A novel numerical function optimization framework: campaign based optimization framework

Abstract

Abstract This paper proposes a new optimization framework: campaign optimization framework (COF), which is based on the historical results of traditional swarm intelligence algorithms and uses two different campaign rules to get better solutions. Firstly, the candidate solution set is generated by running the swarm optimization algorithm. Secondly, cross campaign rule (CCR) or increment campaign rule (ICR) is used to update the candidate solution set. The campaign rule describes the steps to get better solutions. In CCR, the better solution is obtained by combining the variable bits of two randomly selected solutions. But in ICR, it is generated by every variable bits’ optimal pools. Then, the best candidate solution is optimized by swarm optimization algorithm again. Finally, the proposed framework is tested on two well-known benchmark functions. Through the analysis of the experimental results, the proposed algorithm is compared with traditional swarm optimization algorithm. And two campaign rules are compared in terms of results and optimization time dimensions.