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Artículos de revistas

Literatura académica sobre el tema "GLOBAL BEST (GBEST)"

Autor: Grafiati

Publicado: 16 de enero de 2024

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Artículos de revistas sobre el tema "GLOBAL BEST (GBEST)"

1

CHEN, LEI, and HAI-LIN LIU. "A REGION DECOMPOSITION-BASED MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 08 (2014): 1459009. http://dx.doi.org/10.1142/s0218001414590095.

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In this paper, a novel multi-objective particle swarm optimization algorithm based on MOEA/D-M2M decomposition strategy (MOPSO-M2M) is proposed. MOPSO-M2M can decompose the objective space into a number of subregions and then search all the subregions using respective sub-swarms simultaneously. The M2M decomposition strategy has two very desirable properties with regard to MOPSO. First, it facilitates the determination of the global best (gbest) for each sub-swarm. A new global attraction strategy based on M2M decomposition framework is proposed to guide the flight of particles by setting an a

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2

Goudos, Sotirios K., Katherine Siakavara, Argiris Theopoulos, Elias E. Vafiadis, and John N. Sahalos. "Application of Gbest-guided artificial bee colony algorithm to passive UHF RFID tag design." International Journal of Microwave and Wireless Technologies 8, no. 3 (2015): 537–45. http://dx.doi.org/10.1017/s1759078715000902.

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In this paper, new planar spiral antennas with meander lines and loads for passive Radiofrequency identification tag application at ultra-high-frequency band are designed and optimized using the global best (gbest)-guided Artificial Bee Colony (GABC) algorithm. The GABC is an improved Artificial Bee Colony algorithm, which includes gbest solution information into the search equation to improve the exploitation. The optimization goals are antenna size minimization, gain maximization, and conjugate matching. The antenna dimensions were optimized and evaluated in conjunction with commercial softw

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3

Shah, Habib, Nasser Tairan, Harish Garg, and Rozaida Ghazali. "Global Gbest Guided-Artificial Bee Colony Algorithm for Numerical Function Optimization." Computers 7, no. 4 (2018): 69. http://dx.doi.org/10.3390/computers7040069.

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Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS),

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4

Ruan, Xiaodong, Jiaming Wang, Xu Zhang, Weiting Liu, and Xin Fu. "A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients." Applied Sciences 10, no. 10 (2020): 3352. http://dx.doi.org/10.3390/app10103352.

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The artificial bee colony (ABC) algorithm, which has been widely studied for years, is a stochastic algorithm for solving global optimization problems. Taking advantage of the information of a global best solution, the Gbest-guided artificial bee colony (GABC) algorithm goes further by modifying the solution search equation. However, the coefficient in its equation is based only on a numerical test and is not suitable for all problems. Therefore, we propose a novel algorithm named the Gbest-guided ABC algorithm with gradient information (GABCG) to make up for its weakness. Without coefficient

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5

Lenin, Kanagasabai, Bhumanapally Ravindhranath Reddy, and Munagala Surya Kalavathi. "Progressive Particle Swarm Optimization Algorithm for Solving Reactive Power Problem." International Journal of Advances in Intelligent Informatics 1, no. 3 (2015): 125. http://dx.doi.org/10.26555/ijain.v1i3.42.

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In this paper a Progressive particle swarm optimization algorithm (PPS) is used to solve optimal reactive power problem. A Particle Swarm Optimization algorithm maintains a swarm of particles, where each particle has position vector and velocity vector which represents the potential solutions of the particles. These vectors are modernized from the information of global best (Gbest) and personal best (Pbest) of the swarm. All particles move in the search space to obtain optimal solution. In this paper a new concept is introduced of calculating the velocity of the particles with the help of Eucl

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6

Lu, En, Lizhang Xu, Yaoming Li, Zheng Ma, Zhong Tang, and Chengming Luo. "A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning." Mathematical Problems in Engineering 2019 (November 22, 2019): 1–16. http://dx.doi.org/10.1155/2019/9367093.

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In order to balance the exploration and exploitation capabilities of the PSO algorithm to enhance its robustness, this paper presents a novel particle swarm optimization with improved learning strategies (ILSPSO). Firstly, the proposed ILSPSO algorithm uses a self-learning strategy, whereby each particle stochastically learns from any better particles in the current personal history best position (pbest), and the self-learning strategy is adjusted by an empirical formula which expresses the relation between the learning probability and evolution iteration number. The cognitive learning part is

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7

Abdullah, M. N., A. F. A. Manan, J. J. Jamian, S. A. Jumaat, and N. H. Radzi. "Gbest Artificial Bee Colony for Non-convex Optimal Economic Dispatch in Power Generation." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (2018): 187. http://dx.doi.org/10.11591/ijeecs.v11.i1.pp187-194.

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Non-convex Optimal Economic Dispatch (OED) problem is a complex optimization problem in power system operation that must be optimized economically to meet the power demand and system constraints. The non-convex OED is due to the generator characteristic such as prohibited operation zones, valve point effects (VPE) or multiple fuel options. This paper proposes a Gbest Artificial Bee Colony (GABC) algorithm based on global best particle (gbest) guided of Particle Swarm Optimization (PSO) in Artificial bee colony (ABC) algorithm for solving non-convex OED with VPE. In order to investigate the eff

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8

Liu, Yanmin, and Ben Niu. "An Improved PSO with Small-World Topology and Comprehensive Learning." International Journal of Swarm Intelligence Research 5, no. 2 (2014): 13–28. http://dx.doi.org/10.4018/ijsir.2014040102.

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Particle swarm optimization (PSO) is a heuristic global optimization method based on swarm intelligence, and has been proven to be a powerful competitor to other intelligent algorithms. However, PSO may easily get trapped in a local optimum when solving complex multimodal problems. To improve PSO's performance, in this paper the authors propose an improved PSO based on small world network and comprehensive learning strategy (SCPSO for short), in which the learning exemplar of each particle includes three parts: the global best particle (gbest), personal best particle (pbest), and the pbest of

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9

Arumugam, M. Senthil, and M. V. C. Rao. "On the optimal control of single-stage hybrid manufacturing systems via novel and different variants of particle swarm optimization algorithm." Discrete Dynamics in Nature and Society 2005, no. 3 (2005): 257–79. http://dx.doi.org/10.1155/ddns.2005.257.

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This paper presents several novel approaches of particle swarm optimization (PSO) algorithm with new particle velocity equations and three variants of inertia weight to solve the optimal control problem of a class of hybrid systems, which are motivated by the structure of manufacturing environments that integrate process and optimal control. In the proposed PSO algorithm, the particle velocities are conceptualized with the local best (orpbest) and global best (orgbest) of the swarm, which makes a quick decision to direct the search towards the optimal (fitness) solution. The inertia weight of

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10

Selvakumar, K., and S. Naveen Kumar. "Multivariate Quadratic Quasigroup Polynomial based Cryptosystem in Vanet." International Journal of Engineering & Technology 7, no. 4.10 (2018): 832. http://dx.doi.org/10.14419/ijet.v7i4.10.26767.

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Vehicular Ad-hoc Network (VANET) is a developing transmission system to abet in the everyday organization of vehicular traffic and safety of vehicles (nodes). Unsigned verification is one of the key necessities in VANET gives the confidentiality of the root of the message. Current security conventions in VANET’s gives unsigned verification depends on the two-tier architecture, comprises of two VANET components, particularly nodes and Roadside Units (RsU’s) functioning as the key developing server (KDS). This protocol depends densely on RsU’s to give unsigned identification to the nodes. In thi

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