Model-Free Optimization Using Eagle Perching Optimizer

TL;DR

This paper introduces a new nature-inspired optimization algorithm based on eagle perching behavior, demonstrating superior performance and robustness over existing metaheuristics through extensive benchmarking and real-world problem testing.

Contribution

It presents a novel eagle perching-inspired optimization algorithm with two versions, showing improved performance and efficiency compared to existing methods.

Findings

Modified version outperforms the original in benchmarks

Algorithm shows robustness across diverse functions

Better computational efficiency than comparable algorithms

Abstract

The paper proposes a novel nature-inspired technique of optimization. It mimics the perching nature of eagles and uses mathematical formulations to introduce a new addition to metaheuristic algorithms. The nature of the proposed algorithm is based on exploration and exploitation. The proposed algorithm is developed into two versions with some modifications. In the first phase, it undergoes a rigorous analysis to find out their performance. In the second phase it is benchmarked using ten functions of two categories; uni-modal functions and multi-modal functions. In the third phase, we conducted a detailed analysis of the algorithm by exploiting its controlling units or variables. In the fourth and last phase, we consider real world optimization problems with constraints. Both versions of the algorithm show an appreciable performance, but analysis puts more weight to the modified version. The competitive analysis shows that the proposed algorithm outperforms the other tested metaheuristic algorithms. The proposed method has better robustness and computational efficiency.