Advances in Engineering Software· 2014Q1
Grey Wolf Optimizer
- 18,589citations
- Q1SCImago
- 2014year
Short summary
A new meta-heuristic algorithm, the Grey Wolf Optimizer (GWO), is introduced, mimicking the social hierarchy and hunting behaviors of grey wolves.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces the Grey Wolf Optimizer (GWO) algorithm based on wolf social hierarchy and hunting.
- Simulates four wolf types (alpha, beta, delta, omega) for leadership hierarchy.
- Models hunting steps: searching, encircling, and attacking prey.
- Benchmarked on 29 test functions and 3 engineering design problems, showing competitive results.
- Demonstrated applicability to challenging problems with unknown search spaces.
AI-generated from the title and abstract; the full text is not read.
Abstract
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.
The authors' abstract, as published at the source. Advances in Engineering Software, 2014 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Artificial Intelligence
Artificial IntelligenceComputer Science