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Academic literature on the topic 'Discrete Particle Swarm Optimization'

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Published: 6 September 2023

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Journal articles on the topic "Discrete Particle Swarm Optimization"

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Roy, Rahul, Satchidananda Dehuri, and Sung Bae Cho. "A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem." International Journal of Applied Metaheuristic Computing 2, no. 4 (October 2011): 41–57. http://dx.doi.org/10.4018/jamc.2011100104.

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The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.

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Xiao, Bin, and Zhao Hui Li. "An Improved Hybrid Discrete Particle Swarm Optimization Algorithm to Solve the TSP Problem." Applied Mechanics and Materials 130-134 (October 2011): 3589–94. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3589.

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Through investigating the issue of solving the TSP problem by discrete particle swarm optimization algorithm, this study finds a new discrete particle swarm optimization algorithm (NDPSO), which is easy to combine with other algorithm and has fast convergence and high accuracy, by introducing the thought of the greedy algorithm and GA algorithm and refining the discrete particle swarm optimization algorithm. And then the study expands NDPSO by Simulated Annealing algorithm and proposes a hybrid discrete particle swarm optimization algorithm (HDPSO). At last, the experiments prove that these two algorithms both have good convergence, but the HDPSO has a better capacity to find the best solution.

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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 (April 2010): 18–33. http://dx.doi.org/10.4018/jsir.2010040102.

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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 more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.

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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.

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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 are presented. Some development trends and future research directions about discrete PSO are proposed.

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Kang, Qi, Lei Wang, and Qidi Wu. "Swarm-based approximate dynamic optimization process for discrete particle swarm optimization system." International Journal of Bio-Inspired Computation 1, no. 1/2 (2009): 61. http://dx.doi.org/10.1504/ijbic.2009.022774.

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Beheshti, Zahra, Siti Mariyam Shamsuddin, and Shafaatunnur Hasan. "Memetic binary particle swarm optimization for discrete optimization problems." Information Sciences 299 (April 2015): 58–84. http://dx.doi.org/10.1016/j.ins.2014.12.016.

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Sarathambekai, S., and K. Umamaheswari. "Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem." Journal of Algorithms & Computational Technology 11, no. 1 (September 19, 2016): 58–67. http://dx.doi.org/10.1177/1748301816665521.

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Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

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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.

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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 Optimization, self-learning operator is constructed. Particles are re coded and operate rules are redefined to deal with the discrete problem of VRP. The effectiveness of the proposed algorithm is demonstrated by the simulations.

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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.

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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 optimization algorithm for power system network reconfiguration and expounds the basic principle, essential characteristics, and basic model of the particle swarm optimization algorithm. This paper completes the work of improving PSO through the common improved methods of PSO and the introduction of mutation operation and tent mapping. In the experimental simulation part, the improved particle swarm optimization algorithm is used to simulate the 10-machine 39-bus simulation system in IEEE, and the experimental data are compared with the chaos genetic algorithm and particle swarm optimization discrete algorithm. Through the experimental data, we can know that the improved particle swarm optimization algorithm has the least number of actions in switching times, only 4 times, and the chaos genetic algorithm and discrete particle swarm optimization algorithm are 5 times; compared with the other two algorithms, the improved particle swarm optimization algorithm has the fastest convergence speed and the highest convergence accuracy. The improved particle swarm optimization algorithm proposed in this paper provides an excellent solution for power system network reconfiguration and has important research significance for power system subsequent optimization and particle swarm optimization algorithm improvement.

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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 (April 1, 2022): 1–12. http://dx.doi.org/10.4018/ijcac.305856.

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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 aim is to maximize the utilization of resources in the cloud datacenter. The obtained results show that the proposed algorithm provides an optimized solution when compared to the existing algorithms.

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Dissertations / Theses on the topic "Discrete Particle Swarm Optimization"

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Djaneye-Boundjou, Ouboti Seydou Eyanaa. "Particle Swarm Optimization Stability Analysis." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386413941.

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Muthuswamy, Shanthi. "Discrete particle swarm optimization algorithms for orienteering and team orienteering problems." Diss., Online access via UMI:, 2009.

