Homotopy-lnspired Cat Swarm Algorithm for Global Optimization

摘要

Based on the concepts of homotopy, a novel cat swarm algorithm, called ahomotopy-inspired cat swarm algorithm (HCSA),is proposed to deal with the problem of globaloptimization. Proceeding from dependent variables of optimized function,it traces a path from thesolution of an easy problem to the solution of the given one by use of a homotopy- |a continuoustransformation from the easy problem to the given one.This novel strategy enables the cat swarmalgorithm (CSA) to improve the search efficiency. Theoretical analysis proves that HCSA convergesto the global optimum. Experimenting with a wide range of benchmark functions, we show that theproposed new version of CSA, with the continuous transformation, performs better, or at leastcomparably, to classic CSA.