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Добірка наукової літератури з теми "GLOBAL BEST (GBEST)"

Автор: Grafiati

Опубліковано: 16 січня 2024

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Статті в журналах з теми "GLOBAL BEST (GBEST)"

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 (December 2014): 1459009. http://dx.doi.org/10.1142/s0218001414590095.

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Анотація:

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 archive set which is used to store the historical best solutions found by the swarm. When we determine the gbest for each particle, the archive set is decomposed and associated with each sub-swarm. Therefore, every sub-swarm has its own archive subset and the gbest of the particle in a sub-swarm is selected randomly in its archive subset. The new global attraction strategy yields a more reasonable gbest selection mechanism, which can be more effective to guide the particles to the Pareto Front (PF). This strategy can ensure that each sub-swarm searches its own subregion so as to improve the search efficiency. Second, it has a good ability to maintain the diversity of the population which is desirable in multi-objective optimization. Additionally, MOPSO-M2M applies the Tchebycheff approach to determine the personal best position (pbest) and no additional clustering or niching technique is needed in this algorithm. In order to demonstrate the performance of the proposed algorithm, we compare it with two other algorithms: MOPSO and DMS-MO-PSO. The experimental results indicate the validity of this method.

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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 (June 1, 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 software FEKO. GABC is compared with other popular algorithms. The optimization results produced show that GABC is a powerful optimization algorithm that can be efficiently applied to tag antenna design problems.

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

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Анотація:

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), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing.

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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 (May 12, 2020): 3352. http://dx.doi.org/10.3390/app10103352.

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Анотація:

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 factors, a new solution search equation based on variable gradients is established. Besides, the gradients are also applied to differentiate the priority of different variables and enhance the judgment of abandoned solutions. Extensive experiments are conducted on a set of benchmark functions with the GABCG algorithm. The results demonstrate that the GABCG algorithm is more effective than the traditional ABC algorithm and the GABC algorithm, especially in the latter stages of the evolution.

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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 (November 30, 2015): 125. http://dx.doi.org/10.26555/ijain.v1i3.42.

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Анотація:

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 Euclidian Distance conception. This new-fangled perception helps in finding whether the particle is closer to Pbest or Gbest and updates the velocity equation consequently. By this we plan to perk up the performance in terms of the optimal solution within a rational number of generations. The projected PPS has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss with control variables are within the limits.

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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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Анотація:

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 improved by the self-learning strategy, and the optimal individual is reserved to ensure the convergence speed. Meanwhile, based on the multilearning strategy, the global best position (gbest) of particles is replaced with randomly chosen from the top k of gbest and further improve the population diversity to prevent premature convergence. This strategy improves the social learning part and enhances the global exploration capability of the proposed ILSPSO algorithm. Then, the performance of the ILSPSO algorithm is compared with five representative PSO variants in the experiments. The test results on benchmark functions demonstrate that the proposed ILSPSO algorithm achieves significantly better overall performance and outperforms other tested PSO variants. Finally, the ILSPSO algorithm shows satisfactory performance in vehicle path planning and has a good result on the planned path.

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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 (July 1, 2018): 187. http://dx.doi.org/10.11591/ijeecs.v11.i1.pp187-194.

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Анотація:

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 effectiveness and performance of GABC algorithm, the IEEE 14-bus 5 unit generators and IEEE 30-bus 6 unit generators test systems are considered. The comparison of optimal solution, convergence characteristic and robustness are also highlighted to reveal the advantages of GABC. Moreover, the optimal results obtained by proposed GABC are compared with other reported results of meta-heuristic algorithms. It found that the GABC capable to obtain lowest cost as compared to others. Thus, it has great potential to be implemented in different types of power system optimization problem.

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

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Анотація:

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 its neighborhood. Additionally, a random position around a particle is used to increase its probability to jump to a promising region. These strategies enable the diversity of the swarm to discourage premature convergence. By testing on five benchmark functions, SCPSO is proved to have better performance than PSO and its variants. SCPSO is then used to determine the optimal parameters involved in the Van-Genuchten model. The experimental results demonstrate the good performance of SCPSO compared with other methods.

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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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Анотація:

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 the proposed methods is also described as a function of pbest and gbest, which allows the PSO to converge faster with accuracy. A typical numerical example of the optimal control problem is included to analyse the efficacy and validity of the proposed algorithms. Several statistical analyses including hypothesis test are done to compare the validity of the proposed algorithms with the existing PSO technique, which adopts linearly decreasing inertia weight. The results clearly demonstrate that the proposed PSO approaches not only improve the quality but also are more efficient in converging to the optimal value faster.

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Selvakumar, K., and S. Naveen Kumar. "Multivariate Quadratic Quasigroup Polynomial based Cryptosystem in Vanet." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 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 this paper, we propose the K-means Cluster Head algorithm which is utilized for guide assortment, for both personal-best (’pbest’) and global-best (’gbest’), are observed a tremendously successful and complete well evaluate to the before existing methods. Here, we also propose an asymmetric encryption algorithm, with emphasis on Multivariate Quadratic Quasigroups (MVQQ) algorithm, in a circumstance of VANET. We set forward prime pseudonyms reasonably make a long time cycle that are worn to interact with semi-confided in experts and alternate pseudonyms with a minor lifetime which are utilized to talk with different nodes.

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Більше джерел

Дисертації з теми "GLOBAL BEST (GBEST)"

HANSRAJ and BIJESH YADAV. "PARTICLE SWARM OPTIMIZATION." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20425.

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Анотація:

An optimisation algorithm based on the behaviors of social organisms is known as particle swarm optimizatio (PSO).It represents a set of potential answers to an optimi sation issue as a swarm of moving particles in the parameter space. The performance of the particles is guided by their own performance and the performance of their neighbors, leading to an optimized solution. This thesis presents a study of the impact of boundary conditions on the performance of Particle Swarm Optimization (PSO) through the use of the invisible wall technique. The convergence behaviors of PSO are analyzed and its application to discrete-valued problems and multi-objective optimization problems are discussed. Additionally, practical applications of PSO are explored. We are solved linear programming problems, transportation problem using Particle Swarm Optimization and applying on a Data Set.

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Частини книг з теми "GLOBAL BEST (GBEST)"

Goudos, Sotirios K., Katherine Siakavara, and John N. Sahalos. "Application of Artificial Bee Colony Algorithms to Antenna Design Problems for RFID Applications." In Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, 236–65. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9644-0.ch009.

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Анотація:

In this chapter, the Artificial Bee Colony (ABC) algorithm and its variants are presented and applied to spiral antennas design for RFID tag application at the UHF band. The ABC variants include the Improved ABC (I-ABC), which is an improved version of the original ABC algorithm. The I-ABC introduces the best-so-far solution, inertia weight and acceleration coefficients to modify the search process. Furthermore, another ABC variant is the Gbest ABC (ABC), which includes global best (gbest) solution information into the search equation to improve the exploitation. These algorithms are applied to antenna design where the optimization goals are antenna size minimization, gain maximization, and conjugate matching. The algorithms performance is compared with other popular evolutionary algorithms. The optimization results produced show that the ABC family of algorithms is a powerful tool that can be efficiently applied to antenna design problems.

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