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Aminbakhsh, Saman. "Hybrid Particle Swarm Optimization Algorithm For Obtaining Pareto Front Of Discrete Time-cost Trade-off Problem." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615398/index.pdf.

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In pursuance of decreasing costs, both the client and the contractor would strive to speed up the construction project. However, accelerating the project schedule will impose additional cost and might be profitable up to a certain limit. Paramount for construction management, analyses of this trade-off between duration and cost is hailed as the time-cost trade-off (TCT) optimization. Inadequacies of existing commercial software packages for such analyses tied with eminence of discretization, motivated development of different paradigms of particle swarm optimizers (PSO) for three extensions of discrete TCT problems (DTCTPs). A sole-PSO algorithm for concomitant minimization of time and cost is proposed which involves minimal adjustments to shift focus to the completion deadline problem. A hybrid model is also developed to unravel the time-cost curve extension of DCTCPs. Engaging novel principles for evaluation of cost-slopes, and pbest/gbest positions, the hybrid SAM-PSO model combines complementary strengths of overhauled versions of the Siemens Approximation Method (SAM) and the PSO algorithm. Effectiveness and efficiency of the proposed algorithms are validated employing instances derived from the literature. Throughout computational experiments, mixed integer programming technique is implemented to introduce the optimal non-dominated fronts of two specific benchmark problems for the very first time in the literature. Another chief contribution of this thesis can be depicted as potency of SAM-PSO model in locating the entire Pareto fronts of the practiced instances, within acceptable time-frames with reasonable deviations from the optima. Possible further improvements and applications of SAM-PSO model are suggested in the conclusion.

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Devarakonda, SaiPrasanth. "Particle Swarm Optimization." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1335827032.

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Al-kazemi, Buthainah Sabeeh No'man. "Multiphase particle swarm optimization." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2002. http://wwwlib.umi.com/cr/syr/main.

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JUNQUEIRA, Caio Marco dos Santos. "Um algoritmo para alocação ótima de detectores de afundamentos de tensão." Universidade Federal de Campina Grande, 2017. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/479.

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Submitted by Lucienne Costa (lucienneferreira@ufcg.edu.br) on 2018-04-24T17:30:10Z No. of bitstreams: 1 CAIO MARCO DOS SANTOS JUNQUEIRA – DISSERTAÇÃO (PPGEE) 2017.pdf: 6011061 bytes, checksum: 25c9c9fad6015613e54aae9e700918af (MD5)
Made available in DSpace on 2018-04-24T17:30:10Z (GMT). No. of bitstreams: 1 CAIO MARCO DOS SANTOS JUNQUEIRA – DISSERTAÇÃO (PPGEE) 2017.pdf: 6011061 bytes, checksum: 25c9c9fad6015613e54aae9e700918af (MD5) Previous issue date: 2017-03-09
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Um algoritmo para alocação ótima de detectores de afundamentos de tensão (AT) é apresentado nesta dissertação. O algoritmo utiliza a Transformada Wavelet Discreta (TWD) paraa detecção de AT e o conceito de Matriz de Observabilidade Topológica (MOT) para avaliar o desempenho dos Sistemas de Distribuição de Energia Elétrica (SDEE) quando submetidos à tais distúrbios. Para resolver o problema de alocação ótima dos dispositivos detectores de AT, utilizou-se o método Binary Particle Swarm Optimi- tization (BPSO). Adicionalmente, apresenta-se uma metodologia de criação de uma base de dados para geração automática de AT. O algoritmo foi avaliado considerando-se dois sistemas: um sistema-testedo IEEE e um SDEE que simula um alimentador real da cidade de BoaVista-PB, os quais foram simulados no software Alternative Transient Pro- gram (ATP). Osresultados obtidos indicaram que o algoritmo é capaz de detectar AT em todo o sistema, fazendo o uso da instalação de detectores em poucas barras, oque indubitavelmente, reduzirá o custo do sistema de monitoramento.
An algorithm for optimal placement of voltage sags (VS) detectors is presented in this dissertation. The algorithm uses the Discrete Wavelet Transform (DWT) for VS detection and the Topological Observability Matrix (TOM) concept to evaluate the per- formance of the Electric Power Distribution Systems (EPDS) when subjected to such disturbances. In order to solve the problem of optimal placement of the VS detecting devices, the Binary Particle Swarm Optimization (BPSO) method was used. Additionally, a methodology for the creation of a database for automatic VS generation is presented. The algorithm was evaluated considering two systems: an IEEE test system and a EPDS that simulates a real feeder in Boa Vista-PB city, which were simulated in the Alternative Transient Program (ATP) software. The results indicate that the algorithm is capable of detecting VS throughout the system, using the installation of detectors in a few buses, which will undoubtedly reduce the cost of the monitoring system.

