Opposition-Based Artificial Bee Colony with Dynamic Cauchy Mutation for Function Optimization

This paper presents a new Artificial Bee Colony (ABC) optimization algorithm to solve function optimization problems. The proposed approach is called OCABC, which introduces opposition-based learning concept and dynamic Cauchy mutation into the standard ABC algorithm. To verify the performance of OCABC, eight well-known benchmark function optimization problems are used in the experiments. Experimental results show that our approach outperforms the original ABC, Particle Swarm Optimization (PSO) and opposition-based PSO for the majority of test functions.