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The Chinese Journal of Artificial Intelligence, 2022
Background: The Particle Swarm Optimization (PSO) algorithm is amongst the utmost favourable optimization algorithms often employed in hybrid procedures by the researchers considering simplicity, smaller count of parameters involved, convergence speed and capability of searching global optima.
Sumita Gulati, Ashok Pal
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Background: The Particle Swarm Optimization (PSO) algorithm is amongst the utmost favourable optimization algorithms often employed in hybrid procedures by the researchers considering simplicity, smaller count of parameters involved, convergence speed and capability of searching global optima.
Sumita Gulati, Ashok Pal
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On the improvement in grey wolf optimization
Neural Computing and Applications, 2019Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method which mimics the social behaviour and hunting mechanism of grey wolves. Though the algorithm is very competitive and has been applied to various fields of research, it has poor exploration capability and suffers from local optima stagnation.
Rohit Salgotra +2 more
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Application of Mutation Operators on Grey Wolf Optimizer
2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021Nature-inspired computing has been widely used for solving different optimization problems. Grey wolf optimization (GWO) is one of the recent addition in the category of swarm-based techniques. The swarm-based algorithms try to find a balance between exploitation and exploration through different steps/processes.
Priyanka Singh 0003, Rahul Kottath
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A novel Random Walk Grey Wolf Optimizer
Swarm and Evolutionary Computation, 2019Abstract Grey Wolf Optimizer (GWO) algorithm is a relatively new algorithm in the field of swarm intelligence for solving continuous optimization problems as well as real world optimization problems. The Grey Wolf Optimizer is the only algorithm in the category of swam intelligence which is based on leadership hierarchy.
Kusum Deep, Shubham Gupta
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Improved Discrete Grey Wolf Optimizer
2018 26th European Signal Processing Conference (EUSIPCO), 2018Grey wolf optimizer (GWO) is a bioinspired iterative optimization algorithm which simulates the hunting process of a wolf pack guided by three leaders. In this paper, a novel discrete GWO is proposed: a random leader selection is performed, and the probability for the main leader to be selected increases at the detriment of the other leaders across ...
Martin, Benoît +2 more
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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
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Grey wolf optimizer with cellular topological structure
Expert Systems With Applications, 2018Abstract Grey wolf optimizer (GWO) is a newly developed metaheuristic inspired by hunting mechanism of grey wolves. The paramount challenge in GWO is that it is prone to stagnation in local optima. This paper proposes a cellular grey wolf optimizer with a topological structure (CGWO). The proposed CGWO has two characteristics.
Liang Gao, Jin Yi, Chao Lu
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Empirical Study of Grey Wolf Optimizer
2016In this paper, the authors empirically investigate performance of the grey wolf optimizer (GWO). A test suite of six non-linear benchmark functions, well studied in the swarm and the evolutionary optimization literature, is selected to highlight the findings. The test suite contains three unimodal and three multimodal functions.
Avadh Kishor, Pramod Kumar Singh
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Chaotic grey wolf optimization
2016 International Conference on Progress in Informatics and Computing (PIC), 2016Grey wolf optimization algorithm (GWO) is a recently proposed meta-heuristics and has shown promising performance in solving complex function optimization and engineering problems. To further enrich the search dynamics of GWO, the chaotic local search (CLS) mechanism is incorporated into GWO to enhance the search by taking the properties of ergodicity ...
Hang Yu +4 more
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Entropy Based Grey Wolf Optimizer
2020Recently Shannon’s Entropy has been incorporated in nature inspired metaheuristics with good results. Depending on the problem, the Grey Wolf Optimization (GWO) algorithm may suffer from premature convergence. Here, an Entropy Grey Wolf Optimization (E-GWO) technique is proposed with the overall aim to improve the original GWO performance.
Daniel Duarte +2 more
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