Abstract:Nature-inspired algorithms are based on the concepts of self-organization and complex biological systems. They have been designed by researchers and scientists to solve complex problems in various environmental situations by observing how naturally occurring phenomena behave. The introduction of nature-inspired algorithms has led to new branches of study such as neural networks, swarm intelligence, evolutionary computation, and artificial immune systems. Particle swarm optimization (PSO), social spider optimiz… Show more
“…In the region, psycho-social support activities were provided. 2 In the fault line map, dark red areas represent regions with high earthquake potential, and highlighted part of the map indicates the recent earthquake region.…”
Section: Introductionmentioning
confidence: 99%
“…Analyses of these data are significant to find out problems and developing a solution. Therefore, data clustering is one of the most utilized unsupervised classification mechanisms in data mining for summarization huge amount of data-sets [1,2]. Recently, swarm intelligence algorithms such as the Artificial Bee Colony approach [3], Particle Swarm Optimization (PSO) approach [4], Ant Colony Optimization (ACO) approach [5] and evolutionary algorithms such as the Genetic Algorithm approach [6,7] have been applied by researchers to solve high-dimensional optimization problems in text clustering area.…”
With the increase in accumulated data and usage of the Internet, social media such as Twitter has become a fundamental tool to access all kinds of information. Therefore, it can be expressed that processing, preparing data, and eliminating unnecessary information on Twitter gains its importance rapidly. In particular, it is very important to analyze the information and make it available in emergencies such as disasters. In the proposed study, an earthquake with the magnitude of Mw = 6.8 on the Richter scale that occurred on January 24, 2020, in Elazig province, Turkey, is analyzed in detail. Tweets under twelve hashtags are clustered separately by utilizing the Social Spider Optimization (SSO) algorithm with some modifications. The sum-of intra-cluster distances (SICD) is utilized to measure the performance of the proposed clustering algorithm. In addition, SICD, which works in a way of assigning a new solution to its nearest node, is used as an integer programming model to be solved with the GUROBI package program on the test data-sets. Optimal results are gathered and compared with the proposed SSO results. In the study, center tweets with optimal results are found by utilizing modified SSO. Moreover, results of the proposed SSO algorithm are compared with the K-means clustering technique which is the most popular clustering technique. The proposed SSO algorithm gives better results. Hereby, the general situation of society after an earthquake is deduced to provide moral and material supports.
“…In the region, psycho-social support activities were provided. 2 In the fault line map, dark red areas represent regions with high earthquake potential, and highlighted part of the map indicates the recent earthquake region.…”
Section: Introductionmentioning
confidence: 99%
“…Analyses of these data are significant to find out problems and developing a solution. Therefore, data clustering is one of the most utilized unsupervised classification mechanisms in data mining for summarization huge amount of data-sets [1,2]. Recently, swarm intelligence algorithms such as the Artificial Bee Colony approach [3], Particle Swarm Optimization (PSO) approach [4], Ant Colony Optimization (ACO) approach [5] and evolutionary algorithms such as the Genetic Algorithm approach [6,7] have been applied by researchers to solve high-dimensional optimization problems in text clustering area.…”
With the increase in accumulated data and usage of the Internet, social media such as Twitter has become a fundamental tool to access all kinds of information. Therefore, it can be expressed that processing, preparing data, and eliminating unnecessary information on Twitter gains its importance rapidly. In particular, it is very important to analyze the information and make it available in emergencies such as disasters. In the proposed study, an earthquake with the magnitude of Mw = 6.8 on the Richter scale that occurred on January 24, 2020, in Elazig province, Turkey, is analyzed in detail. Tweets under twelve hashtags are clustered separately by utilizing the Social Spider Optimization (SSO) algorithm with some modifications. The sum-of intra-cluster distances (SICD) is utilized to measure the performance of the proposed clustering algorithm. In addition, SICD, which works in a way of assigning a new solution to its nearest node, is used as an integer programming model to be solved with the GUROBI package program on the test data-sets. Optimal results are gathered and compared with the proposed SSO results. In the study, center tweets with optimal results are found by utilizing modified SSO. Moreover, results of the proposed SSO algorithm are compared with the K-means clustering technique which is the most popular clustering technique. The proposed SSO algorithm gives better results. Hereby, the general situation of society after an earthquake is deduced to provide moral and material supports.
Modelling freight logistics is challenging due to the variable consignments and diverse customers. Discrete-event Simulation (DES) is an approach that can model freight logistics and incorporate stochastic events. However, the flexible delivery routes of Pickup and Delivery (PUD) are still problematic to simulate. This research aims to develop last-mile delivery architecture in DES and evaluate the credibility of the model. A two-tier architecture was proposed and integrated with a DES model to simulate freight operations. The geographic foundation of the model was determined using Geographic Information Systems (GIS), including identifying customer locations, finding cluster centres, and implementing Travelling Salesman Problem (TSP) simulation. This complex model was simplified to the two-tier architecture with stochastic distances, which is more amenable to DES models. The model was validated with truck GPS data. The originality of the work is the development of a novel and simple methodology for developing a logistics model for highly variable last-mile delivery.
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