Log in

Relevant bibliographies by topics / PSO (Particle Swarm Optimization) discrete / Journal articles

To see the other types of publications on this topic, follow the link: PSO (Particle Swarm Optimization) discrete.

Journal articles on the topic 'PSO (Particle Swarm Optimization) discrete'

Author: Grafiati

Published: 4 June 2025

Last updated: 14 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'PSO (Particle Swarm Optimization) discrete.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Wang, Bei Zhan, Xiang Deng, Wei Chuan Ye, and Hai Fang Wei. "Study on Discrete Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 220-223 (November 2012): 1787–94. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1787.

Full text

Abstract:

The particle swarm optimization (PSO) algorithm is a new type global searching method, which mostly focus on the continuous variables and little on discrete variables. The discrete forms and discretized methods have received more attention in recent years. This paper introduces the basic principles and mechanisms of PSO algorithm firstly, then points out the process of PSO algorithm and depict the operation rules of discrete PSO algorithm. Various improvements and applications of discrete PSO algorithms are reviewed. The mechanisms and characteristics of two different discretized strategies ar

APA, Harvard, Vancouver, ISO, and other styles

2

Ting, T. O., H. C. Ting, and T. S. Lee. "Taguchi-Particle Swarm Optimization for Numerical Optimization." International Journal of Swarm Intelligence Research 1, no. 2 (2010): 18–33. http://dx.doi.org/10.4018/jsir.2010040102.

Full text

Abstract:

In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a mor

APA, Harvard, Vancouver, ISO, and other styles

3

Wu, Hua Li, Jin Hua Wu, and Ai Li Liu. "Hybrid Discrete Particle Swarm Optimizer Algorithm for Traveling Salesman Problem." Advanced Materials Research 433-440 (January 2012): 4526–29. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4526.

Full text

Abstract:

PSO has been widely used in continuous optimization problems, but in discrete domain the research and application is very little. By redefining the position and speed of particles and related operations, the discrete particle swarm algorithm can be constructed. Due to the weak capacity of local search of PSO and be easy to constringe the local optimum, it is combined with simulated annealing and the hybrid discrete PSO is constructed using the characteristics that simulated annealing can accept some ungraded solution under the control of certain probability,finally the algorithm is applied to

APA, Harvard, Vancouver, ISO, and other styles

4

Zhang, Jun Ting, and Li Xia Qiao. "Optimization Mechanism Control Strategy of Vehicle Routing Problem Based on Improved PSO." Advanced Materials Research 681 (April 2013): 130–36. http://dx.doi.org/10.4028/www.scientific.net/amr.681.130.

Full text

Abstract:

Traveling salesman problem based on vehicle routing problem in the case, according to the discrete domain specificity, redefine the problem domain to the mapping relationship between particles and related operation rules, and the introduction of self learning operator so that the PSO algorithm can deal with discrete problem. Vehicle Routing Problem (VRP) is research on how to plan the vehicles routes in order to save the transportation cost. Improved Particle Swarm Optimization (PSO) algorithm is proposed to solve the VRP in this paper. To improve the efficiency of the Particle Swarm Optimizat

APA, Harvard, Vancouver, ISO, and other styles

5

Goudos, Sotirios K., Zaharias D. Zaharis, and Konstantinos B. Baltzis. "Particle Swarm Optimization as Applied to Electromagnetic Design Problems." International Journal of Swarm Intelligence Research 9, no. 2 (2018): 47–82. http://dx.doi.org/10.4018/ijsir.2018040104.

Full text

Abstract:

Particle swarm optimization (PSO) is a swarm intelligence algorithm inspired by the social behavior of birds flocking and fish schooling. Numerous PSO variants have been proposed in the literature for addressing different problem types. In this article, the authors apply different PSO variants to common design problems in electromagnetics. They apply the Inertia Weight PSO (IWPSO), the Constriction Factor PSO (CFPSO), and the Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithms to real-valued optimization problems, i.e. microwave absorber design, and linear array synthesis. Mo

APA, Harvard, Vancouver, ISO, and other styles

6

Bratton, Dan, and Tim Blackwell. "A Simplified Recombinant PSO." Journal of Artificial Evolution and Applications 2008 (February 19, 2008): 1–10. http://dx.doi.org/10.1155/2008/654184.

Full text

Abstract:

Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding how a particle swarm optimization (PSO) swarm functions. One of these forms, PSO with discrete recombination, is extended and analyzed, demonstrating not just improvements in performance relative to a standard PSO algorithm, but also significantly different behavior, namely, a reduction in bursting patterns due to the removal of stochastic components from the update equations.

APA, Harvard, Vancouver, ISO, and other styles

7

R. B., Madhumala, Harshvardhan Tiwari, and Devaraj Verma C. "Resource Optimization in Cloud Data Centers Using Particle Swarm Optimization." International Journal of Cloud Applications and Computing 12, no. 2 (2022): 1–12. http://dx.doi.org/10.4018/ijcac.305856.

