Central Force Optimization with variable initial probes and adaptive decision space

摘要

An implementation of Central Force Optimization (CFO) utilizing variable initial probes and decision space adaptation is presented. The algorithm is tested against a suite of benchmark functions and CFO’s results compared to those of other algorithms. CFO performs well against the benchmarks, and also in scalability tests in 300-dimensions.