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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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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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Sharma, Harish, Jagdish Chand Bansal, K. V. Arya, and Kusum Deep. "Dynamic Swarm Artificial Bee Colony Algorithm." International Journal of Applied Evolutionary Computation 3, no. 4 (October 2012): 19–33. http://dx.doi.org/10.4018/jaec.2012100102.
Повний текст джерелаАнотація:
Artificial Bee Colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over test problems as well as real world optimization problems. This paper presents an attempt to modify ABC to make it less susceptible to stick at local optima and computationally efficient. In the case of local convergence, addition of some external potential solutions may help the swarm to get out of the local valley and if the algorithm is taking too much time to converge then deletion of some swarm members may help to speed up the convergence. Therefore, in this paper a dynamic swarm size strategy in ABC is proposed. The proposed strategy is named as Dynamic Swarm Artificial Bee Colony algorithm (DSABC). To show the performance of DSABC, it is tested over 16 global optimization problems of different complexities and a popular real world optimization problem namely Lennard-Jones potential energy minimization problem. The simulation results show that the proposed strategies outperformed than the basic ABC and three recent variants of ABC, namely, the Gbest-Guided ABC, Best-So-Far ABC and Modified ABC.
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Chen, Gonggui, Siyuan Qiu, Zhizhong Zhang, Zhi Sun, and Honghua Liao. "Optimal Power Flow Using Gbest-Guided Cuckoo Search Algorithm with Feedback Control Strategy and Constraint Domination Rule." Mathematical Problems in Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/9067520.
Повний текст джерелаАнотація:
The optimal power flow (OPF) is well-known as a significant optimization tool for the security and economic operation of power system, and OPF problem is a complex nonlinear, nondifferentiable programming problem. Thus this paper proposes a Gbest-guided cuckoo search algorithm with the feedback control strategy and constraint domination rule which is named as FCGCS algorithm for solving OPF problem and getting optimal solution. This FCGCS algorithm is guided by the global best solution for strengthening exploitation ability. Feedback control strategy is devised to dynamically regulate the control parameters according to actual and specific feedback value in the simulation process. And the constraint domination rule can efficiently handle inequality constraints on state variables, which is superior to traditional penalty function method. The performance of FCGCS algorithm is tested and validated on the IEEE 30-bus and IEEE 57-bus example systems, and simulation results are compared with different methods obtained from other literatures recently. The comparison results indicate that FCGCS algorithm can provide high-quality feasible solutions for different OPF problems.
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Tang, Langping, Yuren Zhou, Yi Xiang, and Xinsheng Lai. "A Multi-Objective Artificial Bee Colony Algorithm Combined with a Local Search Method." International Journal on Artificial Intelligence Tools 25, no. 03 (June 2016): 1650009. http://dx.doi.org/10.1142/s0218213016500093.
Повний текст джерелаАнотація:
This paper proposes a new multi-objective artificial bee colony (ABC) algorithm called MOABCLS by combining ABC with a polynomial mutation based local search method. In this algorithm, an external archive is used to store the non-dominated solutions found so far which are maintained by the crowding distance method. A global best food source gbest is selected and used to produce new food sources in both employed and onlooker bee phases. The aim of adopting a local search is to keep good balance between exploration and exploitation. The MOABCLS is able to deal with both unconstrained and constrained problems, and it is evaluated on test functions (with up to five objectives) taken from the CEC09 competition. The performance of MOABCLS is compared with that of eight state-of-the-art multi-objective algorithms with respect to IGD metric. It is shown by the Wilcoxon test results that MOABCLS performs competitively or even better than the peer algorithms. Further experimental results clearly demonstrate MOABCLS’s ability of finding a set of well converged and appropriately distributed non-dominated solutions, and the performance promotion by introducing the local search method.
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CHONG, KET FAH, KANG NING, HON WAI LEONG, and PAVEL PEVZNER. "MODELING AND CHARACTERIZATION OF MULTI-CHARGE MASS SPECTRA FOR PEPTIDE SEQUENCING." Journal of Bioinformatics and Computational Biology 04, no. 06 (December 2006): 1329–52. http://dx.doi.org/10.1142/s021972000600248x.