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Bernardes, Wellington Maycon Santos. "Algoritmo enxame de partículas discreto para coordenação de relés direcionais de sobrecorrente em sistemas elétricos de potência." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-14052013-094113/.

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Este trabalho propõe uma metodologia baseada em técnicas inteligentes capaz de fornecer uma coordenação otimizada de relés direcionais de sobrecorrente instalados em sistemas de energia elétrica. O problema é modelado como um caso de programação não linear inteira mista, em que os relés permitem ajustes discretizados de múltiplos de tempo e/ou múltiplos de corrente. A solução do problema de otimização correspondente é obtida através de uma metaheurística nomeada como Discrete Particle Swarm Optimization. Na literatura técnico-científica esse problema geralmente é linearizado e aplicam-se arredondamentos das variáveis discretas. Na metodologia proposta, as variáveis discretas são tratadas adequadamente para utilização na metaheurística e são apresentados os resultados que foram comparados com os obtidos pelo modelo clássico de otimização implementado no General Algebraic Modeling System (GAMS). Tendo em vista os aspectos observados, o método permite ao engenheiro de proteção ter um subsídio adicional na tarefa da coordenação dos relés direcionais de sobrecorrente, disponibilizando uma técnica eficaz e de fácil aplicabilidade ao sistema elétrico a ser protegido, independentemente da topologia e condição operacional.
This work proposes a methodology that based on intelligent technique to obtain an optimized coordination of directional overcurrent relays in electric power systems. The problem is modeled as a mixed integer nonlinear problem, because the relays allows a discrete setting of time and/or current multipliers. The solution of the proposed optimization problem is obtained from the proposed metaheuristic named as Discrete Particle Swarm Optimization. In scientific and technical literature this problem is usually linearized and discrete variables are rounded off. In the proposed method, the discrete variables are modeled adequately in the metaheuristic and the results are compared to the classical optimization solvers implemented in General Algebraic Modeling System (GAMS). The method provides an important method for helping the engineers in to coordinate directional overcurrent relays in a very optimized way. It has high potential for the application to realistic systems, regardless of topology and operating condition.

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Scheepers, Christiaan. "Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/64041.

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An exploratory analysis in low-dimensional objective space of the vector evaluated particle swarm optimization (VEPSO) algorithm is presented. A novel visualization technique is presented and applied to perform the exploratory analysis. The exploratory analysis together with a quantitative analysis revealed that the VEPSO algorithm continues to explore without exploiting the well-performing areas of the search space. A detailed investigation into the influence that the choice of archive implementation has on the performance of the VEPSO algorithm is presented. Both the Pareto-optimal front (POF) solution diversity and convergence towards the true POF is considered during the investigation. Attainment surfaces are investigated for their suitability in efficiently comparing two multi-objective optimization (MOO) algorithms. A new measure to objectively compare algorithms in multi-dimensional objective space, based on attainment surfaces, is presented. This measure, referred to as the porcupine measure, adapts the attainment surface measure by using a statistical test along with weighted intersection lines. Loosely based on the VEPSO algorithm, the multi-guided particle swarm optimization (MGPSO) algorithm is presented and evaluated. The results indicate that the MGPSO algorithm overcomes the weaknesses of the VEPSO algorithm and also outperforms a number of state of the art MOO algorithms on at least two benchmark test sets.
Thesis (PhD)--University of Pretoria, 2017.
Computer Science
PhD
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Karlberg, Hampus. "Task Scheduling Using Discrete Particle Swarm Optimisation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287311.

