A hybrid algorithm based on chicken swarm and improved raven roosting optimization

Abstract

One of the newest bio-inspired meta-heuristic algorithms is the chicken swarm optimization (CSO) algorithm. This algorithm is inspired by the hierarchical behavior of chickens in a swarm for finding food. The diverse movements of the chickens create a balance between the local and the global search for finding the optimal solution. Raven roosting optimization (RRO) algorithm is inspired by the social behavior of raven and the information flow between the members of the population with the goal of finding food. The advantage of this algorithm lies in using the individual perception mechanism in the process of searching the problem space. Premature convergence is one of the drawbacks of the algorithm that is analogous to the early convergence of the algorithm to an undesirable point. In the current work, a hybrid (IRRO–CSO) meta-heuristic approach based on the improved raven roosting optimization algorithm (IRRO) and the CSO algorithm is proposed. The CSO algorithm is used for its efficiency in satisfying the balance between the local and the global search, and IRRO algorithm is chosen for solving the problem of premature convergence and its better performance in bigger search spaces. The performance of the proposed hybrid IRRO–CSO algorithm is compared with other imitation-based swarm intelligence methods using benchmark functions (CEC2017). The obtained results from the implementation of the hybrid IRRO–CSO algorithm in MATLAB show an improvement in the average best fitness compared with the following algorithms: WOA, GWO, CSO, BAT and PSO. Due to avoiding the varying experimental results, the Friedman statistical test was applied. The presented combinatorial algorithm IRRO–CSO shows better results in comparison with the competitive algorithms after testing IRRO–CSO on 30 standard functions presented in CEC2017.