Results 171 to 180 of about 4,807 (213)
Some of the next articles are maybe not open access.
A chaotic grey wolf optimizer for constrained optimization problems
Expert Systems, 2021AbstractBio‐inspired algorithms have become popular due to their capability of finding good solutions for complex optimization problems in an acceptable computational time. The Grey Wolf Optimizer is a nature‐inspired, population‐based metaheuristic that simulates the social hierarchy and the hunting strategy observed in a grey wolf pack.
openaire +2 more sources
A better exploration strategy in Grey Wolf Optimizer
Journal of Ambient Intelligence and Humanized Computing, 2020The Grey Wolf Optimizer (GWO) is a recently developed population-based meta-heuristics algorithm that mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Although, GWO has shown very good results on several real-life applications but still it suffers from some issues like, the low exploration and slow convergence rate ...
Jagdish Chand Bansal, Shitu Singh
openaire +2 more sources
Recommender system with grey wolf optimizer and FCM
Neural Computing and Applications, 2016Recommender systems are contributing a significant aspect in information filtering and knowledge management systems. They provide explicit and reliable recommendations to the users so that user can get information about all products in e-commerce domain.
Rahul Katarya, Om Prakash Verma
openaire +1 more source
An improved hybrid grey wolf optimization algorithm
Soft Computing, 2018The existing grey wolf optimization algorithm has some disadvantages, such as slow convergence speed, low precision and so on. So this paper proposes a grey wolf optimization algorithm combined with particle swarm optimization (PSO_GWO). In this new algorithm, the Tent chaotic sequence is used to initiate the individuals’ position, which can increase ...
Zhi-jun Teng, Jin-ling Lv, Li-wen Guo
openaire +2 more sources
LGWO: An Improved Grey Wolf Optimization for Function Optimization
2017Grey wolf optimization (GWO) algorithm is a novel nature-inspired heuristic paradigm. GWO was inspired by grey wolves, which mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. It has exhibited promising performance in many fields. However, GWO algorithm has the drawback of slow convergence and low precision.
Jie Luo 0002 +5 more
openaire +2 more sources
Anais do XVI Congresso Brasileiro de Inteligência Computacional
Swarm-based metaheuristics have become the most prominent method for solving optimization problems. Several operators already proposed in the literature can also be reused to expand the current metaheuristics. We present in this paper the Volitive Grey Wolf Optimizer (VGWO), a Grey Wolf Optimizer variant created by the addition of the collective ...
Joao Paulo Silva +4 more
openaire +1 more source
Swarm-based metaheuristics have become the most prominent method for solving optimization problems. Several operators already proposed in the literature can also be reused to expand the current metaheuristics. We present in this paper the Volitive Grey Wolf Optimizer (VGWO), a Grey Wolf Optimizer variant created by the addition of the collective ...
Joao Paulo Silva +4 more
openaire +1 more source
Hybrid Grey Wolf Optimizer with Mutation Operator
2018Grey Wolf Optimizer (GWO), developed by Mirjalili et al. (Adv Eng Softw 69:46–61, 2014 [1]), is a recently developed nature-inspired technique based on leadership hierarchy of grey wolves. In this paper, Grey Wolf Optimizer has been hybridized with differential evolution (DE) mutation, and two versions, namely DE-GWO and gDE-GWO, have been proposed to ...
Shubham Gupta, Kusum Deep
openaire +2 more sources
Boolean Binary Grey Wolf Optimizer
2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 2022Rodrigo Cesar Lira +3 more
openaire +1 more source
Natural selection methods for Grey Wolf Optimizer
Expert Systems with Applications, 2018Abstract The selection process is the most attractive operator in the optimization algorithms. It normally mimics the natural selection of survival of the fittest principle. When the selection is too greedy, the selection pressure will be high and therefore the search becomes biased toward exploitation.
Mohammed Azmi Al-Betar +4 more
openaire +1 more source
Evolutionary population dynamics and grey wolf optimizer
Neural Computing and Applications, 2014Evolutionary population dynamics (EPD) deal with the removal of poor individuals in nature. It has been proven that this operator is able to improve the median fitness of the whole population, a very effective and cheap method for improving the performance of meta-heuristics. This paper proposes the use of EPD in the grey wolf optimizer (GWO). In fact,
Saremi, Shahrzad +2 more
openaire +3 more sources