Повний текст джерелаАнотація:
Peptide sequencing using tandem mass spectrometry data is an important and challenging problem in proteomics. We address the problem of peptide sequencing for multi-charge spectra. Most peptide sequencing algorithms currently consider only charge one or two ions even for higher-charge spectra. We give a characterization of multi-charge spectra by generalizing existing models. Using our models, we analyzed spectra from Global Proteome Machine (GPM) [Craig R, Cortens JP, Beavis RC, J Proteome Res3:1234–1242, 2004.] (with charges 1–5), Institute for Systems Biology (ISB) [Keller A, Purvine S, Nesvizhskii AI, Stolyar S, Goodlett DR, Kolker E, OMICS6:207–212, 2002.] and Orbitrap (both with charges 1–3). Our analysis for the GPM dataset shows that higher charge peaks contribute significantly to prediction of the complete peptide. They also help to explain why existing algorithms do not perform well on multi-charge spectra. Based on these analyses, we claim that peptide sequencing algorithms can achieve higher sensitivity results if they also consider higher charge ions. We verify this claim by proposing a de novo sequencing algorithm called the greedy best strong tag (GBST) algorithm that is simple but considers higher charge ions based on our new model. Evaluation on multi-charge spectra shows that our simple GBST algorithm outperforms Lutefisk and PepNovo, especially for the GPM spectra of charge three or more.
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Salido, Miguel A., Adriana Giret, Christian Perez, and Carlos March. "Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach." International FLAIRS Conference Proceedings 36 (May 8, 2023). http://dx.doi.org/10.32473/flairs.36.133368.
Повний текст джерелаАнотація:
Particle Swarm Optimization is a metaheuristic optimization algorithm inspired by the collective behavior of animal swarms where a set of candidate solutions, called particles, are randomly initialized in the search space, and their movements are iteratively updated based on their individual best solutions and the global best solution found by the swarm. This paper proposes a Multi-Swarm rooster colony algorithm (RCA) that considers a set of roosters, each owning a group of hens to compose a team. Each team (rooster and its hens) competes for the resource (food) with the other teams. From the combinatorial optimization point of view, each team analyzes part of the search space by an independent PSO algorithm with the same objective function. The RCA algorithm concurrently executes all PSO algorithms with different inertial weights for exploring different regions and the best solution (Gbest) of each team will compose the initial population for a new further centralized PSO algorithm that will exploit the previous solutions to search for the optimal one. Thus, the proposed RCA is composed of two steps, based on exploration and exploitation strategies to find an optimized solution in the search space. The results show that the proposed algorithm is competitive in solving well-known optimization functions. The objective is to apply this technique to solving real-life scheduling problems.
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"Preface." IOP Conference Series: Earth and Environmental Science 1274, no. 1 (December 1, 2023): 011001. http://dx.doi.org/10.1088/1755-1315/1274/1/011001.
Повний текст джерелаАнотація:
THE INTERNATIONAL GRADUATE CONFERENCE OF BUILT ENVIRONMENT AND SURVEYING (GBES 2023) Greetings from the International Graduate Conference of Built Environment and Surveying (GBES 2023). GBES 2023 is delighted to extend a warm welcome to all industry professionals, academics, and researchers who share a passion for advancing the fields of built environment and surveying to grasp this proceeding. The papers in this proceeding were presented in the GBES 2023. Hosted by the Faculty of Built Environment and Surveying at Universiti Teknologi Malaysia (UTM), in collaboration with the Post Graduate Student Society, GBES 2023 promises to be an enlightening and transformative experience. With the theme of “Innovating Solutions in Built Environment and Surveying”, this conference is designed to be a beacon of innovation and knowledge exchange in the fields that shape the world we live in. The conference was held on September 17 to 18th, 2023, offers a dual-platform experience. Participants have the choice of joining us physically at the esteemed Faculty of Built Environment and Surveying in Johor Bahru, Malaysia, or virtually via the Webex platform, ensuring accessibility to a global audience. Our primary goal is to foster an environment of collaborative learning and ideas sharing. GBES 2023 serves as a conduit for the dissemination of cutting-edge research, industry insights, and best practises across a broad spectrum of built environment and surveying disciplines. The diverse range of themes and topics, encompassing geoinformation, urban and regional planning, quantity surveying, real estate, architecture, and landscape architecture, guarantees that each participant will find valuable knowledge and inspiration. At GBES 2023, we envision a platform where professionals, scholars, and students can explore the latest developments, identify emerging trends, and lay the foundations for future growth areas in the built environment and surveying sectors. This conference offers a unique opportunity for participants to showcase their expertise, network with like-minded individuals, and engage in discussions that have the potential to shape the future of these industries. Thank you for being part of GBES 2023. Together, we will innovate, inspire, and contribute to the advancement of the built environment and surveying fields. Best regards, ASSOCIATE PROFESSOR TS. GS. SR DR. MUHAMAD UZNIR UJANG Conference Chair, GBES 2023 Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM) List of Conference Programme Committee, Keynote Speakers, Conference Sessions, Participants of the Conference are available in this Pdf.