Neighborhood topologies in central force optimization

Central Force Optimization (CFO) is a population based metaheuristic algorithm that has been demonstrated to be competitive with other metaheuristic algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Group Search Optimization (GSO). While CFO has often shown superiority in terms of functional evaluations and solution quality, the algorithm is complex and often requires increased computation time in order to achieve convergence. Previous studies have shown that this increased computation time is due to most complex step of the algorithm - updating the acceleration of the probes in the population. In order to reduce the computation time required by CFO while simultaneously maintaining the quality results produced by the algorithm, this study focuses on the impact of different neighborhood topologies on the computation time, functional evaluations, probe corrections, solution quality, convergence, and parameter selection of the CFO algorithm. Results demonstrate that the use of these topologies results in a CFO algorithm that requires 80% - 90% less computation time to converge, averages a 66% reduction in probe corrections, and generally reduces the number of required functional evaluations while continuing to experience convergence characteristics and produce results that are very similar to those of the Standard or Fully Connected topology commonly used with CFO.