Artykuły w czasopismach: „Mtspo”

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Jaremko, Mariusz, Łukasz Jaremko, Garima Jaipuria, Stefan Becker i Markus Zweckstetter. "Structure of the mammalian TSPO/PBR protein". Biochemical Society Transactions 43, nr 4 (1.08.2015): 566–71. http://dx.doi.org/10.1042/bst20150029.

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The 3D structure of the 18-kDa transmembrane (TM) protein TSPO (translocator protein)/PBR (peripheral benzodiazepine receptor), which contains a binding site for benzodiazepines, is important to better understand its function and regulation by endogenous and synthetic ligands. We have recently determined the structure of mammalian TSPO/PBR in complex with the diagnostic ligand PK11195 [1-(2-chlorophenyl)-N-methyl-N-(1-methylpropyl)-3-isoquinolinecarboxamide; Jaremko et al. (2014) Science 343, 1363–1366], providing for the first time atomic-level insight into the conformation of this protein, which is up-regulated in various pathological conditions including Alzheimer's disease and Parkinson's disease. Here, we review the studies which have probed the structural properties of mammalian TSPO/PBR as well as the homologues bacterial tryptophan-rich sensory proteins (TspOs) over the years and provide detailed insight into the 3D structure of mouse TSPO (mTSPO)/PBR in complex with PK11195.

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Massawe, Ezrael J., Richard Kimwaga i Fredrick Mwanuzi. "Modelling Transport of Nitrogen Compounds in Geita Wetland along Mtakuja River". Tanzania Journal of Engineering and Technology 37, nr 2 (30.06.2018): 89–106. http://dx.doi.org/10.52339/tjet.v37i2.486.

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The impacts of excessive nitrogen loading to streams in a watershed occur in the receiving waters such as rivers at the outlet of the watershed. To quantify the impacts of land use and management practices on the nitrogen loading at the watershed outlet, simulation models are needed that can both predict the nitrogen loading at the edge of individual fields and predict the fate of nitrogen as it moves through the river network to the watershed outlet. This paper presents the results of a model analysis for describing the processes governing transformations and transport of nitrogen compounds (NO3-N and NH4-N) through Mtakuja River in the Geita wetland. The model was made in Soil and Water Assessment Tool (SWAT), a watershed model developed to assess the impact of land management practices on water, sediment and agricultural chemical yields with varying soils, land use and management conditions. Two monitoring stations namely MTSP1 and MTSP2 were established along Mtakuja River. A set of SWAT model inputs representative of the water conditions was collected from the established monitoring stations. The model was calibrated and validated for the prediction of flow and nitrogen compounds (NO3-N and NH4-N) transport, against a set of measured mean monthly monitoring data. Sensitive model parameters were adjusted within their feasible ranges during calibration to minimize model prediction errors. At the gauging station MTSP2, the calibration results showed that the model predicted mean monthly flow within 18% of the measured mean monthly flow with the r2 coefficient and Nash-Sutcliffe (NSE) were 0.84 and 0.82, respectively. At the water quality monitoring station MTSP2, the calibration results showed the model predicted nitrogen compounds (NO3-N and NH4-N) loadings within 21% and 23% of their respective measured mean monthly loadings. The mean monthly comparisons of r 2 values for nitrogen compounds ranged from 0.77 to 0.81 while the Nash-Sutcliffe Efficiency (NSE) values were between 0.72 and 0.73. The model results and field measurements demonstrated that about 70% of the annual nitrogen compounds loadings which would otherwise reach Lake Victoria are retained in the wetland. The Mtakuja river model can therefore be used for prediction of nitrogen compounds (NO3-N and NH4-N) transformation processes in the Geita wetland.

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Jin, Hui, Qingsong He, Miao He, Fangchao Hu i Shiqing Lu. "New Method of Path Optimization for Medical Logistics Robots". Journal of Robotics and Mechatronics 33, nr 4 (20.08.2021): 944–54. http://dx.doi.org/10.20965/jrm.2021.p0944.

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The path planning problem of logistics robots is mainly subjected to the time cost of the operation of the mathematical model. To save the time of refilling process in the fast medicine dispensing system (FMDS), the optimization procedure is divided into two steps in this study. First, a new mathematical model called the multiple steps traveling salesman problem model (MTSPM) is proposed to optimize the replenishment quantity of each picking and establish picking sets. Second, an improved ant colony optimization (IACO) algorithm is employed, considering the effects of velocity, acceleration, and deceleration in the refilling route during the development of the new model. Simulation results and operational results demonstrated that MTSPM-IACO was better than both the order picking model (OPM) and MTSPM-ACO in terms of saving refilling time. Compared to the OPM, the optimization of the refilling time of MTSPM-IACO was more than 1.73% in simulation and 15.26% in operation. Compared to MTSPM-ACO, the optimization of the refilling time of MTSPM-IACO was more than 0.13% in simulation and 1.67% in operation.

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Teplonogova, Maria A., Marina V. Volostnykh, Alexey D. Yapryntsev, Madina M. Sozarukova, Yulia G. Gorbunova, Ekaterina D. Sheichenko, Alexander E. Baranchikov i Vladimir K. Ivanov. "Switchable Nanozyme Activity of Porphyrins Intercalated in Layered Gadolinium Hydroxide". International Journal of Molecular Sciences 23, nr 23 (6.12.2022): 15373. http://dx.doi.org/10.3390/ijms232315373.

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In this study, organo-inorganic nanohybrids LHGd-MTSPP with enzyme-like activity were prepared by in situ intercalation of anionic 5,10,15,20-tetrakis-(4-sulfonatophenyl)porphyrin and its complexes with Zn(II) and Pd(II) (MTSPP, M = 2H, Zn(II) and Pd(II)) into gadolinium layered hydroxide (LHGd). The combination of powder XRD, CHNS analysis, FT-IR, EDX, and TG confirmed the layered structure of the reaction products. The basal interplanar distances in LHGd-MTSPP samples were 22.3–22.6 Å, corresponding to the size of an intercalated tetrapyrrole molecule. According to SEM data, LHGd-MTSPP hybrids consisted of individual lamellar nanoparticles 20–50 nm in thickness. The enzyme-like activity of individual constituents, LHGd-Cl and sulfoporphyrins TSPP, ZnTSPP and PdTSPP, and hybrid LHGd-MTSPP materials, was studied by chemiluminescence analysis using the ABAP/luminol system in phosphate buffer solution. All the individual porphyrins exhibited dose-dependent antioxidant properties with respect to alkylperoxyl radicals at pH 7.4. The intercalation of free base TSPP porphyrin into the LHGd preserved the radical scavenging properties of the product. Conversely, in LHGd-MTSPP samples containing Zn(II) and Pd(II) complexes, the antioxidant properties of the porphyrins changed to dose-dependent prooxidant activity. Thus, an efficient approach to the design and synthesis of advanced LHGd-MTSPP materials with switchable enzyme-like activity was developed.

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Cokrowibowo, Sugiarto, Ismail i Indra. "Multiple Traveling Salesman Problem Menggunakan Algoritma Ant Colony Optimization dengan Operasi Elitism". Journal of Computer and Information System ( J-CIS ) 1, nr 2 (17.10.2019): 23–28. http://dx.doi.org/10.31605/jcis.v1i2.619.

