An improved marriage in honey bees optimization algorithm for single objective unconstrained optimization.

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Metaheuristic Optimization

Background:

• Marriage in Honey Bees Optimization (MBO) is a metaheuristic algorithm inspired by honey bee reproduction.
• Swarm intelligence algorithms offer robust solutions for complex optimization problems.
• Existing MBO algorithms can be enhanced for improved performance.

Purpose of the Study:

• To propose an Improved Marriage in Honey Bees Optimization (IMBO) algorithm.
• To enhance the MBO algorithm by integrating Levy flight and neighboring strategies.
• To evaluate the performance of IMBO against other metaheuristic algorithms.

Main Methods:

• The proposed IMBO algorithm incorporates Levy flight for the queen bee's mating flight.
• Neighboring strategies are integrated for the worker drone's search behavior.
• IMBO's efficacy is tested using six standard unconstrained test functions.

Main Results:

• The IMBO algorithm demonstrated superior performance on the tested unconstrained functions.
• Comparative analysis showed IMBO outperforming other established metaheuristic optimization algorithms.
• The enhancements led to more effective and efficient optimization outcomes.

Conclusions:

• The proposed IMBO algorithm offers significant improvements over the standard MBO.
• Integrating Levy flight and neighboring strategies enhances swarm intelligence optimization.
• IMBO presents a promising alternative for complex optimization tasks.