Distance Criterion Research Articles

Diagnostics of economic security level of Yenisei Siberia regions in the context of socio-economic sphere digitalization processes

Subject. The article addresses treats to the economic security of Russia’s regions in conditions of large-scale introduction of information and communication technologies. Objectives. The purpose is to explore problems of assessing the level of economic security of regional systems, analyze the impact of digitalization on systemic risks and threats to territory development. Methods. The developed approach rests on methods of statistical data processing, the criterion of the shortest distance to the optimum, methods for constructing a fuzzy set and membership function, aggregation of indicators given the probability of occurrence of events. Results. I obtained statistical estimates of the level of economic security and risk of the regions of Yenisei Siberia. The paper establishes the fact of rising risks and a decrease in the level of economic security of the territories, taking into account the influence of digitalization processes. Conclusions. Processes of digital transformation entail an increase in risks and threats to economic security. This problem is particularly acute in the territories that have a smaller margin of safety in terms of parameters and indicators of socio-economic development.

Using Maps to Boost the Urban Proximity: Analysis of the Location of Public Facilities According to the Criteria of the Spanish Urban Agenda

Developing analysis models that promote the sustainability, compactness and social balance of cities is particularly important in addressing post-pandemic urban planning. In this context, the population’s proximity to public facilities is essential for achieving these objectives. Based on this framework, this paper analyses the city of Valladolid (Spain) under the criteria of distance between the population and public facilities proposed by the Spanish Urban Agenda. Specifically, the focus is on calculating the coverage of population with access to the facilities within the recommended distance thresholds using GIS techniques. The methods used relate the facilities with the distance to the population in the census sections, a highly detailed statistical unit. The results have been mapped as a decision making support tool for the city, and show how general coverage of access to facilities for the urban area as a whole is adequate, especially in terms of public transport services, and meets the recommendations of the Spanish Urban Agenda. Maps also reveal how some areas of the city are not covered by most public facilities.

Performance analysis of distance metrics on the exploitation properties and convergence behaviour of the conventional firefly algorithm

The nature of the distance metric used to compare the span between two fireflies in conventional firefly algorithm can significantly enhance the exploitation properties of the algorithm and improve its convergence towards the optimum solution. Therefore, this research proposes several variants of the firefly algorithm based on different distance relations like Minkowski distance, Canberra distance, and Angular Cosine distance. To compare the performance of these variants, benchmark non-linear and non-convex objective functions are solved under the varying nature of the medium absorption coefficient and attractiveness constant. It is observed from detail analysis of results that Canberra distance provides optimal results for almost all of the benchmark functions. Furthermore, the importance of the distance metric for real-world optimization problems with very large or small magnitude of decision variables is also presented by solving a practical hydro thermal scheduling problem. It is also presented that the optimal set of parameters for a particular distance criterion may not always be optimal and better convergence results can be achieved by varying the distance metric to measure the length between the fireflies.

Embolization of Recurrent Pulmonary Arteriovenous Malformations by Ethylene Vinyl Alcohol Copolymer (Onyx®) in Hereditary Hemorrhagic Telangiectasia: Safety and Efficacy.

Objectives: To evaluate short- and long-term safety and efficacy of embolization with Onyx® for recurrent pulmonary arteriovenous malformations (PAVMs) in hereditary hemorrhagic telangiectasia (HHT). Methods: In total, 45 consecutive patients (51% women, mean (SD) age 53 (18) years) with HHT referred to a reference center for treatment of recurrent PAVM were retrospectively included from April 2014 to July 2021. Inclusion criteria included evidence of PAVM recurrence on CT or angiography, embolization using Onyx® and a minimal 1-year-follow-up CT or angiography. Success was defined based on the standard of reference criteria on unenhanced CT or pulmonary angiography if a recurrence was suspected. PAVMs were analyzed in consensus by two radiologists. The absence of safety distance, as defined by a too-short distance for coil/plug deployment, i.e., between 0.5 and 1 cm, between the proximal extremity of the primary embolic material used and a healthy upstream artery branch, was reported. Results: In total, 70 PAVM were analyzed. Mean (SD) follow-up was 3 (1.3) years. Safety distance criteria were missing in 33 (47%) PAVMs. All procedures were technically successful, with a short-term occlusion rate of 100% using a mean (SD) of 0.6 (0.5) mL of Onyx®. The long-term occlusion rate was 60%. No immediate complication directly related to embolization was reported, nor was any severe long-term complication such as strokes or cerebral abscesses. Conclusions: In HHT, treatment of recurrent PAVM with Onyx® showed satisfactory safety and efficacy, with an immediate occlusion rate of 100% and a long-term rate of 60%.