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The Multiple Traveling Salesman Problem (MTSP) is a combinatorial optimization problem and extension of Traveling Salesman Problem (TSP). There are many implementation of MTSP on transportation and scheduling. MTSP is an NP-hard problem. This paper study about solving MTSP using Ant Colony Optimization and Elitisme operator from Genetic Algorithm. The result of this combination algorithm enhance the solution search performace because of the elitism operation which ensure the solution value always increase.

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Zhang, Wenze, i Chenyang Xu. "A comparative study between SA and GA in solving MTSP". Theoretical and Natural Science 18, nr 1 (8.12.2023): 61–70. http://dx.doi.org/10.54254/2753-8818/18/20230321.

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The multiple traveling salesmen problems (MTSP) is a combinatorial optimization and np-hard problem. In practice, the computational resource required to solve such problems is usually prohibitive, and, in most cases, using heuristic algorithms is the only practical option. This paper implements genetic algorithms (GA) and simulated annealing (SA) to solve the MTSP and does an experimental study based on a benchmark from the TSPLIB instance to compare the performance of two algorithms in reality. The results show that GA can achieve an acceptable solution in a shorter time for any of the MTSP cases and is more accurate when the data size is small. Meanwhile, SA is more robust and achieves a better solution than GA for complex MTSP cases, but it takes more time to converge. Therefore, the result indicates that it is hard to identify which algorithm is comprehensively superior to the other one. However, It also provides an essential reference to developers who want to choose algorithms to solve MTSP in real life, facilitating them to balance the algorithms performance on different metrics they value.

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Iriani, Rahma Try, Sapti Wahyuningsih i Darmawan Satyananda. "TWO PHASE HEURISTIC ALGORITHM (TPHA) PADA MULTIPLE TRAVELLING SALESMAN PROBLEM (MTSP) DAN IMPLEMENTASI PROGRAMNYA". Jurnal Kajian Matematika dan Aplikasinya (JKMA) 1, nr 1 (17.06.2020): 10. http://dx.doi.org/10.17977/um055v1i12020p10-17.

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Multiple Traveling Salesman Problem (MTSP) is one variant of Traveling Salesman Problem (TSP) which involves several salesmen in making a trip to visit several customers. In this article, the Two-Phase Heuristic Algorithm (TPHA) is used to solve MTSP problems. The algorithm classifies customers into several regions using the K-Means algorithm, which will then find a route solution for each region using a genetic algorithm. The MTSP problems that were resolved using TPHA were implemented into the Borland Delphi 7.0 programming language. Application testing was conducted using 21, 32, and 46 point cases.

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BROWN, EVELYN C., CLIFF T. RAGSDALE i ARTHUR E. CARTER. "A GROUPING GENETIC ALGORITHM FOR THE MULTIPLE TRAVELING SALESPERSON PROBLEM". International Journal of Information Technology & Decision Making 06, nr 02 (czerwiec 2007): 333–47. http://dx.doi.org/10.1142/s0219622007002447.

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The multiple traveling salesperson problem (MTSP) involves scheduling m > 1 salespersons to visit a set of n > m locations. Thus, the n locations must be divided into m groups and arranged so that each salesperson has an ordered set of cities to visit. The grouping genetic algorithm (GGA) is a type of genetic algorithm (GA) designed particularly for grouping problems. It has been successfully applied to a variety of grouping problems. This paper focuses on the application of a GGA to solve the MTSP. Our GGA introduces a new chromosome representation to indicate which salesperson is assigned to each tour and the ordering of the cities within each tour. We compare our method to standard GAs that employ either the one-chromosome or two-chromosome representation for MTSP. This research demonstrates that our GGA with its new chromosome representation is capable of solving a variety of MTSP problems from the literature and can outperform the traditional encodings of previously published GA methods.

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Mercante, Luiza A., Vanessa P. Scagion, Adriana Pavinatto, Rafaela C. Sanfelice, Luiz H. C. Mattoso i Daniel S. Correa. "Electronic Tongue Based on Nanostructured Hybrid Films of Gold Nanoparticles and Phthalocyanines for Milk Analysis". Journal of Nanomaterials 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/890637.

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The use of gold nanoparticles combined with other organic and inorganic materials for designing nanostructured films has demonstrated their versatility for various applications, including optoelectronic devices and chemical sensors. In this study, we reported the synthesis and characterization of gold nanoparticles stabilized with poly(allylamine hydrochloride) (Au@PAH NPs), as well as the capability of this material to form multilayer Layer-by-Layer (LbL) nanostructured films with metal tetrasulfonated phthalocyanines (MTsPc). Film growth was monitored by UV-Vis absorption spectroscopy, atomic force microscopy (AFM), and Fourier transform infrared spectroscopy (FTIR). Once LbL films have been applied as active layers in chemical sensors, Au@PAH/MTsPc and PAH/MTsPc LbL films were used in an electronic tongue system for milk analysis regarding fat content. The capacitance data were treated using Principal Component Analysis (PCA), revealing the role played by the gold nanoparticles on the LbL films electrical properties, enabling this kind of system to be used for analyzing complex matrices such as milk without any prior pretreatment.

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Jin, Hui, Qingsong He, Miao He, Shiqing Lu, Fangchao Hu i Daxian Hao. "Optimization for medical logistics robot based on model of traveling salesman problems and vehicle routing problems". International Journal of Advanced Robotic Systems 18, nr 3 (1.05.2021): 172988142110225. http://dx.doi.org/10.1177/17298814211022539.

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Fast medicine dispensing system (FMDS) as a kind of medical logistic robot can dispense many drugs for one prescription at the same time. To guarantee the sustainability of drug dispensation, it is required that FMDS replenish drugs rapidly. The traditional order picking model (OPM) is difficult to meet the demand of prompt replenishment. To solve the problems of prolonged refilling route and inefficiency of drugs replenishment, a mixed refilling model based on multiple steps traveling salesman problem model (MTSPM) and vehicle routing problem model (VRPM) is proposed, and it is deployed in two circumstances of FMDS, including temporary replenishment mode (TRM) and concentrate replenishment mode (CRM). It not only meted the demand under different circumstances of drug replenishment but also shortened the refilling route significantly. First, the new pick sets were generated. Then, the orders of pick sets were optimized and the new paths were achieved. When the number of pickings is varied no more than 20, experiment results declared that the refilling route is the shortest by utilizing MTSPM when working under the TRM condition. Comparing MTSPM with OPM, the rate of refilling route length decreased up to 32.18%. Under the CRM condition, the refilling route is the shortest by utilizing VRPM. Comparing VRPM with OPM, the rate of refilling route length decreased up to 58.32%. Comparing VRPM with MTSPM, the rate of refilling route length has dropped more than 43.26%.

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Yi, H. R., D. Winkler i T. Claeson. "Junction parameters of mtsp;YBa2Cu3O7 step edge junctions on mtsp;LaAlO3 substrates from Fiske resonances". Applied Physics Letters 66, nr 13 (27.03.1995): 1677–79. http://dx.doi.org/10.1063/1.113890.

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

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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 difficulty rises, which is to find a suitable mapping between sequence and continuous position of particles in particle swarm optimization. For overcoming this difficulty, PSO is combined with ant colony optimization (ACO), and the mapping between sequence and continuous position of particles is established. To verify the efficiency of the DPSO algorithm, it is used to solve the MTSP and its performance is compared with the ACO and some traditional DPSO algorithms. The computational results show that the proposed DPSO algorithm is efficient.

