A New Framework for Optimization Based-On Hybrid Swarm Intelligence

Abstract

A hybrid optimization algorithm based on Cat Swarm Optimization (CSO) and Artificial Bee Colony (ABC) is proposed in this chapter. CSO is an optimization algorithm designed to solve numerical optimization problems, and ABC is an optimization algorithm generated by simulating the behavior of bees finding foods. By hybridizing these two algorithms, the hybrid algorithm called Hybrid PCSOABC is presented. Five benchmark functions are used to evaluate the accuracy, convergence, the speed, and the stabilization of the Hybrid PCSOABC. In this chapter, the literature review regarding CSO, AS, ACS, BF, PSO, ABC, and the parallel version of CSO are given at the beginning. The proposed hybrid framework combining different algorithms is given in the fourth section. And the experimental results are presented at the end of the chapter with the conclusions.