Nonparametric Passenger Flow Monitoring using A Minimum Distance Criterion

Monitoring real-time passenger flow in urban rapid transit systems is very important to maintain social stability and prevent unexpected group events and system failure. To monitor passenger flow, data are collected by sensors deployed in important stations and many existing control charts can be applied. However, because of unknown complex distributions and the requirement to detect shifts of all ranges effectively, conventional methods may perform poorly. Nevertheless, while there are certain charting schemes that truncate the log-likelihood ratio (LLR) function to detect large shifts more quickly, they can cause massive loss of information by truncation, and can only handle particular distributions, leading to unstable online monitoring. In this article, we propose a nonparametric CUSUM charting scheme to monitor passenger flow dynamically. We propose a novel minimum distance criterion is proposed to minimize the functional distance between the objective function and the original LLR function while maintaining its monotonically increasing property. By integrating this concept with kernel density estimation, our proposed chart does not require any parametric process distribution, can be constructed easily in any situation, and is sensitive to shifts of all sizes. Theoretical analysis, simulations and a real application to monitoring passenger flow in the mass transit railway (MTR) in Hong Kong show that our method performs well in various cases.

The 15-Minute City—The Geographical Proximity of Services in Krakow

The discussion about 15-minute cities (also referred to as cities of short distances) has gained considerable momentum in the last decade. Thanks to the optimal spatial layout of these urban areas, their residents can meet most or all of their day-to-day needs within a short walk or a bicycle ride from their place of residence. This is especially important in the context of problems currently faced by most large agglomerations worldwide, the negative externalities caused by predominantly motorised transport and inappropriate spatial planning policies that entail substantial environmental and technical infrastructure outlays. The concept of spatial proximity to services played a key role in the empirical part of the present study, where Krakow’s layout was analysed in detail from the perspective of a 15-minute city. Krakow is a resident-friendly, inclusive city created primarily with local communities in mind. However, certain design flaws in terms of spatial proximity to services prompted us to reconsider how its specific spatial components operate. To achieve the study’s goal—a comprehensive assessment of Krakow as a 15-minute city—we proposed several solutions that can be made universally adaptable for other urban areas striving to meet the short distance criteria.

Frequency control in standalone renewable based-microgrids using steady state load shedding considering droop characteristic

This paper presents a new systematic scheme for designing optimized robust and efficient steady state load shedding (LS) in standalone inverter-based microgrids (IBMGs) considering uncertainties of the load and renewable generations. In order to incorporate different states of non-dispatchable renewable based-Distributed Energy Resources (DERs) and load demand fluctuations, the scenario tree model is implemented. To reduce the computational burden of the proposed model, a scenario-reduction technique using the Kantorovich distance (or Wasserstein distance) criteria is adopted to extract a sufficiently small number of scenarios. To reflect the system frequency in each opted scenario, a new power flow (PF) procedure, in which the system frequency turns up as a supplementary state variable is adopted. This procedure aims at minimizing the required shed loads to maintain the MG frequency over the critical frequency, when the inverters cannot tolerate any overload. According to the optimal curtailed load for different scenarios, a step-wise LS scheme is proposed for applying to an on-line operation manner. The performance of the proposed scheme is evaluated in two typical MV MG in standalone mode, a medium-scale 21 bus test system and a large-scale 118 bus test system, to ensure the efficiency and reliability of the proposed method in real conditions and systems. The obtained results evidence the effectiveness and the correctness of the presented LS scheme to diagnose power shortages and to maintain system frequency in the desirable range. Furthermore, the obtained results by proposed LS model have been compared with the conventional priority-based LS and the superiority of the proposed model over conventional models is proved.

New robot path planning optimization using hybrid GWO-PSO algorithm

Actually, path planning is one of the most crucial aspects of mobile robots study. The primary goal of this research is to develop a solution to the path planning issues that occur when a “mobile robot” operates in a static environment. The problem is handled by finding a collision-free path that meets the given criteria for the shortest distance with quite the smoothness of the path. Two nature-inspired metaheuristic algorithms are used in the computation. By leading a hybrid “gray wolf optimization” with the “particle swarm optimization” (HGWO-PSO) computation that restricts the distance and follows path perfection guidelines, the primary shape is improved. In addition, simulation findings reveal that the proposed HGWO-PSO method is deeply serious in terms of path optimality when compared to path planning approaches such as group search optimizer GSO, PSO, artificial bee colony ABC, and GWO.