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Swami, Umang, Pedro Isaacsson Velho, Roberto Nussenzveig, Victor Sacristan Santos, Stephanie Erickson, Divya Dharmaraj, Ajjai Shivaram Alva i in. "Impact of somatic SPOP (Speckle-Type POZ protein) mutation (mtSPOP) on response to systemic therapy and survival outcome in men with de novo metastatic castration-sensitive prostate cancer (d-mCSPC)." Journal of Clinical Oncology 38, nr 6_suppl (20.02.2020): 329. http://dx.doi.org/10.1200/jco.2020.38.6_suppl.329.

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329 Background: Men with metastatic castration resistant prostate cancer harboring somatic mutant SPOP (mt SPOP) have improved progression free survival (PFS) on abiraterone than those with wild-type SPOP (wt SPOP) (Boysen et al, CCR 2018; PMID: 30068710). We hypothesized that mtSPOP will be associated with improved response to systemic therapy and outcomes in mCSPC. Methods: This retrospective study included patients from 4 academic institutions. Eligibility criteria: receipt of standard androgen deprivation therapy (ADT) without intensification (chemotherapy or novel hormonal agents) for the diagnosis of d-mCSPC, no prior history or treatment for prostate cancer, and established SPOP status determined by targeted next-generation sequencing. PFS was defined per PCWG2 defined PSA or investigator assessed radiographic progression. Overall survival (OS) was calculated from date of starting ADT for d-mCSPC to date of death. Kaplan-Meier analysis and t-test were used to compare variables in these two cohorts. Results: Of 110 mtSPOP men with advanced prostate cancer identified, 37 had d-mCSPC of which 25 received ADT. Of 353 wtSPOP patients, 184 had d-mCSPC of which 97 received ADT. Baseline demographics and disease characteristic were similar (table). mtSPOP was associated with significantly improved PFS [35 vs. 14 months, HR 0.519 (95% CI 0.312-0.861), p=0.011] and OS [97 vs. 69 months, HR 0.4392 (95% CI=0.207-0.931); p=0.032] with ADT as compared to wtSPOP patients. Conclusions: Men with d-mCSPC with somatic mtSPOP have improved outcomes with ADT than those with wtSPOP. Once validated, these hypothesis generating data may aid with counselling and treatment selection, as well as patient stratification in future trials in d-mCSPC.[Table: see text]

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Saito, Kei, Masato Ozaka, Ryo Kanata, Ikuhiro Yamada, Takashi Sasaki, Masato Matsuyama, Kouichi Takano i Naoki Sasahira. "The impact of maximum tumor shrinkage of primary site (MTSP) by FOLFIRINOX in the first-line treatment of pancreatic cancer." Journal of Clinical Oncology 34, nr 4_suppl (1.02.2016): 356. http://dx.doi.org/10.1200/jco.2016.34.4_suppl.356.

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356 Background: FOLFIRINOX is the first-line chemotherapy for patients with unresectable pancreatic cancer and yielded a longer overall survival, a superior progression-free survival, a higher objective response rate compared with gemcitabine alone. Maximum tumor shrinkage of primary site (MTSP) was associated with the survival outcome in colon cancer. However the data of tumor shrinkage in pancreatic cancer has not been well-documented. Therefore, we re-evaluated FOLFIRINOX focusing on the tumor shrinkage of primary site. We also assessed impact of tumor shrinkage on survival of unresectable pancreatic cancer. Methods: Medical records were retrospectively reviewed for consecutive patients receiving FOLFIRINOX as the first-line chemotherapy between January and December 2014. Response was evaluated by CT scan every 2 or 3 months until the end of treatment. We analyzed patients’ characteristics and rate of MTSP related to overall survival (OS) and progression free survival (PFS). Results: Thirty one patients were analyzed: median age of 62, male gender in 64.5%, performance status of 0 in 90.3%, metastatic cancer in 58.1%, the head of cancer in 41.9%, the UGT1A1 wild type in 48.4%, overall response rate in 25.8%. PFS was 7.7 months (95%CI: 6.57-9.6). Rate of MTSP were 18.8% (locally 18.8%, metastatic 18.1%) and rate of GRs/non-GRs/non-R (GRs (Good responders) were defined as patients whose MTSP were more than 20% and non-GRs (non-Good responders) were less than 20%) were 45.2%/35.5%/19.3%. PFS was 9.0(95%CI, 6.7- ) months in GRs, compared with 6.9(95%CI, 3.6-10.2) months in non-GRs (p = 0.15). Conclusions: MTSP by FOLFIRINOX could not affect PFS of pancreatic cancer.

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Hammouda, Souha, Imen Ghzaiel, Pol Picón-Pagès, Wiem Meddeb, Wided Khamlaoui, Sonia Hammami, Francisco J. Muñoz, Mohamed Hammami i Amira Zarrouk. "Nigella and Milk Thistle Seed Oils: Potential Cytoprotective Effects against 7β-Hydroxycholesterol-Induced Toxicity on SH-SY5Y Cells". Biomolecules 11, nr 6 (27.05.2021): 797. http://dx.doi.org/10.3390/biom11060797.

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Oxysterols are assumed to be the driving force behind numerous neurodegenerative diseases. In this work, we aimed to study the ability of 7β-hydroxycholesterol (7β-OHC) to trigger oxidative stress and cell death in human neuroblastoma cells (SH-SY5Y) then the capacity of Nigella sativa and Milk thistle seed oils (NSO and MTSO, respectively) to oppose 7β-OHC-induced side effects. The impact of 7β-OHC, associated or not with NSO or MTSO, was studied on different criteria: cell viability; redox status, and apoptosis. Oxidative stress was assessed through the intracellular reactive oxygen species (ROS) production, levels of enzymatic and non-enzymatic antioxidants, lipid, and protein oxidation products. Our results indicate that 7β-OHC (40 µg/mL) exhibit pr-oxidative and pro-apoptotic activities shown by a decrease of the antioxidant enzymatic activities and an increase of ROS production, lipid, and protein oxidation end products as well as nitrotyrosine formation and caspase 3 activation. However, under the pre-treatment with NSO, and especially with MTSO (100 µg/mL), a marked attenuation of oxidative damages was observed. Our study suggests harmful effects of 7β-OHC consisting of pro-oxidative, anti-proliferative, and pro-apoptotic activities that may contribute to neurodegeneration. NSO and especially MTSO showed potential cytoprotection against the cytotoxicity of 7β-OHC.

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Hussein, Ahmed Abdulmunem, Musa A. Hameed i Saddam Hamdan Ahmed. "Enhancing the multiple traveling salesman problem solutions through Harris Hawks optimization and machine learning techniques". Revista Científica de Sistemas e Informática 4, nr 2 (10.07.2024): e745. http://dx.doi.org/10.51252/rcsi.v4i2.745.

