An efficient multi-objective parrot optimizer for global and engineering optimization problems

The Parrot Optimizer (PO) has recently emerged as a powerful algorithm for single-objective optimization, known for its strong global search capabilities. This study extends PO into the Multi-Objective Parrot Optimizer (MOPO), tailored for multi-objective optimization (MOO) problems. MOPO integrates an outward archive to preserve Pareto optimal solutions, inspired by the search behavior of Pyrrhura Molinae parrots. Its performance is validated on the Congress on Evolutionary Computation 2020 (CEC'2020) multi-objective benchmark suite. Additionally, extensive testing on four constrained engineering design challenges and eight popular confined and unconstrained test cases proves MOPO's superiority. Moreover, the real-world multi-objective optimization of helical coil springs for automotive applications is conducted to depict the reliability of the proposed MOPO in solving practical problems. Comparative analysis was performed with seven recently published, state-of-the-art algorithms chosen for their proven effectiveness and representation of the current research landscape-Improved Multi-Objective Manta-Ray Foraging Optimization (IMOMRFO), Multi-Objective Gorilla Troops Optimizer (MOGTO), Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Whale Optimization Algorithm (MOWOA), Multi-Objective Slime Mold Algorithm (MOSMA), Multi-Objective Particle Swarm Optimization (MOPSO), and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The results indicate that MOPO consistently outperforms these algorithms across several key metrics, including Pareto Set Proximity (PSP), Inverted Generational Distance in Decision Space (IGDX), Hypervolume (HV), Generational Distance (GD), spacing, and maximum spread, confirming its potential as a robust method for addressing complex MOO problems.