Rational Task Assignment and Path Planning Based on Location and Task Characteristics in Mobile Crowdsensing

With the great development in smart devices, mobile crowdsensing (MCS) has been an innovative paradigm for data gathering. Task assignment is a fundamental problem in MCS systems and applications. Previous studies only focused on the assignment of individual tasks, neglecting planning the task processing from a higher level, e.g., making assignments between task locations and workers, which impacts the crowdsensing performance adversely. Furthermore, task characteristics, e.g., route distance, task similarity, and task priority, have a great impact on the rationality of a visiting order and the quality of crowdsensing services. In this article, we tackle the problem of rational task assignment and path planning for MCS, which aims to assign a set of task locations to a set of workers and generate location visiting sequences. We measure the assignment rationality by taking into account geographical information and task characteristics, i.e., route distance, task similarity, and task priority. We prove that the problem of computing the rationality maximization task assignment is NP-hard. For the single-worker scenario, we reduce the problem to a simpler problem with respect to only route distance and task priority criteria since the similarity measurement has a fixed value. We propose an effective greedy algorithm. For the multiple-worker scenario, we first extend the greedy idea by considering all three criteria and then propose an effective approach by reducing the computational complexity of similarity measurement. Extensive experiments show that our proposed approaches achieve promising results.

Communicating With Extremely Large-Scale Array/Surface: Unified Modeling and Performance Analysis

Wireless communications with extremely large-scale array (XL-array) correspond to systems whose antenna sizes are so large that conventional modeling assumptions, such as uniform plane wave (UPW) impingement, are no longer valid. This paper studies the mathematical modeling and performance analysis of XL-array communications. By deviating from the conventional modeling approach that treats the array elements as sizeless points, we explicitly model their physical area/aperture, which enables a unified modeling for the classical discrete antenna arrays and the emerging active continuous surfaces. As such, a generic array/surface model that accurately takes into account the variations of signal phase, amplitude and projected aperture across array elements is proposed. Based on the proposed model, a closed-form expression of the resulting signal-to-noise ratio (SNR) with the optimal single-user maximum ratio combining/transmission (MRC/MRT) beamforming is derived. The expression reveals that instead of scaling linearly with the antenna number <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> as in conventional UPW modeling, the SNR with the more generic model increases with <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> with diminishing return, which is governed by the collective properties of the array, such as the <i>array occupation ratio</i> and the physical sizes of the array along each dimension, while irrespective of the properties of the individual array element. In addition, we have derived an alternative insightful expression for the optimal SNR in terms of the <i>vertical</i> and <i>horizontal angular spans</i>, which are fully determined by the geometric angles formed by the array/surface and user location. Furthermore, we also show that our derived results include the far-field UPW modeling as a special case. One important finding during the study of far-field approximation is the necessity to introduce a new distance criterion to complement the classical Rayleigh distance, termed <i>uniform-power distance</i> (UPD), which concerns the signal amplitude/power variations across array elements, instead of phase variations as for Rayleigh distance. Extensive numerical results are provided to demonstrate the necessity of proper modeling for XL-array communications by comparing the proposed model with various benchmark models.

A 3D SEGMENTS BASED ALGORITHM FOR HETEROGENEOUS DATA REGISTRATION

Abstract. Combining image and LiDAR draws increasing interest in surface reconstruction, city and building modeling for constructing 3D virtual reality models because of their complementary nature. However, to gain from this complementarity, these data sources must be precisely registered. In this paper, we propose a new primitive based registration algorithm that takes 3D segments as features. The objective of the proposed algorithm is to register heterogeneous data. The heterogeneity is both in data type (image and LiDAR) and acquisition platform (terrestrial and aerial). Our algorithm starts by extracting 3D segments from LiDAR and image data with state of the art algorithms. Then it clusters the 3D segments of each data according to their directions. The obtained clusters are associated to find possible rotations, then 3D segments from associated clusters are matched in order to find the translation and scale factor minimizing a distance criteria between the two sets of 3D segments. Two optimizers (simulated annealing and RANSAC) are tested to minimize this distance criterion, first on synthetic data, then on real data. The experiments carried out demonstrate the robustness and speed of RANSAC compared to simulated annealing.