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This work introduces an approach to solving the Multiple Traveling Salesman Problem (mTSP) by integrating metaheuristic algorithms (MHs) with machine learning (ML) techniques. Specifically, the Discrete Harris Hawks Optimization (DHHO) algorithm was developed to handle the discrete nature of the mTSP, as the original Harris Hawks Optimization (HHO) was designed for continuous problems. The DHHO algorithm, enhanced with SARSA-based learning mechanisms for solution initialization and parameter tuning, significantly improves the efficiency of mTSP solutions. By leveraging ML's adaptability within the robust MH framework, this study offers a novel perspective on combinatorial optimization problems, surpassing the best-known solutions (BKS) in various mTSP instances. The results were tested using TSPLIB benchmark instances, including Att48, Berlin52, Bier127, Pr76, and Rat99, for two, three, and four salesmen, achieving optimal results in 12 out of 15 instances. The DHHO's performance was validated by the quality of solutions and consistency across multiple runs, with optimal results in 5 out of 5 instances for two salesmen, 3 out of 5 for three salesmen, and 4 out of 5 for four salesmen. Statistical validation using the Wilcoxon signed-rank test confirmed the significance of these improvements (p < 0.05). This work highlights the impact of integrating MHs and ML, making a substantial contribution to the current literature.

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Hsu, Ling-Yuan, Shi-Jinn Horng, Pingzhi Fan, Muhammad Khurram Khan, Yuh-Rau Wang, Ray-Shine Run, Jui-Lin Lai i Rong-Jian Chen. "MTPSO algorithm for solving planar graph coloring problem". Expert Systems with Applications 38, nr 5 (maj 2011): 5525–31. http://dx.doi.org/10.1016/j.eswa.2010.10.084.

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Козлова, Маргарита Геннадьевна, Владимир Андреевич Лукьяненко i Олег Олегович Макаров. "Построение многоагентных маршрутов в сети с иерархией вершин". Вестник ВГУ. Серия: Системный анализ и информационные технологии, nr 3 (26.10.2023): 32–50. http://dx.doi.org/10.17308/sait/1995-5499/2023/3/32-50.

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Многоагентная задача маршрутизации типа коммивояжера является NP-трудной. Для построения приближенных решений важен учет всей имеющейся информации о структуре сети, ограничениях и целях. Выделяется класс задач (характерный для распределения ресурсов в инфраструктурных сетях), в которых введен иерархический порядок вершин. Рассматриваются прикладные задачи многоагентной маршрутизации mTSP на таких сетях с учетом разного уровня иерархии вершин и кластеризации сети (HCmTSP). Построение маршрутов HCmTSP согласовано с естественной кластеризацией сложной инфраструктурной сети. Приводится обзор задач, методов и алгоритмов, основанных на разных эвристиках. Показано, что в зависимости от логистических целей может быть выбран различный тип кластеризации, согласованной с mTSP. Сравниваются результаты вычислительного эксперимента по типам кластеризации и маршрутам. Приводятся результаты синтеза маршрутов двух уровней иерархии. Разработаны и реализованы алгоритмы построения маршрутов mTSP с одним центром (базой) и синтеза двухуровневых маршрутов обхода кластеров и маршрутов TSP на кластерах. В задаче mTSP с одним выделенным центром для построения начальных маршрутов исследуется гибридный алгоритм распределения вершин по кругу в зависимости от угла, с дальнейшим разбиением на кластеры и поиском TSP агентами на каждом кластере. Используются эвристики нескольких муравьиных колоний. В задаче синтеза базовый алгоритм имитации отжига сравнивается с решателем Concorde. Приведены численные результаты для известных тестовых наборов данных и для реальных данных городской инфраструктуры. Работа предназначена для отбора алгоритмов, их тестирования и проведения вычислительных экспериментов и наполнения программного комплекса: построение многоагентных маршрутов в сложных сетях с иерархией вершин.

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Su, Shoubao, Wei Zhao i Chishe Wang. "Parallel Swarm Intelligent Motion Planning with Energy-Balanced for Multirobot in Obstacle Environment". Wireless Communications and Mobile Computing 2021 (30.08.2021): 1–16. http://dx.doi.org/10.1155/2021/8902328.

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Multirobot motion planning is always one of the critical techniques in edge intelligent systems, which involve a variety of algorithms, such as map modeling, path search, and trajectory optimization and smoothing. To overcome the slow running speed and imbalance of energy consumption, a swarm intelligence solution based on parallel computing is proposed to plan motion paths for multirobot with many task nodes in a complex scene that have multiple irregularly-shaped obstacles, which objective is to find a smooth trajectory under the constraints of the shortest total distance and the energy-balanced consumption for all robots to travel between nodes. In a practical scenario, the imbalance of task allocation will inevitably lead to some robots stopping on the way. Thus, we firstly model a gridded scene as a weighted MTSP (multitraveling salesman problem) in which the weights are the energies of obstacle constraints and path length. Then, a hybridization of particle swarm and ant colony optimization (GPSO-AC) based on a platform of Compute Unified Device Architecture (CUDA) is presented to find the optimal path for the weighted MTSPs. Next, we improve the A ∗ algorithm to generate a weighted obstacle avoidance path on the gridded map, but there are still many sharp turns on it. Therefore, an improved smooth grid path algorithm is proposed by integrating the dynamic constraints in this paper to optimize the trajectory smoothly, to be more in line with the law of robot motion, which can more realistically simulate the multirobot in a real scene. Finally, experimental comparisons with other methods on the designed platform of GPUs demonstrate the applicability of the proposed algorithm in different scenarios, and our method strikes a good balance between energy consumption and optimality, with significantly faster and better performance than other considered approaches, and the effects of the adjustment coefficient q on the performance of the algorithm are also discussed in the experiments.

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Essani, Furqan, i Sajjad Haider. "An Algorithm for Mapping the Asymmetric Multiple Traveling Salesman Problem onto Colored Petri Nets". Algorithms 11, nr 10 (25.09.2018): 143. http://dx.doi.org/10.3390/a11100143.

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The Multiple Traveling Salesman Problem is an extension of the famous Traveling Salesman Problem. Finding an optimal solution to the Multiple Traveling Salesman Problem (mTSP) is a difficult task as it belongs to the class of NP-hard problems. The problem becomes more complicated when the cost matrix is not symmetric. In such cases, finding even a feasible solution to the problem becomes a challenging task. In this paper, an algorithm is presented that uses Colored Petri Nets (CPN)—a mathematical modeling language—to represent the Multiple Traveling Salesman Problem. The proposed algorithm maps any given mTSP onto a CPN. The transformed model in CPN guarantees a feasible solution to the mTSP with asymmetric cost matrix. The model is simulated in CPNTools to measure two optimization objectives: the maximum time a salesman takes in a feasible solution and the collective time taken by all salesmen. The transformed model is also formally verified through reachability analysis to ensure that it is correct and is terminating.

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Smaili, F. "A hybrid genetic-simulated annealing algorithm for multiple traveling salesman problems". Decision Science Letters 13, nr 3 (2024): 709–28. http://dx.doi.org/10.5267/j.dsl.2024.4.001.

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The Multiple Traveling Salesman Problem (MTSP) was able to model and solve various theoretical and real-life applications. This problem is one of the many difficult issues that have no perfect solution yet. In this paper, on the one hand genetic algorithms with different combinations of operators and simulated annealing were used to solve the MTSP. On the other hand, the genetic algorithm with the combination of operators that gave the best solutions of the MTSP was hybridized with a Simulated Annealing algorithm. The simulation results showed that the hybrid algorithm significantly outperforms most of the comparable methods in obtaining the best-fitness solutions compared to the other methods in most of the test cases. In addition, by scaling the fitness function according to the amplitude of tours, it was obvious that the non-dominated front obtained by the hybrid algorithm was better than the non-dominated front obtained by the other algorithms.

