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

Area of Science:

• Computational Intelligence
• Metaheuristic Optimization
• Bio-inspired Algorithms

Background:

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

Purpose of the Study:

• Introduce a novel bio-inspired metaheuristic algorithm, the Lyrebird Optimization Algorithm (LOA).
• Mathematically model LOA based on lyrebird behavior during danger.
• Evaluate LOA's performance against established algorithms on benchmark and real-world problems.

Main Methods:

• Developed the Lyrebird Optimization Algorithm (LOA) simulating lyrebird escape (exploration) and hiding (exploitation) strategies.
• Tested LOA on the CEC 2017 benchmark test suite across various dimensions (10, 30, 50, 100).
• Compared LOA's performance against twelve well-known metaheuristic algorithms and on CEC 2011 constrained optimization problems.

Main Results:

• LOA demonstrated strong capabilities in exploration, exploitation, and balancing these phases.
• LOA achieved superior performance over twelve competing algorithms on most CEC 2017 benchmark functions.
• Statistical analysis confirmed LOA's significant superiority, with effective results on real-world engineering design problems.

Conclusions:

• The Lyrebird Optimization Algorithm (LOA) is a highly effective and robust optimization technique.
• LOA offers a promising alternative for complex optimization tasks in both theoretical and practical domains.
• The bio-inspired approach of LOA provides a significant advantage in optimization problem-solving.