# A Bio-Inspired Comprehensive Learning Strategy-Enhanced Parrot Optimizer: Performance Evaluation and Application to Reservoir Production Optimization

## TL;DR

A new bio-inspired optimization algorithm, CL-PO, is developed to solve complex engineering problems by improving exploration and avoiding local optima.

## Contribution

The novel CL-PO algorithm introduces a multi-exemplar social learning mechanism inspired by parrot behavior to enhance optimization performance.

## Key findings

- CL-PO outperformed nine state-of-the-art algorithms on 29 CEC 2017 test functions with an average Friedman rank of 1.28.
- CL-PO achieved a maximum net present value of 9.625×108 USD in a reservoir production optimization task using the Egg benchmark model.

## Abstract

The efficacy of swarm intelligence algorithms in navigating high-dimensional, non-convex landscapes depends on the dynamic balance between global exploration and local exploitation. Drawing inspiration from the intricate social dynamics of Pyrrhura molinae, this study proposes a novel bio-inspired metaheuristic, the Comprehensive Learning Parrot Optimizer (CL-PO). While the original Parrot Optimizer (PO) simulates collective foraging and communication, it often suffers from population homogenization and entrapment in local optima due to its reliance on single-source social learning. To address these limitations, CL-PO incorporates a dimension-wise multi-exemplar social learning mechanism analogous to the cross-individual knowledge transfer observed in avian colonies. This strategy enables stagnant individuals to reconstruct their search trajectories by learning from multiple superior peers, thereby sustaining population diversity and facilitating adaptive exploration. Rigorous benchmarking on 29 test functions from the CEC 2017 suite reveals that CL-PO achieves statistically superior performance compared to nine state-of-the-art algorithms, securing a top-tier average Friedman rank of 1.28. Furthermore, the practical utility of CL-PO is substantiated through a complex reservoir production optimization task using the Egg benchmark model, where it consistently maximizes the net present value (NPV), reaching 9.625×108 USD. These findings demonstrate that CL-PO is a powerful and reliable solver for addressing large-scale engineering optimization problems under complex constraints.