Full text

Abstract:

To meet the ever-growing demand for computational resources, it is mandatory to have the best resource allocation algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is used to address the resource optimization problem. Particle Swarm Optimization is suitable for continuous data optimization, to use in discrete data as in the case of Virtual Machine placement we need to fine-tune some of the parameters in Particle Swarm Optimization. The Virtual Machine placement problem is addressed by our proposed model called Improved Particle Swarm Optimization (IM-PSO), where the main ai

APA, Harvard, Vancouver, ISO, and other styles

8

Huang, Dan Hua, and Su Wang. "An Improved Discrete Particle Swarm Optimization for Berth Scheduling Problem." Applied Mechanics and Materials 373-375 (August 2013): 1192–95. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1192.

Full text

Abstract:

Berth scheduling operation is an important problem in container terminal. The mathematic model of this problem is described in this paper and an improved particle swarm optimization algorithm is introduced to obtain the optimal scheduling solution. A floating-point allocation rule is used to encode the particles in the discrete space. A local search method is combined with PSO to avoid precocity. Finally the experiments are done to prove the improved PSO in this paper can resolve the berth scheduling problem and get better solution and convergence speed than the basic PSO.

APA, Harvard, Vancouver, ISO, and other styles

9

Abdel-Kader, Rehab F. "Particle Swarm Optimization for Constrained Instruction Scheduling." VLSI Design 2008 (March 15, 2008): 1–7. http://dx.doi.org/10.1155/2008/930610.

Full text

Abstract:

Instruction scheduling is an optimization phase aimed at balancing the performance-cost tradeoffs of the design of digital systems. In this paper, a formal framework is tailored in particular to find an optimal solution to the resource-constrained instruction scheduling problem in high-level synthesis. The scheduling problem is formulated as a discrete optimization problem and an efficient population-based search technique; particle swarm optimization (PSO) is incorporated for efficient pruning of the solution space. As PSO has proven to be successful in many applications in continuous optimiz

APA, Harvard, Vancouver, ISO, and other styles

10

Feng, Hong Kui, Jin Song Bao, and Jin Ye. "Particle Swarm Optimization Combined with Ant Colony Optimization for the Multiple Traveling Salesman Problem." Materials Science Forum 626-627 (August 2009): 717–22. http://dx.doi.org/10.4028/www.scientific.net/msf.626-627.717.

Full text

Abstract:

A lot of practical problem, such as the scheduling of jobs on multiple parallel production lines and the scheduling of multiple vehicles transporting goods in logistics, can be modeled as the multiple traveling salesman problem (MTSP). Due to the combinatorial complexity of the MTSP, it is necessary to use heuristics to solve the problem, and a discrete particle swarm optimization (DPSO) algorithm is employed in this paper. Particle swarm optimization (PSO) in the continuous space has obtained great success in resolving some minimization problems. But when applying PSO for the MTSP, a difficul

APA, Harvard, Vancouver, ISO, and other styles

11

Bartoccini, Umberto, Arturo Carpi, Valentina Poggioni, and Valentino Santucci. "Memes Evolution in a Memetic Variant of Particle Swarm Optimization." Mathematics 7, no. 5 (2019): 423. http://dx.doi.org/10.3390/math7050423.

Full text

Abstract:

In this work, a coevolving memetic particle swarm optimization (CoMPSO) algorithm is presented. CoMPSO introduces the memetic evolution of local search operators in particle swarm optimization (PSO) continuous/discrete hybrid search spaces. The proposed solution allows one to overcome the rigidity of uniform local search strategies when applied to PSO. The key contribution is that memes provides each particle of a PSO scheme with the ability to adapt its exploration dynamics to the local characteristics of the search space landscape. The objective is obtained by an original hybrid continuous/d

APA, Harvard, Vancouver, ISO, and other styles

12

Mühlenthaler, Moritz, and Alexander Raß. "Runtime analysis of discrete particle swarm optimization algorithms: A survey." it - Information Technology 61, no. 4 (2019): 177–85. http://dx.doi.org/10.1515/itit-2019-0009.

Full text

Abstract:

Abstract A discrete particle swarm optimization (PSO) algorithm is a randomized search heuristic for discrete optimization problems. A fundamental question about randomized search heuristics is how long it takes, in expectation, until an optimal solution is found. We give an overview of recent developments related to this question for discrete PSO algorithms. In particular, we give a comparison of known upper and lower bounds of expected runtimes and briefly discuss the techniques used to obtain these bounds.

APA, Harvard, Vancouver, ISO, and other styles

13

Li, Jiu Yong, and Jing Wang. "New Discrete Particle Swarm Algorithm for Traveling Salesman Problem." Advanced Materials Research 148-149 (October 2010): 210–14. http://dx.doi.org/10.4028/www.scientific.net/amr.148-149.210.

