An Enhanced Grey Wolf Optimizer with Elite Inheritance and Balance Search Mechanisms
Jianhua Jiang, Ziying Zhao, Weihua Li, Keqin Li

TL;DR
This paper introduces EBGWO, an improved Grey Wolf Optimizer that incorporates elite inheritance and balanced search mechanisms, significantly enhancing convergence speed and solution quality in benchmark and real-world optimization problems.
Contribution
The paper proposes EBGWO, a novel GWO variant with elite inheritance and balanced search strategies, addressing key flaws in the original GWO to improve optimization performance.
Findings
EBGWO outperforms other meta-heuristics in accuracy and speed.
Demonstrates effectiveness on benchmark functions and real-world problems.
Achieves better convergence and solution quality than existing GWO variants.
Abstract
The Grey Wolf Optimizer (GWO) is recognized as a novel meta-heuristic algorithm inspired by the social leadership hierarchy and hunting mechanism of grey wolves. It is well-known for its simple parameter setting, fast convergence speed, and strong optimization capability. In the original GWO, there are two significant design flaws in its fundamental optimization mechanisms. Problem (1): the algorithm fails to inherit from elite positions from the last iteration when generating the next positions of the wolf population, potentially leading to suboptimal solutions. Problem (2): the positions of the population are updated based on the central position of the three leading wolves (alpha, beta, delta), without a balanced mechanism between local and global search. To tackle these problems, an enhanced Grey Wolf Optimizer with Elite Inheritance Mechanism and Balance Search Mechanism, named as…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications
