Journal ArticleDOI
Hybrid Particle Swarm and Grey Wolf Optimizer and its application to clustering optimization
TLDR
Wang et al. as discussed by the authors proposed a hybrid algorithm based on PSO and GWO (Hybrid GWO with PSO, HGWOP) to improve the global search ability while retaining the strong exploitation ability of GWO.About:
This article is published in Applied Soft Computing.The article was published on 2021-03-01. It has received 59 citations till now. The article focuses on the topics: Swarm intelligence & Hybrid algorithm.read more
Citations
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Journal ArticleDOI
Improved tunicate swarm algorithm: Solving the dynamic economic emission dispatch problems
Lingling Li,Zhi-Feng Liu,Ming-Lang Tseng,Ming-Lang Tseng,Ming-Lang Tseng,Sheng-Jie Zheng,Ming K. Lim +6 more
TL;DR: In this paper, an improved tunicate swarm algorithm (ITSA) was proposed for solving and optimizing the dynamic economic emission dispatch (DEED) problem, which aims to reduce the fuel cost and pollutant emission of the power system.
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Opposition-based learning grey wolf optimizer for global optimization
TL;DR: In this paper, an opposition-based learning grey wolf optimizer (OGWO) is proposed to boost the performance of GWO, which can help the algorithm jump out of the local optimum and not increase the computational complexity.
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Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems
Jie Mao,Zhiyuan Hao,Wenjing Sun +2 more
TL;DR: In this paper , an enhanced multi-strategies sparrow search algorithm (EMSSA) based on three strategies specifically addressing the limitations of SSA is proposed, and the results of the density peak clustering optimization show that the EMSSA outperforms SSA.
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An adaptively balanced grey wolf optimization algorithm for feature selection on high-dimensional classification
TL;DR: Zhang et al. as discussed by the authors proposed an adaptive balanced grey wolf optimization (ABGWO) algorithm to seek out the optimal feature subset for high-dimensional classification, where a random wolf is introduced to cooperate with α, β and δ.
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Three-learning strategy particle swarm algorithm for global optimization problems
Xinming Zhang,Qiuying Lin +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a three-learning strategy PSO (TLS-PSO) to solve complex optimization problems, which replaces the imitation component and social influence component of PSO to enhance the exploitation and exploration, respectively.
References
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Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
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Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
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A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
TL;DR: The basics are discussed and a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis are given.
Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization
Ponnuthurai Nagaratnam Suganthan,Nikolaus Hansen,Jing Liang,Kalyanmoy Deb,Y. P. Chen,Anne Auger,Santosh Tiwari +6 more
TL;DR: This special session is devoted to the approaches, algorithms and techniques for solving real parameter single objective optimization without making use of the exact equations of the test functions.
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The Theory of a general quantum system interacting with a linear dissipative system
TL;DR: In this paper, a formalism has been developed, using Feynman's space-time formulation of nonrelativistic quantum mechanics whereby the behavior of a system of interest, which is coupled to other external quantum systems, may be calculated in terms of its own variables only.