Grey Wolf Optimizer

@article{Mirjalili2014GreyWO,
  title={Grey Wolf Optimizer},
  author={Seyed Mohammad Mirjalili and Seyed Mohammad Mirjalili and Andrew Lewis},
  journal={Adv. Eng. Softw.},
  year={2014},
  volume={69},
  pages={46-61}
}
Grey Wolf Optimizer for solving economic dispatch problems
TLDR
The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature and is able to provide very competitive results compared to these well-known meta-heuristics.
Weighted distance Grey wolf optimizer for global optimization problems
TLDR
In proposed wdGWO algorithm, the location update strategy is modified and weighted sum of best locations is used instead of just a simple average and the performance is comprehensively compared with SI algorithms counterpart.
Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer
TLDR
The results show that the GWO algorithm is able to provide very competitive results for nonlinear characteristics of the generators such as ramp rate limits, prohibited zone and non-smooth cost functions compared to the other well-known meta-heuristics techniques.
A four-step decision-making grey wolf optimization algorithm
TLDR
An improved optimization algorithm, termed decision-making grey wolf optimization algorithm (DGWO), which includes the judging prey stage in DGWO results in a faster convergence and it effectively prevents the algorithm to stop in local optima.
Howling mechanism based grey wolf optimizer
TLDR
Ten benchmark functions are considered and compared with other optimization algorithms such as GWO, Gravitational Search Algorithm (GSA), and Shuffled frog-leaping algorithm (SFLA) and the obtained results show the clear supremacy of the proposed HGWO algorithm.
A novel Random Walk Grey Wolf Optimizer
Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer
TLDR
Comparative results show that the GWO algorithm is able to provide very competitive results compared to other well-known conventional, heuristics and meta-heuristics search algorithms.
A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm
TLDR
A new hybrid algorithm is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively and effectively improves the global optimal search capability and convergence speed of the GWO and FWA.
On the improvement in grey wolf optimization
TLDR
An extended version of grey wolf optimization (GWO-E) algorithm is presented, which is able to explore new areas in the search space because of diverse positions assigned to the leaders and avoids local optima stagnation problem.
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References

SHOWING 1-10 OF 104 REFERENCES
Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation
TLDR
An auto-tuning strategy by using fuzzy logic control for taking the balance among the stochastic search and local search probabilities based on the change of the average fitness of parents and offspring which is occurred at each generation is proposed.
Nature-Inspired Metaheuristic Algorithms: Second Edition
TLDR
This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms for global optimization, including ant and bee algorithms, bat algorithm, cuckoo search, differential evolution, firefly algorithm, genetic algorithms, harmony search, particle swarm optimization, simulated annealing and support vector machines.
Binary bat algorithm
TLDR
The proposed binary bat algorithm (BBA) is able to significantly outperform others on majority of the benchmark functions and there is a real application of the proposed method in optical engineering called optical buffer design that evidence the superior performance of BBA in practice.
A new optimization method: Big Bang-Big Crunch
Nature-Inspired Metaheuristic Algorithms
TLDR
This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization
Krill herd: A new bio-inspired optimization algorithm
MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach
  • H. Abbass
  • Computer Science
    Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546)
  • 2001
TLDR
The aim in this paper is to analyze the behavior of the algorithm using biological concepts (number of queens, spermatheca size, and number of broods) rather than trying to improve the performance of the algorithms while losing the underlying biological essence.
An empirical study about the usefulness of evolution strategies to solve constrained optimization problems
TLDR
It is shown how using just three simple comparison criteria based on feasibility, the simple evolution strategy can be led to the feasible region of the search space and find the global optimum solution (or a very good approximation of it).
An effective co-evolutionary differential evolution for constrained optimization
...
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