Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications

Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) is introduced. The main idea in designing the proposed POA is simulation of the natural behavior of pelicans during hunting. In POA, search agents are pelicans that search for food sources. The mathematical model of the POA is presented for use in solving optimization issues. The performance of POA is evaluated on twenty-three objective functions of different unimodal and multimodal types. The optimization results of unimodal functions show the high exploitation ability of POA to approach the optimal solution while the optimization results of multimodal functions indicate the high ability of POA exploration to find the main optimal area of the search space. Moreover, four engineering design issues are employed for estimating the efficacy of the POA in optimizing real-world applications. The findings of POA are compared with eight well-known metaheuristic algorithms to assess its competence in optimization. The simulation results and their analysis show that POA has a better and more competitive performance via striking a proportional balance between exploration and exploitation compared to eight competitor algorithms in providing optimal solutions for optimization problems.

Automated summary

This paper introduces a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) to solve optimization problems in various scientific disciplines. By simulating the natural behavior of pelicans during hunting, the POA demonstrates high performance in approaching optimal solutions for unimodal functions and exploring the main optimal area for multimodal functions. Comparison with eight well-known metaheuristic algorithms confirms the competitiveness of POA in providing optimal solutions for optimization problems.