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Cornejo-Acosta, José Alejandro, Jesús García-Díaz, Julio César Pérez-Sansalvador i Carlos Segura. "Compact Integer Programs for Depot-Free Multiple Traveling Salesperson Problems". Mathematics 11, nr 13 (6.07.2023): 3014. http://dx.doi.org/10.3390/math11133014.

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Multiple traveling salesperson problems (mTSP) are a collection of problems that generalize the classical traveling salesperson problem (TSP). In a nutshell, an mTSP variant seeks a minimum cost collection of m paths that visit all vertices of a given weighted complete graph. This paper introduces novel compact integer programs for the depot-free mTSP (DFmTSP). This fundamental variant models real scenarios where depots are unknown or unnecessary. The proposed integer programs are adapted to the main variants of the DFmTSP, such as closed paths, open paths, bounding constraints (also known as load balance), and the minsum and minmax objective functions. Some of these integer programs have O(n2m) binary variables and O(n2) constraints, where m is the number of salespersons and n=|V(G)|. Furthermore, we introduce more compact integer programs with O(n2) binary variables and O(n2) constraints for the same problem and most of its main variants. Without losing their compactness, all the proposed programs are adapted to fixed-destination multiple-depots mTSP (FD-MmTSP) and a combination of FD-MmTSP and DFmTSP, where fewer than m depots are part of the input, but the solution still consists of m paths. We used off-the-shelf optimization software to empirically test the proposed integer programs over a classical benchmark dataset; these tests show that the proposed programs meet desirable theoretical properties and have practical advantages over the state of the art.

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YOGA DWI WAHYU NUGRAHA, HENDRAWAN ARMANTO i YOSI KRISTIAN. "Single Objective Mayfly Algorithm with Balancing Parameter for Multiple Traveling Salesman Problem". Journal of Electronics, Electromedical Engineering, and Medical Informatics 5, nr 3 (8.07.2023): 193–204. http://dx.doi.org/10.35882/jeemi.v5i3.299.

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The Multiple Travelling Salesman Problem (MTSP) is a challenging combinatorial problem that involves multiple salesman visiting a set of cities, each exactly once, starting and ending at the same depot. The aim is to determine the optimal route with minimal cost and node cuts for each salesman while ensuring that at least one salesman visits each city. As the problem is NP-Hard, a single-objective metaheuristic algorithm, called the Mayfly Algorithm, inspired by the collective behavior of mayflies, is employed to solve the problem using the TSPlib95 test data. Since the Mayfly Algorithm employs a single fitness function, a balancing parameter is added to perform multiobjective optimization. Three balancing parameters in the optimization process: SumRoute represents the total cost of all salesmen travelling, StdRoute balances each salesman cost, and StdNodes balances the number of nodes for each salesman. The values of these parameters are determined based on the results of various tests, as they significantly impact the MTSP optimization process. With the appropriate parameter values, the single-objective Mayfly Algorithm can produce optimal solutions and avoid premature convergence. Overall, the Mayfly Algorithm shows promise as a practical approach to solving the MTSP problem. Using multiobjective optimization with balancing parameters enables the algorithm to achieve optimal results and avoid convergence issues. The TSPlib95 dataset provides a robust testing ground for evaluating the algorithm’s effectiveness, demonstrating its ability to solve MTSP effectively with multiple salesman.

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Husban, Ahmad. "An Exact Solution Method for the MTSP". Journal of the Operational Research Society 40, nr 5 (maj 1989): 461. http://dx.doi.org/10.2307/2583618.

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Ponraj, Ranjana, i George Amalanathan. "Dynamic Capacity Routing in Networks with MTSP". International Journal of Computer and Communication Engineering 5, nr 6 (2016): 465–72. http://dx.doi.org/10.17706/ijcce.2016.5.6.465-472.

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Husban, Ahmad. "An Exact Solution Method for the MTSP". Journal of the Operational Research Society 40, nr 5 (maj 1989): 461–69. http://dx.doi.org/10.1057/jors.1989.73.

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Umul, Yusuf Z. "Rubinowicz transform of the MTPO surface integrals". Optics Communications 281, nr 23 (grudzień 2008): 5641–46. http://dx.doi.org/10.1016/j.optcom.2008.08.008.

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Wang, Yao, Yongkang Shi i Yunhui Liu. "Research on improved genetic simulated annealing algorithm for multi-UAV cooperative task allocation". Journal of Physics: Conference Series 2246, nr 1 (1.04.2022): 012081. http://dx.doi.org/10.1088/1742-6596/2246/1/012081.

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Abstract In order to solve the cooperative search problem of multiple unmanned aerial vehicles (multi-UAVs) in a large-scale area, we propose a genetic algorithm (GA) incorporating simulated annealing (SA) for solving the task region allocation problem among multi-UAVs on the premise that the large area is divided into several small areas. Firstly, we describe the problem to be solved, and regard the task areas allocation problem of multi-UAVs as a multiple traveling salesman problem (MTSP). And the objective function is established under the premise that the number of task areas to be searched by each UAV is balanced. Then, we improve the GA, using the advantages of the SA can jump out of the local optimal solution to optimize the new population of offspring generated by GA. Finally, the validity of the algorithm is verified by using the TSPLIB database, and the set MTSP problem is solved. Through a series of comparative experiments, the validity of GAISA and the superiority of solving the MTSP problem can be demonstrated.

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Al-Furhud, Maha Ata, i Zakir Hussain Ahmed. "Experimental Study of a Hybrid Genetic Algorithm for the Multiple Travelling Salesman Problem". Mathematical Problems in Engineering 2020 (27.10.2020): 1–13. http://dx.doi.org/10.1155/2020/3431420.

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The multiple travelling salesman problem (MTSP), an extension of the well-known travelling salesman problem (TSP), is studied here. In MTSP, starting from a depot, multiple salesmen require to visit all cities so that each city is required to be visited only once by one salesman only. It is NP-hard and is more complex than the usual TSP. So, exact optimal solutions can be obtained for smaller sized problem instances only. For large-sized problem instances, it is essential to apply heuristic algorithms, and amongst them, genetic algorithm is identified to be successfully deal with such complex optimization problems. So, we propose a hybrid genetic algorithm (HGA) that uses sequential constructive crossover, a local search approach along with an immigration technique to find high-quality solution to the MTSP. Then our proposed HGA is compared against some state-of-the-art algorithms by solving some TSPLIB symmetric instances of several sizes with various number of salesmen. Our experimental investigation demonstrates that the HGA is one of the best algorithms.

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Liang, Hebin, Yi Ma, Zilin Cao, Tianyang Liu, Fei Ni, Zhigang Li i Jianye Hao. "SplitNet: A Reinforcement Learning Based Sequence Splitting Method for the MinMax Multiple Travelling Salesman Problem". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 7 (26.06.2023): 8720–27. http://dx.doi.org/10.1609/aaai.v37i7.26049.

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MinMax Multiple Travelling Salesman Problem (mTSP) is an important class of combinatorial optimization problems with many practical applications, of which the goal is to minimize the longest tour of all vehicles. Due to its high computational complexity, existing methods for solving this problem cannot obtain a solution of satisfactory quality with fast speed, especially when the scale of the problem is large. In this paper, we propose a learning-based method named SplitNet to transform the single TSP solutions into the MinMax mTSP solutions of the same instances. Specifically, we generate single TSP solution sequences and split them into mTSP subsequences using an attention-based model trained by reinforcement learning. We also design a decision region for the splitting policy, which significantly reduces the policy action space on instances of various scales and thus improves the generalization ability of SplitNet. The experimental results show that SplitNet generalizes well and outperforms existing learning-based baselines and Google OR-Tools on widely-used random datasets of different scales and public datasets with fast solving speed.