Full text

Abstract:

In this paper, a novel algorithm called CIPSO for short based on particle optimization algorithm(PSO) and Chaos optimization Algorithm(COA) is presented to solve traveling salesman problem(TSP). We propose some new operators to solve the difficulties of implementing PSO into solving this discrete problem based on the special fitness landscape of TSP. Meanwhile embedded with chaos theory it can enhance particles’ global searching ability so as not to converge to the local optimal solution too quickly, and the introduction of information intercourse can enhance thire local searching ability. Com

APA, Harvard, Vancouver, ISO, and other styles

14

Wu, Yanmin, and Qipeng Song. "Improved Particle Swarm Optimization Algorithm in Power System Network Reconfiguration." Mathematical Problems in Engineering 2021 (March 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/5574501.

Full text

Abstract:

With the rapid development of the social economy, the rapid development of all social circles places higher demands on the electricity industry. As a fundamental industry supporting the salvation of the national economy, society, and human life, the electricity industry will face a significant improvement and the restructuring of the network as an important part of the power system should also be optimised. This paper first introduces the development history of swarm intelligence algorithm and related research work at home and abroad. Secondly, it puts forward the importance of particle swarm

APA, Harvard, Vancouver, ISO, and other styles

15

He, Fang Guo. "Research on Information Applied Technology with Swarm Intelligence for the TSP Problem." Advanced Materials Research 886 (January 2014): 584–88. http://dx.doi.org/10.4028/www.scientific.net/amr.886.584.

Full text

Abstract:

As a swarm intelligence algorithm, particle swarm optimization (PSO) has received increasing attention and wide applications in information applied technology. This paper investigates the application of PSO algorithm to the traveling salesman problem (TSP) on applied technology. Proposing the concepts of swap operator and swap sequence, we present a discrete PSO algorithm by redefinition of the equation for the particles velocity. A computational experiment is reported. The results show that the method proposed in this paper can achieve good results.

APA, Harvard, Vancouver, ISO, and other styles

16

Qi-Wen Zhang, Qi-Wen Zhang, and Qiao-Hong Bai Qi-Wen Zhang. "A Discrete Particle Swarm Optimization Algorithm Based on Neighbor Cognition to Solve the Problem of Social Influence Maximization." 電腦學刊 33, no. 4 (2022): 107–19. http://dx.doi.org/10.53106/199115992022083304009.

Full text

Abstract:

<p>In view of the problem that the estimation method of node influence in social network is not comprehen-sive and the Particle Swarm Optimization (PSO) algorithm is easy to fall into the local optimal and the lo-cal search ability is insufficient. In this paper, we proposed a Neighbor Cognitive Discrete Particle Swarm Optimization (NCDPSO) algorithm. Aiming at the problem of influence in social networks, a new node influence measure method is proposed, the three-degree theory is introduced to comprehensively estimate the influence of nodes. In order to improve the global search ability

APA, Harvard, Vancouver, ISO, and other styles

17

G.Lakshmi, Kameswari. "Optimality Test cases of Particle Swarm optimization of single objective functions." Journal of Applied Mathematics and Statistical Analysis 1, no. 1 (2020): 1–9. https://doi.org/10.5281/zenodo.4010485.

Full text

Abstract:

<em>Optimization problems are classified into continuous, discrete, constrained, unconstrained deterministic, stochastic, single objective and multi-objective optimization problems. Deterministic, Heuristics and Meta-Heuristic technique, mostly dominate the solution set of small and medium scale problems, whereas for large data class optimization problems, Evolutionary techniques ( mostly derivative -free) are used to address the near -optimal solution of these class of P, N-P, N-P Hard problems. In the present paper, evolutionary algorithmic approach without evolutionary operators, mimicking

APA, Harvard, Vancouver, ISO, and other styles

18

Witkowski, Tadeusz. "Particle swarm optimization and discrete artificial bee colony algorithms for solving production scheduling problems." Technical Sciences 1, no. 22 (2019): 61–74. http://dx.doi.org/10.31648/ts.4348.

Full text

Abstract:

This paper shows the use of Discrete Artificial Bee Colony (DABC) and Particle Swarm Optimization (PSO) algorithm for solving the job shop scheduling problem (JSSP) with the objective of minimizing makespan. The Job Shop Scheduling Problem is one of the most difficult problems, as it is classified as an NP-complete one. Stochastic search techniques such as swarm and evolutionary algorithms are used to find a good solution. Our objective is to evaluate the efficiency of DABC and PSO swarm algorithms on many tests of JSSP problems. DABC and PSO algorithms have been developed for solving real pro

APA, Harvard, Vancouver, ISO, and other styles

19

Chen, Qinglong, Yong Peng, Miao Zhang, and Quanjun Yin. "Application Analysis on PSO Algorithm in the Discrete Optimization Problems." Journal of Physics: Conference Series 2078, no. 1 (2021): 012018. http://dx.doi.org/10.1088/1742-6596/2078/1/012018.