Heart rate acceleration during cardioneuroablation is a weak predictor of significantly reduced parasympathetic modulation of sinus node

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Ablation of superior parasympathetic ganglia is associated with acceleration of sinus rhythm (SR). This has been considered a favorable sign during cardioneuroablation (CNA) for the treatment of functional bradyarrhythmias. Aim We studied whether the relative increase in SR frequency (DeltaSR) during CNA is a reliable predictor of significantly reduced parasympathetic modulation of the sinus node (SAN). Methods In patients undergoing CNA under general anesthesia, the gradual reduction in parasympathetic modulation of SAN during the procedure was assessed by extracardiac right vagal nerve stimulation (ECVS). The response to ECVS was quantified by the ratio of the maximum P-P interval (induced by ECVS) and the baseline SR cycle length (MaxPPratio). The ECVS was performed repeatedly after partial ablation steps, and therefore several pairs of DeltaSR and MaxPPratio values were obtained in the course of the single procedure. A MaxPPratio &amp;lt;1.5 was arbitrarily chosen as the criterion of significant attenuation of vagally induced responses. The optimum dichotomy of Delta-SR for the prediction of the MaxPPratio &amp;lt;1.5 was found according to the criterion of the minimum distance of the ROC curve from the point (0; 1). Results The study included 64 patients (mean age: 42 ± 16 years, 48% men). A total of 188 intraprocedural pairs of DeltaSR and MaxPPratio (2.9 ± 2.1 pairs per procedure) covering the wide distribution of their values (19 ± 14 bpm for DeltaSR and 2.9 ± 2.8 for MaxPPratio) were obtained. One half of ECVS tests (51%) met the criterion of MaxPPratio &amp;lt;1.5. In the analysis of receiver operating characteristic, DeltaSR as a predictor of significantly reduced parasympathetic modulation of SAN showed an area under the curve (AUC) of 0.69 with 95% confidence interval (CI) of 0.62 - 0.77 (Figure). The optimum cut-off (DeltaSR ≥20 bpm) had a sensitivity (SENS), specificity (SPEC), positive (PPV), and negative predictive value (NPV) of 61%, 78%, 75%, and 66%, respectively. Conclusion Acceleration of SR during CNA is not a relevant surrogate of significantly reduced parasympathetic modulation of SAN. Elimination or significant suppression of responses of SAN to ECVS serves as an excellent procedural endpoint that might contribute to the favorable clinical outcome of the CNA.

Accident type analysis and consequence simulation of LNG terminals equipment and facilities

A new round of energy change, energy clean and low carbon development become an inevitable trend. In the adjustment of energy structure, natural gas becomes the strongest growth carrier and LNG reserve peak regulation is of great significance. Building liquefied natural gas receiving center to secure domestic natural gas supply has become the main target, task and key work. Through the simulation of various equipment and facilities in the LNG terminal under different leakage scenarios, the accident scenarios are selected according to the protection distance criteria, the simulation of steam diffusion, pool fire, jet fire and explosion is carried out, and the influence range of consequences is obtained. The thesis provides a theoretical basis for the quantitative risk assessment for LNG terminal accident and the prediction of emergencies, having positive guiding significance for risk management and emergency disposal of new or operating LNG terminals.

An energy aware cluster‐based routing and adaptive semi‐synchronized MAC for energy harvesting WSN

