Kookaburra 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.
• Bio-inspired approaches offer novel strategies by mimicking natural phenomena.
• Existing algorithms may face challenges in balancing exploration and exploitation effectively.

Purpose of the Study:

• Introduce a novel bio-inspired metaheuristic algorithm, the Kookaburra Optimization Algorithm (KOA).
• Model KOA based on kookaburra hunting and prey-killing behaviors.
• Evaluate KOA's performance on benchmark and real-world optimization problems.

Main Methods:

• Developed the Kookaburra Optimization Algorithm (KOA) inspired by kookaburra hunting strategies.
• Mathematically modeled KOA in two phases: exploration (hunting) and exploitation (securing prey).
• Tested KOA on CEC 2017 benchmark functions (10-100 dimensions) and CEC 2011 constrained problems.

Main Results:

• KOA demonstrated a good balance between exploration and exploitation for efficient search.
• KOA achieved superior performance compared to 12 well-known metaheuristic algorithms on benchmark functions.
• KOA showed acceptable and superior performance on constrained and engineering design problems.

Conclusions:

• The Kookaburra Optimization Algorithm (KOA) is an efficient and effective metaheuristic for optimization.
• KOA's bio-inspired approach provides a competitive advantage over existing algorithms.
• KOA shows promise for handling complex real-world optimization applications.