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AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems.

Guoping You¹, Zengtong Lu^2,3, Zhipeng Qiu⁴

¹School of Information Engineering, Jiangxi Science and Technology Normal University, Nanchang 330000, China.

Biomimetics (Basel, Switzerland)

|December 27, 2024

Summary

This summary is machine-generated.

This study introduces augmented multi-strategy beluga optimization (AMBWO), an improved algorithm addressing beluga whale optimization

Keywords:

adaptive beluga whale optimization global optimization metaheuristic

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Area of Science:

Artificial Intelligence
Computational Intelligence
Optimization Algorithms

Background:

Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm.
BWO exhibits limitations in global exploration and can get stuck in local optima.

Purpose of the Study:

To enhance the performance of the beluga whale optimization algorithm.
To improve global exploration and exploitation capabilities.
To increase the ability to escape local optima.

Main Methods:

Proposes augmented multi-strategy beluga optimization (AMBWO).
Introduces adaptive population learning for global exploration.
Incorporates roulette equilibrium selection for exploitation flexibility.
Implements an adaptive avoidance strategy to escape local optima.

Main Results:

AMBWO demonstrates superior performance on CEC2017 and CEC2022 test sets.
Statistical, convergence, and stability analyses confirm enhanced capabilities.
Validated applicability and superiority on engineering optimization problems.

Conclusions:

The proposed AMBWO significantly outperforms standard BWO and other state-of-the-art algorithms.
AMBWO offers improved global search, exploitation, and local optima avoidance.
The enhanced algorithm shows strong potential for solving complex engineering optimization tasks.