AbstractMedium access control (MAC) layer scheduling is an intelligent solution to protract the lifetime of energy harvesting wireless sensor network (WSN) which is challenging issue in recent times. Self‐adaptive MAC protocol was working upon reinforcement learning. However, this decentralized adaptive scheduling introduces collisions and degrades the overall network performance. To fix this problem, this paper proposes novel semi‐synchronized MAC approach for energy harvesting WSN. The major objective of this work is to improve energy efficiency of the network by minimizing energy consumption which is the major issue in WSN. Proposed WSN network is modeled with dual mobile sinks with circular moving path and static sensor nodes to monitor the environment. Our novel MAC approach is initiated with unequal cluster formation by correlation based unequal clustering scheme in which multi‐objective elephant herding optimization (CUC‐MEHO) algorithm is incorporated for cluster head (CH) selection. In clustered network, all sensor nodes follow semi‐synchronized adaptive active period MAC scheduling (S2A2MAC) protocol. In this work, S2A2MAC operates upon hidden Markov model (HMM) which controls the active time period of a node in an adaptive manner. Energy efficient hop‐by‐hop intra‐cluster routing is empowered by Bayes rule enabled perfect matching (BR‐PM) algorithm. Before route selection, all candidate nodes are regarded into perfect match, partially match, and no match nodes. Furthermore, emergency data is identified by CH based on flag value and transmitted without any time delay. For emergency data transmission, extremity aware routing (EAR) algorithm is presented and EAR algorithm is functioning on hopcount and distance criteria. Finally, proposed network is evaluated by extensive simulations in NS‐3.26. Simulation upshots show better performance in percentage of dead nodes, network lifetime, energy consumption, PDR, and delay.

Temporal Clustering of the Causes of Death for Mortality Modelling

Actuaries utilize demographic features such as mortality and longevity rates for pricing, valuation, and reserving life insurance and pension contracts. Capturing accurate mortality estimates requires factual mortality assumptions in mortality models. However, the dynamic and uncertain nature of mortality improvements and deteriorations necessitates better approaches in tracking mortality changes, for instance, using the causes of deaths features. This paper aims to determine temporal homogeneous clusters using unsupervised learning, a clustering approach to group causes of death based on (dis)similarity measures to set representative clusters in detection and monitoring death trends. The causes of death dataset were derived from the World Health Organization, Global Health Estimates for males and females, from 2000 to 2019, for Kenya. A hierarchical agglomerative clustering technique was implemented with modified Dynamic Time Warping distance criteria. Between 6 and 14 clusters were optimally achieved for both males and females. Using visualisations, principal clusters were detected. Over time, the causes of death trends of these clusters have demonstrated a correlated association with mortality and longevity rates, rationalizing why insurance and pension offices may include this approach as a preliminary step to undertake mortality and longevity modelling.

Dual AGN in the Horizon-AGN simulation and their link to galaxy and massive black hole mergers, with an excursus on multiple AGN

ABSTRACT The occurrence of dual active galactic nuclei (AGN) on scales of a few tens of kpc can be used to study merger-induced accretion on massive black holes (MBHs) and to derive clues on MBH mergers, using dual AGN as a parent population of precursors. We investigate the properties of dual AGN in the cosmological simulation Horizon-AGN. We create catalogs of dual AGN selected with distance and luminosity criteria, plus sub-catalogs where further mass cuts are applied. We divide the sample into dual AGN hosted in different galaxies, on the way to a merger, and into those hosted in one galaxy, after the galaxy merger has happened. We find that the relation between MBH and galaxy mass is similar to that of general AGN population and we compare the properties of dual AGN also with a control sample, discussing differences and similarities in masses and Eddington ratios. The typical mass ratio of galaxy mergers associated to dual AGN is 0.2, with mass loss in the smaller galaxy decreasing the mass ratio as the merger progresses. Between 30 and 80 per cent of dual AGN with separations between 4 and 30 kpc can be matched to an ensuing MBH merger. The dual AGN fraction increases with redshift and with separation threshold, although above 50 kpc the increase of multiple AGN limits that of duals. Multiple AGN are generally associated with massive haloes, and mass loss of satellites shapes the galaxy–halo relation.

Effectiveness of Ehteraz digital contact tracing app versus conventional contact tracing in managing the outbreak of COVID-19 in the State of Qatar

IntroductionContact tracing is an integral component of the overall public health strategy for controlling the outbreak of infectious diseases such as COVID-19. In addition to the conventional contact tracing, Qatar...

A spatially based machine learning algorithm for potential mapping of the hearing senses in an urban environment