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Zhang, Xinyi. "Optimizing Patrolling Operations with Multiple Traveling Salesmen Problem: A Formulation and Simulation Approach". BCP Business & Management 44 (27.04.2023): 933–37. http://dx.doi.org/10.54691/bcpbm.v44i.4980.

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Integer optimization program is a useful tool for solving realistic problems especially in assigning tasks problems. Traveling salesman (TSP) problem is one of the most classic subjects under integer program. This paper mainly focuses on the multiple traveling salesmen (MTSP) problem, which introduces two ways of formulations for the MTSP problem. The first program is formulated by tracking the traveling direction for each ‘salesperson’ when they are visiting different locations. On the contrary, the second program does not track the moving direction for salesmen and instead, which only tracks which edge between two locations is assigned to a certain salesperson. The two formulations do share some similarities and have some differences. This study briefly compares the complexity of the two models by counting the number of constraints for two formulations as well. In addition to a simulation test based on the two formulated optimization program, the limitations regarding the two formulations are discussed. According to the analysis, TSP is a classic problem under optimization, hence MTSP is meaningful for companies especially in assigning multiple tasks to workers or machines.

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Groscurth, Sira, Boje Müller, Franziska Visser, Bernhard Blob, Matthias Menzel, Boris A. Rüping, Richard M. Twyman, Dirk Prüfer i Gundula A. Noll. "Uncertain role of MtSEO-F3 in assembly ofMedicago truncatulaforisomes". Plant Signaling & Behavior 9, nr 9 (18.06.2014): e29581. http://dx.doi.org/10.4161/psb.29581.

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Yalçın, Uğur, i Mücahit Sarnık. "Uniform Diffracted Fields from a Perfectly Conducting Cylindrical Reflector with Modified Theory of Physical Optics". Scientific World Journal 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/195402.

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The uniform diffracted fields are calculated on PEC cylindrical reflector by Modified Theory of Physical Optics (MTPO). It is aimed to convert the noncontinuous solution to a continuous solution by finding a uniform equation which does not contain any expression converging to 0 in the denominator part. Three axioms of MTPO theory are used to construct the integral equations for the perfectly electrically conducting surface application. The “edge-point” technique is used to find the diffracted field, and uniform solution is to be found via “detour parameter(s).” Finally, the obtained results are to be compared with the nonuniform ones, numerically.

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Zarrouk, Amira, Lucy Martine, Stéphane Grégoire, Thomas Nury, Wiem Meddeb, Emmanuelle Camus, Asmaa Badreddine i in. "Profile of Fatty Acids, Tocopherols, Phytosterols and Polyphenols in Mediterranean Oils (Argan Oils, Olive Oils, Milk Thistle Seed Oils and Nigella Seed Oil) and Evaluation of their Antioxidant and Cytoprotective Activities". Current Pharmaceutical Design 25, nr 15 (19.08.2019): 1791–805. http://dx.doi.org/10.2174/1381612825666190705192902.

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Background: The effects of vegetable oils on human health depend on their components. Therefore, their profiles of lipid nutrients and polyphenols were determined. Objective: To establish and compare the fatty acid, tocopherol, phytosterol and polyphenol profiles of Mediterranean oils: cosmetic and dietary argan oils (AO; Morocco: Agadir, Berkane); olive oils (OO; Morocco, Spain, Tunisia); milk thistle seed oils (MTSO; Tunisia: Bizerte, Sousse, Zaghouane); nigella seed oil (NSO). Methods: The biochemical profiles were determined by gas chromatography-flame ionization, high performance liquid chromatography and gas chromatography, coupled with mass spectrometry as required. The antioxidant and cytoprotective activities were evaluated with the KRL (Kit Radicaux Libres) and the fluorescein diacetate tests on nerve cells treated with 7-ketocholesterol (7KC). Results: The fatty acid profile revealed high linoleic acid (C18:2 n-6) content in AO, OO, MTSO and NSO. The highest levels of oleic acid (C18:1 n-9) were found in AO and OO. The tocopherol profile showed that Agadir AO contained the highest amount of α-tocopherol, also present at high level in MTSO and Tunisian OO; Berkane AO was rich in γ-tocopherol. The phytosterol profile indicated that β-sitosterol was predominant in the oils, except AO; spinasterol was only present in AO. Polyphenol profiles underlined that OO was the richest in polyphenols; hydroxytyrosol was only found in OO; few polyphenols were detected in AO. The oils studied have antioxidant activities, and all of them, except NSO, prevented 7KC-induced cell death. The antioxidant characteristics of AO were positively correlated with procatechic acid and compestanol levels. Conclusion: Based on their biochemical profiles, antioxidant and cytoprotective characteristics, AO, OO, and MTSO are potentially beneficial to human health.

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Germanchuk, M. S. "Solvability of pseudobulous conditional optimization problems of the type of many salesmen". TAURIDA JOURNAL OF COMPUTER SCIENCE THEORY AND MATHEMATICS, nr 4 (49) (2021): 30–55. http://dx.doi.org/10.37279/1729-3901-2020-19-4-30-55.

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Formalizing routing problems of many traveling salesman (mTSP) in complex networks leads to NP-complete pseudobulous conditional optimization problems. The subclasses of polynomially solvable problems are distinguished, for which the elements of the distance matrix satisfy the triangle inequality and other special representations of the original data. The polynomially solvable assignment problem can be used to determine the required number of salesmen and to construct their routes. Uses a subclass of tasks in the form of pseudobulous optimization with disjunctive normal shape (\textit{DNS}) constraints to which the task is reduced mTSP. Problems in this form are polynomially solvable and allow to combine knowledge about network structure, requirements to pass routes by agents (search procedures) and efficient algorithms of logical inference on constraints in the form of \textit{DNS}. This approach is the theoretical justification for the development of multi-agent system management leading to a solution mTSP. Within the framework of intellectual planning, using resources and capabilities, and taking into account the constraints for each agent on the selected clusters of the network, the construction of a common solution for the whole complex network is achieved.

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De Carvalho, Emerson Bezerra, Elizabeth Ferreira Gouvêa Goldbarg i Marco Cesar Goldbarg. "A Multi-objective Version of the Lin-Kernighan Heuristic for the Traveling Salesman Problem". Revista de Informática Teórica e Aplicada 25, nr 1 (18.02.2018): 48. http://dx.doi.org/10.22456/2175-2745.76452.

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The Lin and Kernighan’s algorithm for the single objective Traveling Salesman Problem (TSP) is one of the most efficient heuristics for the symmetric case. Although many algorithms for the TSP were extended to the multi-objective version of the problem (MTSP), the Lin and Kernighan’s algorithm was still not fully explored. Works that applied the Lin and Kernighan’s algorithm for the MTSP were driven to weighted sum versions of the problem. We investigate the LK from a Pareto dominance perspective. The multi-objective LK was implemented within two local search schemes and applied to 2 to 4-objective instances. The results showed that the proposed algorithmic variants obtained better results than a state-of-the-art algorithm.

