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An innovative metaheuristic algorithm for photovoltaic tilt angle optimization. [PDF]

Sci Rep
He L, Zhao B, Rong F, Zhu B, Xu M, Wang L.   +5 more
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Efficient estimation of proton exchange membrane fuel cells parameters using a hybrid swarm intelligent algorithm. [PDF]

Sci Rep
Sharma P, Salgotra R, Raju S, Łukasik S, Gandomi AH.   +4 more
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Multi-objective optimization of grinding process parameters for complicated worm space surface based on the grey wolf optimization algorithm

Jiongkang Ren, Shisong Wang, Keqi Ren
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Forecasting photovoltaic power in high-latitude regions via support vector machine optimized by meta-heuristics. [PDF]

Sci Rep
Oruç S, Hınıs MA, Tuğrul T.
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Grey wolf gravitational search algorithm

2016 International Workshop on Computational Intelligence (IWCI), 2016
Gravitational 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 Joshi, Jagdish Chand Bansal
openaire   +1 more source

Enhanced Grey Wolf Optimization Algorithm for Global Optimization

Fundamenta Informaticae, 2017
Grey 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. In the original GWO, half of the iterations are dedicated to exploration and the
Joshi, Himani, Arora, Sankalap
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Modified Grey Wolf Algorithm for optimization problems

2016 International Conference on Inventive Computation Technologies (ICICT), 2016
Grey 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
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Fully Informed Grey Wolf Optimizer Algorithm

2020
Grey wolf optimizer (GWO) is a newly generated metaheuristic search algorithm inspired by the social behaviour of the grey wolf, which resembles the social structure and hunting mechanism of grey wolves in nature, and is based on three main steps: searching for prey, encircling prey and attacking prey. This paper presents a new variant of GWO algorithm
Priyanka Meiwal, Harish Sharma, Nirmala Sharma   +2 more
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An improved hybrid grey wolf optimization algorithm

Soft Computing, 2018
The 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
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