Full text

Abstract:

Abstract Particle Swarm Optimization (PSO) is kind of algorithm that can be used to solve optimization problems. In practice, many optimization problems are discrete but PSO algorithm was initially designed to meet the requirements of continuous problems. A lot of researches had made efforts to handle this case and varieties of discrete PSO algorithms were proposed. However, these algorithms just focus on the specific problem, and the performance of it significantly degrades when extending the algorithm to other problems. For now, there is no reasonable unified principle or method for analyzin

APA, Harvard, Vancouver, ISO, and other styles

20

Zhang, Yudong, Shuihua Wang, and Genlin Ji. "A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications." Mathematical Problems in Engineering 2015 (2015): 1–38. http://dx.doi.org/10.1155/2015/931256.

Full text

Abstract:

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algor

APA, Harvard, Vancouver, ISO, and other styles

21

Liu, Nan Ping, Fei Zheng, and Ke Wen Xia. "CDMA Multiuser Detection Based on Improved Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 50-51 (February 2011): 3–7. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.3.

Full text

Abstract:

CDMA multiuser detection (MUD) is a crucial technique to mobile communication. We adopt improved particle swarm optimization (PSO) algorithm in MUD which incorporates factor and utilizes function to discrete PSO. Comparison of BER and near-far effect has verified its effectiveness on multi-access interference (MAI). The algorithm accelerates the convergent speed meanwhile it also displays feasibility and superiority in case simulation.

APA, Harvard, Vancouver, ISO, and other styles

22

Rapaic, Milan, Zeljko Kanovic, and Zoran Jelicic. "Discrete particle swarm optimization algorithm for solving optimal sensor deployment problem." Journal of Automatic Control 18, no. 1 (2008): 9–14. http://dx.doi.org/10.2298/jac0801009r.

Full text

Abstract:

This paper addresses the Optimal Sensor Deployment Problem (OSDP). The goal is to maximize the probability of target detection, with simultaneous cost minimization. The problem is solved by the Discrete PSO (DPSO) algorithm, a novel modification of the PSO algorithm, originally presented in the current paper. DPSO is general-purpose optimizer well suited for conducting search within a discrete search space. Its applicability is not limited to OSDP, it can be used to solve any combinatorial and integer programming problem. The effectiveness of the DPSO in solving OSDP was demonstrated on severa

APA, Harvard, Vancouver, ISO, and other styles

23

Yang, Jia-Quan, Chun-Hua Chen, Jian-Yu Li, Dong Liu, Tao Li, and Zhi-Hui Zhan. "Compressed-Encoding Particle Swarm Optimization with Fuzzy Learning for Large-Scale Feature Selection." Symmetry 14, no. 6 (2022): 1142. http://dx.doi.org/10.3390/sym14061142.

Full text

Abstract:

Particle swarm optimization (PSO) is a promising method for feature selection. When using PSO to solve the feature selection problem, the probability of each feature being selected and not being selected is the same in the beginning and is optimized during the evolutionary process. That is, the feature selection probability is optimized from symmetry (i.e., 50% vs. 50%) to asymmetry (i.e., some are selected with a higher probability, and some with a lower probability) to help particles obtain the optimal feature subset. However, when dealing with large-scale features, PSO still faces the chall

APA, Harvard, Vancouver, ISO, and other styles

24

Wang, Yong Sheng, Jun Li Li, and Yang Lou. "A Novel Centroid Particle Swarm Optimization Algorithm Based on Two Subpopulations." Applied Mechanics and Materials 29-32 (August 2010): 929–33. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.929.

Full text

Abstract:

This paper proposed the concept of centroid in particle swarm optimization which is similar to physical centroid properties of objects. Similarly, we may think of a particle swarm as a discrete system of particles and find the centroid representing the entire population. Usually, it has a more promising position than worse particles among the population. In order to verify the role of centroid which can speed up the convergence rate of the algorithm, and prevent the algorithm from being trapped into a local solution early as far as possible at the same time, A Novel Centroid Particle Swarm Opt

APA, Harvard, Vancouver, ISO, and other styles

25

Zhu, Jian, Jianhua Liu, Yuxiang Chen, Xingsi Xue, and Shuihua Sun. "Binary Restructuring Particle Swarm Optimization and Its Application." Biomimetics 8, no. 2 (2023): 266. http://dx.doi.org/10.3390/biomimetics8020266.