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Bian, Jinhua, i Xiaoxia Zhang. "A Hill-Climbing Differential Evolutionary Algorithm for solving Multiple Travelling Salesman Problem". Frontiers in Computing and Intelligent Systems 9, nr 2 (27.08.2024): 4–7. http://dx.doi.org/10.54097/fm6hta13.

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The Multiple Travelling Salesman Problem (MTSP) is a basic deformation of the Travelling Salesman Problem (TSP), which is a typical NP-Hard problem. For combinatorial optimization problems shaped like MTSP, researchers often use intelligent optimization algorithms such as genetic algorithms, differential evolutionary algorithms, simulated annealing algorithms and other intelligent optimization algorithms to approximate the solution. However, typical intelligent optimization algorithms have the disadvantage of being prone to local convergence and failing to produce theoretically optimal solutions. Based on this, we propose an HCA&DE algorithm that improves the DE algorithm by using the hill-climbing algorithm, and carry out data experiments using the solution set of TSPLIB, which proves the practicality and effectiveness of the algorithm.

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Rajina, Shakya, Woo Jean Kim, Jung-Hyun Shim, Kyung-Soo Chun, Sang Hoon Joo, Hwa Kyoung Shin, Seo-Yeon Lee i Joon-Seok Choi. "Isolinderalactone Induces Cell Death via Mitochondrial Superoxide- and STAT3-Mediated Pathways in Human Ovarian Cancer Cells". International Journal of Molecular Sciences 21, nr 20 (13.10.2020): 7530. http://dx.doi.org/10.3390/ijms21207530.

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The mortality rate of ovarian cancer (OC) worldwide increases with age. OC is an often fatal cancer with a curative rate of only 20–30%, as symptoms often appear after disease progression. Studies have reported that isolinderalactone (ILL), a furanosesquiterpene derivative extracted from the dried root of Lindera aggregata, can inhibit several cancer cell lines’ growth. However, the molecular mechanisms underlying ILL activities in human OC cells remain unexplored. This study investigated the antitumor activities of ILL in human OC cells by inducing mitochondrial superoxide (mtSO) and JAK-signal transducer and activator of transcription 3 (STAT3)-dependent cell death. ILL caused cell death in SKOV-3 and OVCAR-3 cells and increased the cell proportion in the subG1 phase. Additionally, ILL significantly induced mtSO production and reduced ROS production. Moreover, ILL downregulated mitochondrial membrane potential and the expression levels of anti-apoptotic Bcl-2 family proteins and superoxide dismutase (SOD)2. Results showed that ILL decreased phosphorylation of serine 727 and tyrosine 705 of STAT3 and expression of survivin, a STAT3-regulated gene. Furthermore, ILL-induced cell death was reversed by pretreatment of Mito-TEMPO, a mitochondria-specific antioxidant. These results suggest that ILL induces cell death by upregulation of mtSO, downregulation of mitochondrial SOD2, and inactivation of the STAT3-mediated pathway.

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Pacheco-Valencia, Víctor Hugo, Nodari Vakhania, Frank Ángel Hernández-Mira i José Alberto Hernández-Aguilar. "A Multi-Phase Method for Euclidean Traveling Salesman Problems". Axioms 11, nr 9 (30.08.2022): 439. http://dx.doi.org/10.3390/axioms11090439.

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The Traveling Salesman Problem (TSP) aims to find the shortest tour for a salesman who starts and ends in the same city and visits the remaining n−1 cities exactly once. There are a number of common generalizations of the problem including the Multiple Traveling Salesman Problem (MTSP), where instead of one salesman, there are k salesmen and the same amount of individual tours are to be constructed. We consider the Euclidean version of the problem where the distances between the cities are calculated in two-dimensional Euclidean space. Both general the TSP and its Euclidean version are strongly NP-hard. Hence, approximation algorithms with a good practical behavior are of primary interest. We describe a general method for the solution of the Euclidean versions of the TSP (including MTSP) that yields approximation algorithms with a favorable practical behavior for large real-life instances. Our method creates special types of convex hulls, which serve as a basis for the constructions of our initial and intermediate partial solutions. Here, we overview three algorithms; one of them is for the bounded version of the MTSP. The proposed novel algorithm for the Euclidean TSP provides close-to-optimal solutions for some real-life instances.

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Singamsetty, Purusotham, i Jayanth Kumar Thenepalle. "An efficient genetic algorithm for solving open multiple travelling salesman problem with load balancing constraint". Decision Science Letters 10, nr 4 (2021): 525–34. http://dx.doi.org/10.5267/j.dsl.2021.5.003.

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The multiple travelling salesman problem (MTSP) is one of the widely studied combinatorial optimization problems with various theoretical and practical applications. However, most of the studies intended to deal with classical MTSP, very limited attention has been given to an open multiple travelling salesman problem and its variants. In this paper, an open multiple travelling salesman problem with load balancing constraint (OMTSPLB) is addressed. The OMTSPLB differs from the conventional MTSP, in which all the salesmen start from the central depot and need not come back to it after visiting the given number of cities by accomplishing the load balance constraint, which helps in fairly distributing the task among all salesmen. The problem aims to minimize the overall traversal distance/cost for operating open tours subject to the load balancing constraint. A zero-one integer linear programming (0-1 ILP) model and an efficient metaheuristic genetic algorithm (GA), is established for the OMTSPLB. Since no existing study on OMTSPLB, the proposed GA is tested on the relaxed version of the present model, comparative results are reported. The comparative results show that the proposed GA is competent over the existing algorithms. Furthermore, extensive experiments are carried out on OMTSPLB and the results show that proposed GA can find the global solution effectively.

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Faigl, Jan. "An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective". Computational Intelligence and Neuroscience 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/2720630.

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In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to “see” the whole robots’ workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning.

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Zhang, Yong, Ting Luan i Yuan Yao. "MtsPSO-PID Neural Network Decoupling Control in Power Plant Boiler". IFAC Proceedings Volumes 46, nr 20 (2013): 101–5. http://dx.doi.org/10.3182/20130902-3-cn-3020.00105.

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Pietrzak, Mariusz, Joanna Buczyńska, Fritz Duus, Jacek Waluk i Poul Erik Hansen. "Photoinduced and ground state conversions in a cyclic β-thioxoketone". RSC Advances 12, nr 2 (2022): 681–89. http://dx.doi.org/10.1039/d1ra09020h.

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Veeresh, M., T. Jayanth Kumar i M. Thangaraj. "Solving the single depot open close multiple travelling salesman problem through a multi-chromosome based genetic algorithm". Decision Science Letters 13, nr 2 (2024): 401–14. http://dx.doi.org/10.5267/j.dsl.2024.1.006.

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The multiple travelling salesman problem (MTSP) extends the classical travelling salesman problem (TSP) by involving multiple salesman in the solution. MTSP has found widespread applications in various domains, such as transportation, robotics, and networking. Despite extensive research on MTSP and its variants, there has been limited attention given to the open close multiple travelling salesman problem (OCMTSP) and its variants in the literature. To the best of the author's knowledge, only one study has addressed OCMTSP, introducing an exact algorithm designed for optimal solutions. However, the efficiency of this existing algorithm diminishes for larger instances due to computational complexity. Therefore, there is a crucial need for a high-level metaheuristic to provide optimal/best solutions within a reasonable timeframe. Addressing this gap, this study proposes a first meta-heuristic called multi-chromosome-based Genetic Algorithm (GA) for solving OCMTSP. The effectiveness of the developed algorithm is demonstrated through a comparative study on distinct asymmetric benchmark instances sourced from the TSPLIB dataset. Additionally, results from comprehensive experiments conducted on 90 OCMTSP symmetric instances, generated from the renowned TSPLIB benchmark dataset, highlight the efficiency of the proposed GA in addressing the OCMTSP. Notably, the proposed multi-chromosome-based GA stands out as the top-performing approach in terms of overall performance. Further, solutions to symmetric TSPLIB benchmark instances are also reported, which will be used as a basis for future studies.

