization problems as a new mathematical construct based on the Unit Step function.
Central force metaheuristic optimisation
Central Force Optimisation (CFO) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the in uence of
Central force optimization on a GPU: a case study in high performance metaheuristics
TLDR
This study presents the first parallel implementation of CFO on a Graphics Processing Unit (GPU) using the NVIDIA Compute Unified Device Architecture (CUDA) and demonstrates substantial speedups depending on problem size and complexity.
Central Force Optimization on a GPU: A case study in high performance metaheuristics using multiple topologies
TLDR
This work has implemented the concept of local neighborhoods and implemented CFO on a Graphics Processing Unit (GPU) using the NVIDIA Compute Unified Device Architecture (CUDA) extensions for C/C++ to decrease CFO's computational time.
Neighborhood topologies in central force optimization
  • Robert C. Green
  • Computer Science
    2017 IEEE Symposium Series on Computational Intelligence (SSCI)
  • 2017
TLDR
Use of different neighborhood topologies on the computation time, functional evaluations, probe corrections, solution quality, convergence, and parameter selection of the CFO algorithm produces results that are very similar to those of the Standard or Fully Connected topology commonly used with CFO.
MICROSTRIP PATCH ANTENNA OPTIMIZATION USING MODIFIED CENTRAL FORCE OPTIMIZATION
TLDR
A new scheme, the acceleration clipping, is introduced, which enhances CFO's global search ability while maintaining its simplicity, and is applied to the optimal design of two difierent wideband microstrip patch antennas.
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Central Force Optimization (CFO) is a new deterministic multi-dimensional search metaheuristic based on the metaphor of gravitational kinematics. It models “probes” that “fly” through the decision
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