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

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Nature-Inspired Computing

Background:

• Optimization is a fundamental challenge across diverse scientific disciplines.
• Existing metaheuristic algorithms often struggle to balance exploration and exploitation effectively.
• Nature-inspired algorithms offer promising approaches for complex problem-solving.

Purpose of the Study:

• Introduce a novel stochastic nature-inspired optimization algorithm: the Pelican Optimization Algorithm (POA).
• Simulate pelican hunting behavior to develop a new optimization strategy.
• Evaluate POA's performance on benchmark functions and real-world engineering problems.

Main Methods:

• Developed a mathematical model for the Pelican Optimization Algorithm (POA) based on pelican foraging behavior.
• Tested POA on twenty-three diverse unimodal and multimodal objective functions.
• Assessed POA's efficacy on four engineering design optimization problems.
• Compared POA's performance against eight established metaheuristic algorithms.

Main Results:

• POA demonstrated strong exploitation capabilities on unimodal functions, efficiently converging to optimal solutions.
• POA exhibited excellent exploration capabilities on multimodal functions, effectively identifying global search space optima.
• POA achieved competitive and superior performance compared to eight other algorithms in solving engineering design problems.

Conclusions:

• The Pelican Optimization Algorithm (POA) effectively balances exploration and exploitation for robust optimization.
• POA presents a promising and competitive alternative for addressing complex optimization challenges in science and engineering.
• The algorithm's nature-inspired approach offers a novel strategy for metaheuristic optimization.