Grey Wolf Optimizer and Its Applications: A Survey

Panda, Madhusmita

doi:10.1007/978-981-13-7091-5_17

Cited by 49 publications

(22 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next comes is the delta wolves, which dominate the omega wolves, which are the lowest in the ranking. In the GWO algorithm, alpha wolves represent the best solution, followed by beta and delta [15]. The algorithm is shown in Algorithm 2.…”

Section: Grey Wolf Optimizationmentioning

confidence: 99%

“…Another technique used is Grey Wolf Optimization (GWO), which is based on the hunting process and social hierarchy of grey wolves. [15] presents an overview of GWO with its mathematical modeling, mainly contributing to the area of path planning for vehicles for Autonomous Underwater Vehicle (AUV). The optimization performed by the GWO proved to be better both in execution time and in path length compared to Ant Colony Optimization (ACO).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison between Meta-Heuristic Algorithms for Path Planning

Rocha

Vivaldini

2020

Anais Estendidos Do XII Simpósio Brasileiro De Robótica E XVII Simpósio Latino Americano De Robótica (SBR/LARS Estendido 2020)

View full text Add to dashboard Cite

Unmanned Aerial Vehicle (UAV) has been increasingly employed in several missions with a pre-defined path. Over the years, UAV has become necessary in complex environments, where it demands high computational cost and execution time for traditional algorithms. To solve this problem meta-heuristic algorithms are used. Meta-heuristics are generic algorithms to solve problems without having to describe each step until the result and search for the best possible answer in an acceptable computational time. The simulations are made in Python, with it, a statistical analyses was realized based on execution time and path length between algorithms Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Glowworm Swarm Optimization (GSO). Despite the GWO returns the paths in a shorter time, the PSO showed better performance with similar execution time and shorter path length. However, the reliability of the algorithms will depend on the size of the environment. PSO is less reliable in large environments, while the GWO maintains the same reliability.

show abstract

Section: Grey Wolf Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison between Meta-Heuristic Algorithms for Path Planning

Rocha

Vivaldini

2020

Anais Estendidos Do XII Simpósio Brasileiro De Robótica E XVII Simpósio Latino Americano De Robótica (SBR/LARS Estendido 2020)

View full text Add to dashboard Cite

show abstract

“…Researchers have continued to develop different classes of these algorithms by relying on parts within the algorithm or by combining them with algorithms that support them to strengthen them, such as [24][25][26][27][28][29][30]. "…”

Section: Introductionmentioning

confidence: 99%

Implementation of combined new optimal cuckoo algorithm with a gray wolf algorithm to solve unconstrained optimization nonlinear problems

Al-Arabo

Al-Kawaz²

2020

IJEECS

View full text Add to dashboard Cite

<p>In this article, a combined optimization algorithm was proposed which combines the optimal adaptive Cuckoo algorithm (OACS) which is Nature-inspired algorithm with Gray Wolf optimizer algorithm (GWO). Sometimes considering the cuckoo algorithm alone, may fail to find the local minimum-point and also fails to reach to the solution because of the slow speed of its convergence property. Therefore, considering the new proposed adaptive combined algorithm gave a strong improvement for using this to reach the minimum point in solving (23) nonlinear test problems. This is suitable to solve a large number of nonlinear unconstraint optimization test functions with obtaining good and robust numerical results.</p>

show abstract

“…The choice of such techniques relies on their dominant stochastically behavior, since more random operators and population subgroups (or population split) are included and, thus, one has the improved ability of escaping from local optima through the search process. Although other similar strategies are available, previous experience [15], recent results [16], application diversity, and continuous improvement on both Ant Lion (ALO) [17,18] and Grey Wolf Optimizers (GWO) [19,20] encourage and motivate their application. Regarding mobile robotics, besides an improved version for ALO [21], GWO has also been recommended for mission (and trajectory) planning [22], even with moving obstacles [23].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Oliveira

Boaventura‐Cunha

et al. 2020

Robotics

View full text Add to dashboard Cite

The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

show abstract

Grey Wolf Optimizer and Its Applications: A Survey

Cited by 49 publications

References 40 publications

Comparison between Meta-Heuristic Algorithms for Path Planning

Comparison between Meta-Heuristic Algorithms for Path Planning

Implementation of combined new optimal cuckoo algorithm with a gray wolf algorithm to solve unconstrained optimization nonlinear problems

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Contact Info

Product

Resources

About