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Optimising task allocation in networked systems helps in utilising available resources. When working with unstable and heterogeneous networks, task scheduling can be used to optimise task completion time, energy efficiency and system reliability. The dynamic nature of networks also means that the optimal schedule is subject to change over time. The heterogeneity and variability in network design also complicate the translation of setups from one network to another. Discrete Particle Swarm Optimisation (DPSO) is a metaheuristic that can be used to find solutions to task scheduling. This thesis will explore how DPSO can be used to optimise job scheduling in an unstable network. The purpose is to find solutions for networks like the ones used on trains. This in turn is done to facilitate trajectory planning calculations. Through the use of an artificial neural network, we estimate job scheduling costs. These costs are then used by our DPSO meta heuristic to explore a solution space of potential scheduling. The results focus on the optimisation of batch sizes in relation to network reliability and latency. We simulate a series of unstable and heterogeneous networks and compare completion time. The baseline comparison is the case where scheduling is done by evenly distributing jobs at fixed sizes. The performance of the different approaches is then analysed with regards to usability in real-life scenarios on vehicles. Our results show a noticeable increase in performance within a wide range of network set-ups. This is at the cost of long search times for the DPSO algorithm. We conclude that under the right circumstances, the method can be used to significantly speed up distributed calculations at the cost of requiring significant ahead of time calculations. We recommend future explorations into DPSO starting states to speed up convergence as well as benchmarks of real-life performance.
Optimering av arbetsfördelning i nätverk kan öka användandet av tillgängliga resurser. I instabila heterogena nätverk kan schemaläggning användas för att optimera beräkningstid, energieffektivitet och systemstabilitet. Då nätverk består av sammankopplade resurser innebär det också att vad som är ett optimalt schema kan komma att ändras över tid. Bredden av nätverkskonfigurationer gör också att det kan vara svårt att överföra och applicera ett schema från en konfiguration till en annan. Diskret Particle Swarm Optimisation (DPSO) är en meta heuristisk metod som kan användas för att ta fram lösningar till schemaläggningsproblem. Den här uppsatsen kommer utforska hur DPSO kan användas för att optimera schemaläggning för instabila nätverk. Syftet är att hitta en lösning för nätverk under liknande begränsningar som de som återfinns på tåg. Detta för att i sin tur facilitera planerandet av optimala banor. Genom användandet av ett artificiellt neuralt nätverk (ANN) uppskattar vi schemaläggningskostnaden. Denna kostnad används sedan av DPSO heuristiken för att utforska en lösningsrymd med potentiella scheman. Våra resultat fokuserar på optimeringen av grupperingsstorleken av distribuerade problem i relation till robusthet och letens. Vi simulerar ett flertal instabila och heterogena nätverk och jämför deras prestanda. Utgångspunkten för jämförelsen är schemaläggning där uppgifter distribueras jämnt i bestämda gruperingsstorlekar. Prestandan analyseras sedan i relation till användbarheten i verkliga scenarion. Våra resultat visar på en signifikant ökning i prestanda inom ett brett spann av nätverkskonfigurationer. Det här är på bekostnad av långa söktider för DPSO algoritmen. Vår slutsats är att under rätt förutsättningar kan metoden användas för att snabba upp distribuerade beräkningar förutsatt att beräkningarna för schemaläggningen görs i förväg. Vi rekommenderar vidare utforskande av DPSO algoritmens parametrar för att snabba upp konvergens, samt undersökande av algoritmens prestanda i verkliga miljöer.

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Czogalla, Jens. "Particle swarm optimization for scheduling problems." Aachen Shaker, 2010. http://d-nb.info/1002307813/04.

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Books on the topic "Discrete Particle Swarm Optimization"

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Lazinica, Aleksandar. Particle swarm optimization. Rijek, Crotia: InTech, 2009.

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Mercangöz, Burcu Adıgüzel, ed. Applying Particle Swarm Optimization. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6.

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Couceiro, Micael, and Pedram Ghamisi. Fractional Order Darwinian Particle Swarm Optimization. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19635-0.

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Mikki, Said M., and Ahmed A. Kishk. Particle Swarm Optimization: A Physics-Based Approach. Cham: Springer International Publishing, 2008. http://dx.doi.org/10.1007/978-3-031-01704-9.