Full text

Abstract:

Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RPSO for solving discrete optimization problems, this paper proposes the binary Restructuring Particle Swarm Optimization (BRPSO) algorithm. Unlike other binary metaheuristic algorithms, BRPSO does not utilize the transfer function. The particle updating process in BRPSO relies solely on comparison re

APA, Harvard, Vancouver, ISO, and other styles

26

Elilraja, D., and Sundaravel Vijayan. "Particle Swarm Optimization for Integrated Fixture Layout." Applied Mechanics and Materials 787 (August 2015): 285–90. http://dx.doi.org/10.4028/www.scientific.net/amm.787.285.

Full text

Abstract:

Fixture is a work-holding or supporting device used in the manufacturing industry to hold the workpiece. Fixtures are used to securely locate (position in a specific location or orientation) and support the work, ensuring that all parts produced using the fixture will maintain conformity and interchangeability. The location of fixture elements is called as fixture layout. The fixture layout plays major role in the work piece deformation during the machining operation. Hence optimization of fixture layout to minimize the work piece deformation is one of the critical aspects in the fixture desig

APA, Harvard, Vancouver, ISO, and other styles

27

Ma, Qing, Zhi Jun Long, Chang Hong Deng, Miao Li, Jia Bin You, and Yong Xiao. "Applications of Cloud Model Migration Particle Swarm Optimization and Gaussian Penalty Function in Reactive Power Optimization." Advanced Materials Research 986-987 (July 2014): 1365–69. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1365.

Full text

Abstract:

In order to cope with the defects of traditional particle swarm optimization (PSO) algorithm, such as its prematurity and deficiency in global optimization, a cloud model migration particle swarm optimization (CMMPSO) algorithm is proposed. Firstly, the X-condition generator based on Cloud model is introduced to adjust the inertia weights of particles; then migration action is implemented to lead the flight of global optimal particle. In allusion to the mixed integer programming problem of reactive power optimization, discrete variables are treated as continuous variables in early iterations,

APA, Harvard, Vancouver, ISO, and other styles

28

Deroussi, Laurent, and David Lemoine. "Discrete Particle Swarm Optimization for the Multi-Level Lot-Sizing Problem." International Journal of Applied Metaheuristic Computing 2, no. 1 (2011): 44–57. http://dx.doi.org/10.4018/jamc.2011010104.

Full text

Abstract:

This paper presents a Discrete Particle Swarm Optimization (DPSO) approach for the Multi-Level Lot-Sizing Problem (MLLP), which is an uncapacitated lot sizing problem dedicated to materials requirements planning (MRP) systems. The proposed DPSO approach is based on cost modification and uses PSO in its original form with continuous velocity equations. Each particle of the swarm is represented by a matrix of logistic costs. A sequential approach heuristic, using Wagner-Whitin algorithm, is used to determine the associated production planning. The authors demonstrate that any solution of the MLL

APA, Harvard, Vancouver, ISO, and other styles

29

Hong, Lu, and Jing Yang. "A Novel Immune-PSO Algorithm for Job Shop Scheduling." Advanced Materials Research 129-131 (August 2010): 261–65. http://dx.doi.org/10.4028/www.scientific.net/amr.129-131.261.

Full text

Abstract:

The job shop scheduling problem (JSSP) is one of the most difficult problems, as it is classified as an NP-complete one. Particle Swarm Optimization, a nature-inspired evolutionary algorithm, has been successful in solving a wide range of real-value optimization problems. However, little attempts have been made to extend it to discrete problems. In this paper, a new particle swarm optimization method based on the clonal selection algorithm is proposed to avoid premature convergence and guarantee the diversity of the population. Experimental results indicate that the proposed algorithm is highl

APA, Harvard, Vancouver, ISO, and other styles

30

Wang, Miao, Yuhua Huang, and Jindong Zhang. "Current Situation and Development of Advanced Planning and Scheduling System Based on Group Optimization Algorithm in Discrete Industry." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 215–20. http://dx.doi.org/10.54097/hset.v23i.3270.

Full text

Abstract:

Discrete industry, especially job shop scheduling, has always been the key industry of Advanced Planning and Scheduling system (APS) system application. Based on Particle Swarm optimization (PSO), this paper introduces the Group swarm optimization algorithm, expounds the relevant theory and development status of APS, introduces the application of Particle Swarm optimization and Artificial Bee Colony optimization algorithm in APS system, and analyzes the performance and efficiency of the two algorithms. Finally, it predicts the future development trend of APS: the core algorithm will adopt a va

APA, Harvard, Vancouver, ISO, and other styles

31

He, Gang, Zhi Gang Zhang, Deng Lin Zhu, and Zheng Yu Pan. "The Particle Swarm Optimization Method Used in the Design of Two-Stage Cylindrical Helical Gear Reducer." Applied Mechanics and Materials 80-81 (July 2011): 1086–90. http://dx.doi.org/10.4028/www.scientific.net/amm.80-81.1086.