• Four modes of hearing senses (normal, annoying, stressful, and pleasant) were examined in an urban environment. • The relationship between people's perceived hearing senses and spatial criteria was examined. • Spatial modeling and potential mapping of hearing senses were performed using a random forest (RF) algorithm. • The results showed that the RF algorithm had good accuracy in the potential mapping of hearing senses. Mapping individuals’ sense of hearing in the urban environment helps urban managers and planners accomplish goals such as creating a favorable urban environment for the citizens. The present study has been conducted to address the lack of modeling and compilation of a sense of hearing potential map in the urban environment and can help urban managers and planners make decisions in this regard. The present study aims for spatial modeling of people's hearing senses and developing potential maps for various hearing states in Tehran, Iran, using a random forest (RF) machine learning algorithm. The four various states, including pleasant sound, annoying sound, normal sound, and stressful sound, have been considered in the present study for the sounds that can be heard in the city. First, a spatial database made up of dependent, and independent data was built. Dependent data included people's four states of hearing in the urban environment, and the respective data was collected through a questionnaire from 657 people. Independent data were categorized into four groups. The first group was traffic related noises including the criteria of traffic volume and equivalent continuous sound level (Leq), the second group included land use related criteria of distance to cemetery, distance to sports areas, distance to commercial areas, distance to primary streets, distance to secondary streets, distance to park, and distance to industrial areas. Furthermore, the public facilities related group includes the distance to airports and distance to public transportation stations, and the population related group includes population density. 70% of the data were used for training, and 30% were set aside for validation. The spatial database was used to develop the potential map for the four states of the hearing sense in the urban environment using the RF algorithm. The potential hearing sense map was evaluated using the receiver operating characteristic (ROC) curve and the respective area under the curve (AUC). The AUC values were 0.930, 0.957, 0.950, and 0.903 for each of the pleasant, annoying, normal, and stressful states, respectively. There was a higher potential for pleasant sounds in the northern districts and some of the central ones, for annoying sounds mainly in the central districts, for normal sounds in central and southern districts, and stressful sounds in some parts of the central and mainly southern districts.

A Systematic Molecular Epidemiology Screen Reveals Numerous Human Immunodeficiency Virus (HIV) Type 1 Superinfections in the Swiss HIV Cohort Study.

Studying human immunodeficiency virus type 1 (HIV-1) superinfection is important to understand virus transmission, disease progression, and vaccine design. But detection remains challenging, with low sampling frequencies and insufficient longitudinal samples. Using the Swiss HIV Cohort Study (SHCS), we developed a molecular epidemiology screening for superinfections. A phylogeny built from 22 243 HIV-1 partial polymerase sequences was used to identify potential superinfections among 4575 SHCS participants with longitudinal sequences. A subset of potential superinfections was tested by near-full-length viral genome sequencing (NFVGS) of biobanked plasma samples. Based on phylogenetic and distance criteria, 325 potential HIV-1 superinfections were identified and categorized by their likelihood of being detected as superinfections due to sample misidentification. NFVGS was performed for 128 potential superinfections; of these, 52 were confirmed by NFVGS, 15 were not confirmed, and for 61 sampling did not allow confirming or rejecting superinfection because the sequenced samples did not include the relevant time points causing the superinfection signal in the original screen. Thus, NFVGS could support 52 of 67 adequately sampled potential superinfections. This cohort-based molecular approach identified, to our knowledge, the largest population of confirmed superinfections, showing that, while rare with a prevalence of 1%-7%, superinfections are not negligible events.

Modified swarm intelligence algorithms for the pharmacy duty scheduling problem

Assigning duties to pharmacies to serve the public on weekends, nights, and public holidays is known as the pharmacy duty scheduling problem (PDSP). In this study, a p-median model is proposed with a case-specific constraint that provides the distance restriction between on-duty pharmacies. We adapted Binary and Random-Key versions of the most recent and popular Swarm Intelligence (SI) algorithms, which are Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Dragonfly Algorithm (DA), and Harris Hawks Optimization (HHO) to solve it in a reasonable amount of time since the PDSP is known to be an NP-Hard problem. We also proposed several enhancements to the algorithms and conducted computational tests on real cases generated instances using Geographic Information System (GIS) tools to compare the performances of the proposed algorithms with the state-of-the-art general-purpose solver. Two real-world datasets, called DS1 and DS2, are built considering the demand points. To validate the proposed algorithms, the CPLEX results, obtained with the exact solution for the DS1 dataset, are used. Compared to the MIP results for 800 meters distance constraint as an instance, maximum relative errors for Binary and Random-Key GWO, PSO, DA, and HHO are 0.07, 0.08, 0.08, and 0.08, respectively. The success of the algorithms is similar to the other distance criteria as well. Moreover, the results for the large DS2 dataset show that the proposed Binary GWO is more effective and offers efficient solutions compared to the other swarm intelligence algorithms and CPLEX solvers in terms of convergence to optimization and solution time.