Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
Parameter-free deterministic global search with simplified central force optimization
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Expert Systems with Applications: An International Journal
Central force optimization on a GPU: a case study in high performance metaheuristics
The Journal of Supercomputing
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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 accelerations using the gravitational metaphor. Small objects in our universe can become trapped in close orbits around highly gravitating masses. In 'CFO space' probes are attracted to 'masses' created by a user-defined function of the value of an objective function to be maximised. CFO may be thought of in terms of a vector 'force field' or, loosely, as a 'generalised gradient' methodology because the force of gravity can be computed as the gradient of a scalar potential. The CFO algorithm is simple and easily implemented in a compact computer program. Its effectiveness is demonstrated by running CFO against several widely used benchmark functions. The algorithm exhibits very good performance, suggesting that it merits further study.