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Olsson, Andrea E. Particle swarm optimization: Theory, techniques, and applications. Hauppauge, N.Y: Nova Science Publishers, 2010.

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1974-, Parsopoulos Konstantinos E., and Vrahatis Michael N. 1955-, eds. Particle swarm optimization and intelligence: Advances and applications. Hershey, PA: Information Science Reference, 2010.

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Parsopoulos, Konstantinos E. Particle swarm optimization and intelligence: Advances and applications. Hershey, PA: Information Science Reference, 2010.

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Kiranyaz, Serkan, Turker Ince, and Moncef Gabbouj. Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-37846-1.

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Choi-Hong, Lai, and Wu Xiao-Jun, eds. Particle swarm optimisation: Classical and quantum perspectives. Boca Raton: CRC Press, 2011.

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Clerc, Maurice. Particle Swarm Optimization. Wiley & Sons, Incorporated, John, 2010.

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Book chapters on the topic "Discrete Particle Swarm Optimization"

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Liu, QingFeng. "An Improved Discrete Particle Swarm Optimization Algorithm." In Lecture Notes in Electrical Engineering, 883–90. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4853-1_108.

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Karthi, R., S. Arumugam, and K. Ramesh Kumar. "Discrete Particle Swarm Optimization Algorithm for Data Clustering." In Nature Inspired Cooperative Strategies for Optimization (NICSO 2008), 75–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03211-0_7.

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Akhand, M. A. H., Md Masudur Rahman, and Nazmul Siddique. "Advances on Particle Swarm Optimization in Solving Discrete Optimization Problems." In Studies in Computational Intelligence, 59–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09835-2_4.

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Tao, Qian, Hui-you Chang, Yang Yi, Chun-qin Gu, and Wen-jie Li. "A Novel Cyclic Discrete Optimization Framework for Particle Swarm Optimization." In Lecture Notes in Computer Science, 166–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14922-1_22.

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Clerc, Maurice. "Discrete Particle Swarm Optimization, illustrated by the Traveling Salesman Problem." In New Optimization Techniques in Engineering, 219–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39930-8_8.

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Garg, Ritu, and Awadhesh Kumar Singh. "Multi-objective Workflow Grid Scheduling Based on Discrete Particle Swarm Optimization." In Swarm, Evolutionary, and Memetic Computing, 183–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27172-4_23.

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Zhan, Zhi-hui, and Jun Zhang. "Discrete Particle Swarm Optimization for Multiple Destination Routing Problems." In Lecture Notes in Computer Science, 117–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01129-0_15.

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Yuan, Ying, Cuirong Wang, Cong Wang, Shiming Zhu, and Siwei Zhao. "Discrete Particle Swarm Optimization Algorithm for Virtual Network Reconfiguration." In Lecture Notes in Computer Science, 250–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38703-6_30.

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Bui, Ha-Duong, Sungmoon Jeong, Nak Young Chong, and Matthew Mason. "Origami Folding Sequence Generation Using Discrete Particle Swarm Optimization." In Neural Information Processing, 484–93. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70093-9_51.

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Zhang, Kai, Wanying Zhu, Jun Liu, and Juanjuan He. "Discrete Particle Swarm Optimization Algorithm for Solving Graph Coloring Problem." In Communications in Computer and Information Science, 643–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-49014-3_57.

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Conference papers on the topic "Discrete Particle Swarm Optimization"

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Sarif, Bambang A. B., and Mostafa Abd-El-Barr. "Functional synthesis using discrete particle swarm optimization." In 2008 IEEE Swarm Intelligence Symposium (SIS). IEEE, 2008. http://dx.doi.org/10.1109/sis.2008.4668306.

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Engelbrecht, A. P. "Asynchronous particle swarm optimization with discrete crossover." In 2014 IEEE Symposium On Swarm Intelligence (SIS). IEEE, 2014. http://dx.doi.org/10.1109/sis.2014.7011788.

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Xu, Yufa, Guochu Chen, and Jinshou Yu. "Three Sub-Swarm Discrete Particle Swarm Optimization Algorithm." In 2006 IEEE International Conference on Information Acquisition. IEEE, 2006. http://dx.doi.org/10.1109/icia.2006.305922.

