A Hybrid Swarm Intelligence Optimization for Benchmark Models by Blending PSO with ABC

In swarm intelligence PSO is not so popular algorithm but ABC is recently developed and most popular whereas PSO is lagging in finding global solutions however the ABC’s neighborhood search is not sufficient to accelerate the convergence rate. The hybrid technique is developed in such a way that it can solve the issues arising in individual PSO and ABC. As ABC outperforms in the most problems, it will be selected as the primary algorithm and the swarming behavior of the particles are included in the bees. A compromising neighborhood search model is developed for ABC to aid accelerated neighborhood search by considering the property of PSO’s particles updating behavior along with the ABC’s neighborhood search. The introduction of such neighborhood search model fine tunes the neighborhood search property of employed and onlooker bees that helps to converge faster than conventional ABC and PSO. The tests will be carried out using standard benchmark test function models and the performance will be compared against the individual PSO and ABC algorithms.