An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm

• New mechanisms of Swallow Swarm Optimization (SSO) are transplanted into Particle Swarm Optimization algorithm.
• Application of the developed algorithm produces a good balance between global and local exploration abilities.
• The algorithm is validated with 11 functions and 5 engineering optimization trusses.

In this article, search mechanisms of Swallow Swarm Optimization (SSO) are implemented in the framework of Particle Swarm Optimization (PSO) to form the Hybrid Particle Swallow Swarm Optimization (HPSSO) algorithm. The new algorithm is tested by solving eleven mathematical optimization problems and six truss weight minimization problems. HPSSO is compared to the standard PSO and some of its advanced variants. Optimization results demonstrate the efficiency of the proposed algorithm that outperforms the PSO variants taken as basis of comparison and is very competitive with other state-of-the-art metaheuristic optimization methods. Here, a good balance between global and local searches is achieved.