A novel hybrid pelican-particle swarm optimization algorithm (HPPSO) for global optimization problem

Particle Swarm Optimization (PSO) has drawn attention due to its widespread use in scientific and engineering fields. However, it suffers from a major limitation which is its slow exploration capability leading to stagnation. To overcome this limitation, various algorithms have been hybridized to improve the exploration phase of PSO but still there is a need to improve it further. Keeping this in mind, this paper proposes a novel hybrid meta-heuristic algorithm called the Hybrid Pelican-Particle Swarm Optimization (HPPSO) for solving complex optimization problems. The purpose of hybridization is motivated by the excellent exploration capability of the Pelican Optimization Algorithm (POA). The performance of the proposed HPPSO has been tested on 33 standard benchmark functions in MATLAB (R2023a). For evaluation, the obtained results of proposed HPPSO algorithm are compared with conventional PSO and POA along with other numerous hybridized algorithms of PSO (PSOGSA, HFPSO, PSOBOA, and PSOGWO). The results are analyzed statistically through convergence curves, boxplot and a non-parametric Wilcoxon signed rank test. These analyses show that the proposed HPPSO algorithm achieves a better optimum than other algorithms used in the present paper.