Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Bio-inspired Computing

Background:

• Metaheuristic algorithms are crucial for solving complex optimization problems.
• Existing algorithms often struggle with balancing exploration and exploitation.
• Bio-inspired approaches offer novel strategies for optimization challenges.

Purpose of the Study:

• Introduce a novel bio-inspired metaheuristic algorithm, Giant Armadillo Optimization (GAO).
• To model and mathematically formulate the GAO algorithm based on giant armadillo hunting strategies.
• Evaluate GAO's performance on benchmark optimization problems and compare it with existing algorithms.

Main Methods:

• Developed the Giant Armadillo Optimization (GAO) algorithm inspired by armadillo foraging and digging behaviors.
• Modeled GAO in two phases: exploration (movement towards prey) and exploitation (digging for prey).
• Tested GAO on the CEC 2017 test suite across various dimensions (10, 30, 50, 100) and compared results with twelve established algorithms.

Main Results:

• GAO demonstrated effective solutions for optimization tasks by balancing exploration and exploitation.
• GAO achieved superior performance compared to twelve well-known metaheuristic algorithms on most benchmark functions.
• Statistical analysis using the Wilcoxon rank sum test confirmed GAO's significant superiority.
• GAO showed effective performance on the CEC 2011 test suite and real-world engineering design problems.

Conclusions:

• The proposed Giant Armadillo Optimization (GAO) algorithm is a highly effective metaheuristic.
• GAO offers a robust approach for tackling complex optimization problems and real-world applications.
• GAO exhibits significant advantages in exploration, exploitation, and balancing these phases during the search process.