Full text

Abstract:

The design optimization of two-stage cylindrical helical gear reducer is discussed in this paper. The volume and the center gear distance are adopted as objective functions separately, and the particle swarm optimization(PSO) method is used to improve the design quality. The design variables contain discrete variables, which are converted to discrete variables by the nearby principle in order to improve the running efficiency. Inertia weight coefficient is used in the PSO algorithm to adjust the coverage speed. The optimization result shows that our method is better than other optimization met

APA, Harvard, Vancouver, ISO, and other styles

32

Gui, Hang, Ruisheng Sun, Wei Chen, and Bin Zhu. "Reaction Control System Optimization for Maneuverable Reentry Vehicles Based on Particle Swarm Optimization." Discrete Dynamics in Nature and Society 2020 (March 12, 2020): 1–11. http://dx.doi.org/10.1155/2020/6518531.

Full text

Abstract:

This paper presents a new parametric optimization design to solve a class of reaction control system (RCS) problem with discrete switching state, flexible working time, and finite-energy control for maneuverable reentry vehicles. Based on basic particle swarm optimization (PSO) method, an exponentially decreasing inertia weight function is introduced to improve convergence performance of the PSO algorithm. Considering the PSO algorithm spends long calculation time, a suboptimal control and guidance scheme is developed for online practical design. By tuning the control parameters, we try to acq

APA, Harvard, Vancouver, ISO, and other styles

33

Dou, Jianping, Jun Li, and Xia Zhao. "A novel discrete particle swarm algorithm for assembly line balancing problems." Assembly Automation 37, no. 4 (2017): 452–63. http://dx.doi.org/10.1108/aa-08-2016-104.

Full text

Abstract:

Purpose The purpose of this paper is to develop a feasible sequence-oriented new discrete particle swarm optimization (NDPSO) algorithm with novel particles’ updating mechanism for solving simple assembly line balancing problems (SALBPs). Design/methodology/approach In the NDPSO, a task-oriented representation is adopted to solve type I and type II SALBPs, and a particle directly represents a feasible task sequence (FTS) as a permutation. Then, the particle (permutation) is updated as a whole using the geometric crossover based on the edit distance with swaps for two permutations. Furthermore,

APA, Harvard, Vancouver, ISO, and other styles

34

Kim, T.-H., I. Maruta, and T. Sugie. "A simple and efficient constrained particle swarm optimization and its application to engineering design problems." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 2 (2010): 389–400. http://dx.doi.org/10.1243/09544062jmes1732.

Full text

Abstract:

Engineering optimization problems usually contain various constraints and mixed integer-discrete-continuous type of design variables. This article proposes an efficient particle swarm optimization (PSO) algorithm for such problems. First, the constrained optimization problem is transformed into an unconstrained problem without introducing any problem-dependent or user-defined parameters such as penalty factors or Lagrange multipliers, though such parameters are usually required in general optimization algorithms. Then, the above PSO method is extended to handle integer, discrete, and continuou

APA, Harvard, Vancouver, ISO, and other styles

35

Lin, Shanxian, Yifei Yang, Yuichi Nagata, and Haichuan Yang. "Elite Evolutionary Discrete Particle Swarm Optimization for Recommendation Systems." Mathematics 13, no. 9 (2025): 1398. https://doi.org/10.3390/math13091398.

Full text

Abstract:

Recommendation systems (RSs) play a vital role in e-commerce and content platforms, yet balancing efficiency and recommendation quality remains challenging. Traditional deep models are computationally expensive, while heuristic methods like particle swarm optimization struggle with discrete optimization. To address these limitations, this paper proposes elite-evolution-based discrete particle swarm optimization (EEDPSO), a novel framework specifically designed to optimize high-dimensional combinatorial recommendation tasks. EEDPSO restructures the velocity and position update mechanisms to ope

APA, Harvard, Vancouver, ISO, and other styles

36

Yang, Bin, and Qi Lin Zhang. "A Parellelzing Modified Particle Swarm Optimizer and its Application to Discrete Topological Optimization." Advanced Materials Research 433-440 (January 2012): 4401–8. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4401.

Full text

Abstract:

recently, a modified Particle Swarm Optimizer (MLPSO) has been succeeded in solving truss topological optimization problems and competitive results are obtained. In order to reduce its execution time for solving large complex optimization problem, a parallel version for this optimizer (PMLPSO) is studied in this paper. This paper first gives an overview of PSO algorithm as well as the modified PSO, and then a design and an implementation of parallel PSO is proposed. Since most of structural problems involve discrete design variables, an effect strategy is involved in MLPSO in order to operate

APA, Harvard, Vancouver, ISO, and other styles

37

Tseng, K.-Y., C.-B. Zhang, and C.-Y. Wu. "An Enhanced Binary Particle Swarm Optimization for Structural Topology Optimization." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 10 (2010): 2271–87. http://dx.doi.org/10.1243/09544062jmes2128.

