Results 201 to 210 of about 3,322,155 (235)
Some of the next articles are maybe not open access.
Grey wolf gravitational search algorithm
2016 International Workshop on Computational Intelligence (IWCI), 2016Gravitational search algorithm is a nature inspired optimization algorithm, inspired by newton's law of gravity and law of motion. In this paper, a new variant of Gravitational search algorithm is presented. The exploration and exploitation capability of GSA is balanced by splitting the whole swarm into two groups.
Susheel Kumar Joshi +1 more
openaire +1 more source
An Enhanced Grey Wolf Algorithm Based on Equalization Mechanism
Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 2020Since the GWO (Grey wolf optimization) has some limitation in application to real-wold problems, such as slow convergence speed, low precision and it easily falls into the local minimal in the later stage of complex optimization problems, a novel grey wolf algorithm based on equalization mechanism (EmGWO) is proposed.
Yuntao Zhao, Wei Mei, Weigang Li 0004
openaire +1 more source
Enhanced Grey Wolf Optimization Algorithm for Global Optimization
Fundamenta Informaticae, 2017Grey Wolf Optimizer (GWO) is a new meta-heuristic search algorithm inspired by the social behavior of leadership and the hunting mechanism of grey wolves. GWO algorithm is prominent in terms of finding the optimal solution without getting trapped in premature convergence.
Himani Joshi, Sankalap Arora
openaire +3 more sources
Review of the grey wolf optimization algorithm: variants and applications
Neural Computing and Applications, 2023One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and superior convergence outcomes, leading to its ...
Yunyun Liu +4 more
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
exaly +3 more sources
A fuzzy hierarchical operator in the grey wolf optimizer algorithm
Applied Soft Computing, 2017Abstract The main goal of this paper is to study the performance of the Grey Wolf Optimizer (GWO) algorithm when a new hierarchical operator is introduced in the algorithm. This new operator is basically a hierarchical transformation that is inspired in the hierarchical social pyramid of the grey wolf.
Luis Rodríguez +7 more
openaire +1 more source
Effects of dominant wolves in grey wolf optimization algorithm
Applied Soft Computing Journal, 2019Abstract Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements.
Fehmi Burcin Ozsoydan
exaly +4 more sources
Grey Wolf Optimization (GWO) Algorithm
2017This chapter describes the grey wolf optimization (GWO) algorithm as one of the new meta-heuristic algorithms. First, a brief literature review is presented and then the natural process of the GWO algorithm is described. Also, the optimization process and a pseudo code of the GWO algorithm are presented in this chapter.
Hossein Rezaei +2 more
openaire +1 more source
A Hybrid Grey Wolf-Bat Algorithm for Global Optimization
2018Bio-inspired algorithms are now becoming powerful methods for solving many real-world optimization problems. In this paper, we propose a hybrid approach involving Grey Wolf optimizer (GWO) and Bat swarm optimizer (BA) for global function optimization problems. GWO is well known for its balanced exploration/exploitation behavior, while BA is known to be
Mohammed ElGayyar +3 more
openaire +2 more sources
Modified Grey Wolf Algorithm for optimization problems
2016 International Conference on Inventive Computation Technologies (ICICT), 2016Grey Wolf Algorithm (GWA) is the recently developed metaheuristic technique. It is inspired from wolf's social and hunting behavior. In paper, a modification in GWA is proposed to provide the better balance between exploration and exploitation. The proposed algorithm is tested on thirteen benchmark test functions.
null Seema, Vijay Kumar
openaire +1 more source