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Lu, Li-Chih, i Tai-Wen Yue. "Mission-oriented ant-team ACO for min–max MTSP". Applied Soft Computing 76 (marzec 2019): 436–44. http://dx.doi.org/10.1016/j.asoc.2018.11.048.

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Potvin, Jean-Yves, Guy Lapalme i Jean-Marc Rousseau. "A Generalized K-Opt Exchange Procedure For The MTSP". INFOR: Information Systems and Operational Research 27, nr 4 (listopad 1989): 474–81. http://dx.doi.org/10.1080/03155986.1989.11732113.

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Ni'am, Muhammad Faiz Nailun, i Nur Hamid. "Penerapan K-means dan Algoritma Genetika untuk Menyelesaikan MTSP". TeIKa 13, nr 02 (2.12.2023): 173–83. http://dx.doi.org/10.36342/teika.v13i02.3221.

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Artikel ini mengimplementasikan kombinasi Kmeans dan algoritma genetika untuk menyelesaikan Multiple Traveling Salesman Problem (MTSP) yang menghindari perpotongan jalur antar travelling salesman. Ada beberapa tujuan dalam permasalahan ini diantaranya adalah mencari jalur terpendek setiap rute, dan mencari berapa banyak pembagian klaster yang paling optimal. Dalam penelitian ini kami mencoba menggabungkan algoritma K-means dan algoritma genetika dalam menyelesaikan Multiple Traveling Salesman Problem (MTSP) dengan pembagian klaster. Penelitian ini dilakukan dengan membagi destinasi menggunakan K-means dan menerapkan Algoritma Genetika pada klaster tersebut. Hasil menunjukkan bahwa kombinasi Algoritma Genetika dan pembagian klaster dapat berkurang secara signifikan dalam merencanakan rute perjalanan yang efisien. Penggunaan klaster memungkinkan pengelompokan destinasi dengan karakteristik geografis yang mirip dan bedekatan, sehingga menghasilkan rute yang lebih optimal. Dalam penelitian mendatang, optimasi parameter dan penggabungan metode lain dapat dieksplorasi untuk solusi perencanaan perjalanan yang lebih baik.

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48

Brunner, G., U. Vettel, S. Jobstmann, MD Kramer i V. Schirrmacher. "A T-cell-related proteinase expressed by T-lymphoma cells activates their endogenous pro-urokinase". Blood 79, nr 8 (15.04.1992): 2099–106. http://dx.doi.org/10.1182/blood.v79.8.2099.2099.

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Abstract In this report, we investigated the expression and activation of proteolytic enzymes by mouse T-lymphoma cell lines of differing metastatic potential. In contrast to the low metastatic Eb line, the metastatic variants ESb and ESb-MP secreted urokinase-type plasminogen activator (u-PA), which was present in the culture supernatant predominantly in the active form (ESb, 96%; ESb-MP, 80%). All three T- lymphoma variants expressed a mainly cell surface-associated proteinase, which proved to be immunologically and enzymatically related to the murine T-cell-associated serine proteinase-1 (MTSP-1). Intact lymphoma cells were able to activate the recombinant human proenzyme of u-PA (pro-u-PA) by a plasmin-independent mechanism, because plasmin contamination of the cells was not detectable. When ESb- MP cells were cultured in the presence of inhibitors of MTSP-1, such as antithrombin III, Pro-Phe-Arg-chloromethylketone, or aprotinin, the ratio of endogenously activated murine u-PA to inactive pro-u-PA in conditioned medium was significantly reduced (from 80% to 15%). The most potent inhibitor, antithrombin, did not inhibit plasmin-catalyzed pro-u-PA activation. These results suggest a novel autocrine mechanism of plasmin-independent pro-u-PA activation for metastatic T lymphomas by the production of an MTSP-1-related proteinase. The ability to initiate the proteolytic cascade of plasminogen activation in the absence of plasmin might contribute to the metastatic behavior of these cells observed in vivo.

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49

Brunner, G., U. Vettel, S. Jobstmann, MD Kramer i V. Schirrmacher. "A T-cell-related proteinase expressed by T-lymphoma cells activates their endogenous pro-urokinase". Blood 79, nr 8 (15.04.1992): 2099–106. http://dx.doi.org/10.1182/blood.v79.8.2099.bloodjournal7982099.

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In this report, we investigated the expression and activation of proteolytic enzymes by mouse T-lymphoma cell lines of differing metastatic potential. In contrast to the low metastatic Eb line, the metastatic variants ESb and ESb-MP secreted urokinase-type plasminogen activator (u-PA), which was present in the culture supernatant predominantly in the active form (ESb, 96%; ESb-MP, 80%). All three T- lymphoma variants expressed a mainly cell surface-associated proteinase, which proved to be immunologically and enzymatically related to the murine T-cell-associated serine proteinase-1 (MTSP-1). Intact lymphoma cells were able to activate the recombinant human proenzyme of u-PA (pro-u-PA) by a plasmin-independent mechanism, because plasmin contamination of the cells was not detectable. When ESb- MP cells were cultured in the presence of inhibitors of MTSP-1, such as antithrombin III, Pro-Phe-Arg-chloromethylketone, or aprotinin, the ratio of endogenously activated murine u-PA to inactive pro-u-PA in conditioned medium was significantly reduced (from 80% to 15%). The most potent inhibitor, antithrombin, did not inhibit plasmin-catalyzed pro-u-PA activation. These results suggest a novel autocrine mechanism of plasmin-independent pro-u-PA activation for metastatic T lymphomas by the production of an MTSP-1-related proteinase. The ability to initiate the proteolytic cascade of plasminogen activation in the absence of plasmin might contribute to the metastatic behavior of these cells observed in vivo.

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50

Chaves, Antônio Augusto, Edson Luiz França Senne i Horacio Hideki Yanasse. "Uma nova heurística para o problema de minimização de trocas de ferramentas". Gestão & Produção 19, nr 1 (2012): 17–30. http://dx.doi.org/10.1590/s0104-530x2012000100002.

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O problema de minimização de troca de ferramentas (MTSP) busca uma sequência de processamento de um conjunto de tarefas, de modo a minimizar o número de trocas de ferramentas requeridas. Este trabalho apresenta uma nova heurística para o MTSP, capaz de produzir bons limitantes superiores para um algoritmo enumerativo. Esta heurística possui duas fases: uma fase construtiva que é baseada em um grafo em que os vértices correspondem a ferramentas e existe um arco k = (i, j) que liga os vértices i e j se e somente se as ferramentas i e j são necessárias para a execução de alguma tarefa k; e uma fase de refinamento baseada na meta-heurística Busca Local Iterativa. Resultados computacionais mostram que a heurística proposta tem um bom desempenho para os problemas testados, contribuindo para uma redução significativa no número de nós gerados de um algoritmo enumerativo.

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