Full text

Abstract:

Particle swarm optimization (PSO), a heuristic optimization method, has been successfully applied in solving many optimization problems in real-value search space. The original binary particle swarm optimization (BPSO) uses the concept of bit flipping of the binary string to convert the velocity from a real code into a binary code. However, the conversion process cannot be reversed, and it is difficult to extend this framework to solve certain discrete optimization problems. An enhanced binary particle swarm algorithm is proposed in this study based on pure binary bit-string frameworks to deal

APA, Harvard, Vancouver, ISO, and other styles

38

Tan, Lin, Ailing Zhang, Sha Li, Minghua Ding, and Pengfei Liu. "Design and Simulation of Logistics Network Model Based on Particle Swarm Optimization Algorithm." Computational Intelligence and Neuroscience 2022 (July 8, 2022): 1–7. http://dx.doi.org/10.1155/2022/1862911.

Full text

Abstract:

With the continuous development of e-commerce, logistics and express services have penetrated into every aspect of people’s life. Research on the optimization of logistics network model is helpful to reduce the waste of routes, improve the utilization rate of transportation tools and hubs, and thus reduce the organizational cost of logistics. In this paper, the basic model of hub-and-spoke network (HSN) is constructed based on the principle of minimizing the connection distance and total cost between hubs. By discretizing the particles in the continuous motion space, the discrete particle swar

APA, Harvard, Vancouver, ISO, and other styles

39

Lu, Yingtong, Yaofei Ma, Jiangyun Wang, and Liang Han. "Task Assignment of UAV Swarm Based on Wolf Pack Algorithm." Applied Sciences 10, no. 23 (2020): 8335. http://dx.doi.org/10.3390/app10238335.

Full text

Abstract:

To perform air missions with an unmanned aerial vehicle (UAV) swarm is a significant trend in warfare. The task assignment among the UAV swarm is one of the key issues in such missions. This paper proposes PSO-GA-DWPA (discrete wolf pack algorithm with the principles of particle swarm optimization and genetic algorithm) to solve the task assignment of a UAV swarm with fast convergence speed. The PSO-GA-DWPA is confirmed with three different ground-attack scenarios by experiments. The comparative results show that the improved algorithm not only converges faster than the original WPA and PSO, b

APA, Harvard, Vancouver, ISO, and other styles

40

Laturkar, Aparna Pradeep, Sridharan Bhavani, and DeepaliParag Adhyapak. "Grid and Force Based Sensor Deployment Methods in Wireless Sensor Network using Particle Swarm Optimization." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 3 (2018): 1287. http://dx.doi.org/10.11591/ijeecs.v10.i3.pp1287-1295.

Full text

Abstract:

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling &amp; data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. PSO is a multidimensional op

APA, Harvard, Vancouver, ISO, and other styles

41

Aparna, Pradeep Laturkar, Bhavani Sridharan, and Adhyapak DeepaliParag. "Grid and Force Based Sensor Deployment Methods in Wireless Sensor Network using Particle Swarm Optimization." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 3 (2018): 1287–96. https://doi.org/10.11591/ijeecs.v10.i3.pp1287-1296.

Full text

Abstract:

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling & data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. PSO is a multidimensional optimi

APA, Harvard, Vancouver, ISO, and other styles

42

Huang, Song, Na Tian, and Zhicheng Ji. "Particle swarm optimization with variable neighborhood search for multiobjective flexible job shop scheduling problem." International Journal of Modeling, Simulation, and Scientific Computing 07, no. 03 (2016): 1650024. http://dx.doi.org/10.1142/s1793962316500240.

Full text

Abstract:

The simulation on benchmarks is a very simple and efficient method to evaluate the performance of the algorithm for solving flexible job shop scheduling model. Due to the assignment and scheduling decisions, flexible job shop scheduling problem (FJSP) becomes extremely hard to solve for production management. A discrete multi-objective particle swarm optimization (PSO) and simulated annealing (SA) algorithm with variable neighborhood search is developed for FJSP with three criteria: the makespan, the total workload and the critical machine workload. Firstly, a discrete PSO is designed and then

APA, Harvard, Vancouver, ISO, and other styles

43

Cai, Huayang, Ruping Zhou, Pengcheng Huang, Yidan Jing, and Genggeng Liu. "SLDPSO-TA: Track Assignment Algorithm Based on Social Learning Discrete Particle Swarm Optimization." Electronics 13, no. 22 (2024): 4571. http://dx.doi.org/10.3390/electronics13224571.

