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}
}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…
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32 Citations
Central force optimization: A new deterministic gradient-like optimization metaheuristic
- Computer Science
- 2009
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
- PhysicsArXiv
- 2010
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
- Business
- 2015
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…
Convergence analysis and performance of an extended central force optimization algorithm
- Computer ScienceAppl. Math. Comput.
- 2012
A multi-start central force optimization for global optimization
- Computer ScienceAppl. Soft Comput.
- 2015
Central force optimization on a GPU: a case study in high performance metaheuristics
- Computer Science2011 IEEE Congress of Evolutionary Computation (CEC)
- 2011
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.
Training neural networks using Central Force Optimization and Particle Swarm Optimization: Insights and comparisons
- Computer ScienceExpert Syst. Appl.
- 2012
Central Force Optimization on a GPU: A case study in high performance metaheuristics using multiple topologies
- Computer ScienceIEEE Congress on Evolutionary Computation
- 2011
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
- Computer Science2017 IEEE Symposium Series on Computational Intelligence (SSCI)
- 2017
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
- Computer Science, BusinessProgress In Electromagnetics Research B
- 2010
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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