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Engelbrecht, AP. "Particle swarm optimization with discrete crossover." In 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557864.

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Wang, Xin, Xing Wang, and Na Li. "Discrete local particle swarm optimization: A more rapid and precise hybrid particle swarm optimization." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6818030.

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Lu, Qiang, Qing-He Xu, and Xue-Na Qiu. "Discrete Particle Swarm Optimization with Chaotic Initialization." In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2009). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5162645.

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Strasser, Shane, Rollie Goodman, John Sheppard, and Stephyn Butcher. "A New Discrete Particle Swarm Optimization Algorithm." In GECCO '16: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2908812.2908935.

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Xu, Yiheng, Qiangwei Wang, and Jinglu Hu. "An Improved Discrete Particle Swarm Optimization Based on Cooperative Swarms." In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2008. http://dx.doi.org/10.1109/wiiat.2008.103.

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Rao, Singiresu S., and Kiran K. Annamdas. "Particle Swarm Methodologies for Engineering Design Optimization." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87237.

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Abstract:

Particle swarm methodologies are presented for the solution of constrained mechanical and structural system optimization problems involving single or multiple objective functions with continuous or mixed design variables. The particle swarm optimization presented is a modified particle swarm optimization approach, with better computational efficiency and solution accuracy, is based on the use of dynamic maximum velocity function and bounce method. The constraints of the optimization problem are handled using a dynamic penalty function approach. To handle the discrete design variables, the closest discrete approach is used. Multiple objective functions are handled using a modified cooperative game theory approach. The applicability and computational efficiency of the proposed particle swarm optimization approach are demonstrated through illustrate examples involving single and multiple objectives as well as continuous and mixed design variables. The present methodology is expected to be useful for the solution of a variety of practical engineering design optimization problems.

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Zhong Liu and Lei Huang. "A mixed discrete particle swarm optimization for TSP." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579238.

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Reports on the topic "Discrete Particle Swarm Optimization"

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Vtipil, Sharon, and John G. Warner. Earth Observing Satellite Orbit Design Via Particle Swarm Optimization. Fort Belvoir, VA: Defense Technical Information Center, August 2014. http://dx.doi.org/10.21236/ada625084.

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Sonugür, Güray, Celal Onur Gçkçe, Yavuz Bahadır Koca, and Şevket Semih Inci. Particle Swarm Optimization Based Optimal PID Controller for Quadcopters. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, December 2021. http://dx.doi.org/10.7546/crabs.2021.12.11.

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Gökçe, Barış, Yavuz Bahadır Koca, Yılmaz Aslan, and Celal Onur Gökçe. Particle Swarm Optimization-based Optimal PID Control of an Agricultural Mobile Robot. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, April 2021. http://dx.doi.org/10.7546/crabs.2021.04.12.

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Davis, Jeremy, Amy Bednar, and Christopher Goodin. Optimizing maximally stable extremal regions (MSER) parameters using the particle swarm optimization algorithm. Engineer Research and Development Center (U.S.), September 2019. http://dx.doi.org/10.21079/11681/34160.

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Styling Parameter Optimization of the Type C Recreational Vehicle Air Drag. SAE International, September 2021. http://dx.doi.org/10.4271/2021-01-5094.

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Abstract:

Recreational vehicles have a lot of potential consumers in China, especially the type C recreational vehicle is popular among consumers due to its advantages, prompting an increase in the production and sales volumes. The type C vehicle usually has a higher air drag than the common commercial vehicles due to its unique appearance. It can be reduced by optimizing the structural parameters, thus the energy consumed by the vehicle can be decreased. The external flow field of a recreational vehicle is analyzed by establishing its computational fluid dynamic (CFD) model. The characteristic of the RV’s external flow field is identified based on the simulation result. The approximation models of the vehicle roof parameters and air drag and vehicle volume are established by the response surface method (RSM). The vehicle roof parameters are optimized by multi-objective particle swarm optimization (MO-PSO). According to the comparison, the air drag is reduced by 2.89% and the vehicle volume is increased by 0.36%. For the RV, the proper geometry parameters can increase the inner space of the vehicle while reducing the air drag.

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