Full text

Abstract:

In modern circuit design, the short-circuit problem is one of the key factors affecting routability. With the continuous reduction in feature sizes, the short-circuit problem grows significantly in detailed routing. Track assignment, as a crucial intermediary phase between global routing and detailed routing, plays a vital role in preprocessing the short-circuit problem. However, existing track assignment algorithms face the challenge of easily falling into local optimality. As a typical swarm intelligence technique, particle swarm optimization (PSO) is a powerful tool with excellent optimizat

APA, Harvard, Vancouver, ISO, and other styles

44

Liu, Li Lan, Xue Wei Liu, Sen Wang, Wei Zhou, and Gai Ping Zhao. "Multi-Objective Optimization Algorithm for Job Shop Scheduling Problem in Discrete Manufacturing Enterprise." Applied Mechanics and Materials 741 (March 2015): 860–64. http://dx.doi.org/10.4028/www.scientific.net/amm.741.860.

Full text

Abstract:

Job Shop scheduling should satisfy the constraints of time, order and resource. To solve this NP-Hard problem, multi-optimization for job shop scheduling problem (JSSP) in discrete manufacturing plant is researched. Objective of JSSP in discrete manufacturing enterprise was analyzed, and production scheduling optimization model was constructed with the optimization goal of minimizing the bottleneck machines’ make-span and the total products’ tardiness; Then, Particle Swarm Optimization (PSO) algorithm was used to solve this model by the process-based encoding mode; To solve the premature conve

APA, Harvard, Vancouver, ISO, and other styles

45

Li, K., D. Li, and H. Q. Ma. "An improved discrete particle swarm optimization approach for a multi-objective optimization model of an urban logistics distribution network considering traffic congestion." Advances in Production Engineering & Management 18, no. 2 (2023): 211–24. http://dx.doi.org/10.14743/apem2023.2.468.

Full text

Abstract:

To optimize urban logistics networks, this paper proposes a multi-objective optimization model for urban logistics distribution networks (ULDN). The model optimizes vehicle usage costs, transportation costs, penalty costs for failing to meet time windows, and carbon emission costs, while also considering the impact of urban road traffic congestion on total costs. To solve the model, a DPSO (Discrete Particle Swarm Optimization) algorithm based on the basic principle of PSO (Particle Swarm Optimization) is proposed. The DPSO introduces multiple populations to handle multiple targets and uses a

APA, Harvard, Vancouver, ISO, and other styles

46

Wei, Cheng-Long, and Gai-Ge Wang. "Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization." Mathematics 8, no. 9 (2020): 1403. http://dx.doi.org/10.3390/math8091403.

Full text

Abstract:

The particle swarm optimization algorithm (PSO) is not good at dealing with discrete optimization problems, and for the krill herd algorithm (KH), the ability of local search is relatively poor. In this paper, we optimized PSO by quantum behavior and optimized KH by simulated annealing, so a new hybrid algorithm, named the annealing krill quantum particle swarm optimization (AKQPSO) algorithm, is proposed, and is based on the annealing krill herd algorithm (AKH) and quantum particle swarm optimization algorithm (QPSO). QPSO has better performance in exploitation and AKH has better performance

APA, Harvard, Vancouver, ISO, and other styles

47

Guo, Sha-sha, Jie-sheng Wang, and Meng-wei Guo. "Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm." Computational Intelligence and Neuroscience 2020 (June 8, 2020): 1–21. http://dx.doi.org/10.1155/2020/6502807.

Full text

Abstract:

Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the conti

APA, Harvard, Vancouver, ISO, and other styles

48

Luo, Qing Yue, Ting Luo, Liu Qing Sun, and Bai Yang Liu. "A Novel Method for Optimal Capacitor Installation Location in Distribution System." Applied Mechanics and Materials 738-739 (March 2015): 1256–61. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.1256.

Full text

Abstract:

In this paper, a new approach is proposed for optimal capacitor installation location using improved particle swarm optimization (PSO) algorithm. The improved PSO can solve the optimal discrete variables problem by forming global intelligent subgroups. The proposed method is implemented on the IEEE30 standard bus system. The obtained results are then compared with the empirical method to validate its effectiveness.Keyword: PSO; distribution system; Optimal power flow; Capacitor installation location

APA, Harvard, Vancouver, ISO, and other styles

49

Aparna, Pradeep Laturkar, Bhavani Sridharan, and Adhyapak DeepaliParag. "Random, PSO & MDBPSO based Sensor Deployment in WSN." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 286–94. https://doi.org/10.11591/ijeecs.v10.i1.pp286-294.

Full text

Abstract:

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling and data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. This paper discusses sensor deploy

APA, Harvard, Vancouver, ISO, and other styles

50

Aparna, Pradeep Laturkar, Bhavani Sridharan, and Adhyapak DeepaliParag. "Random, PSO and MDBPSO based Sensor Deployment in Wireless Sensor Network." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 3 (2018): 1278–86. https://doi.org/10.11591/ijeecs.v10.i3.pp1278-1286.

Full text

Abstract:

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling and data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. This paper discusses sensor deploy

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!