scholarly journals Predicting shallow landslide size and location across a natural landscape: Application of a spectral clustering search algorithm

2015 ◽  
Vol 120 (12) ◽  
pp. 2552-2585 ◽  
Author(s):  
Dino Bellugi ◽  
David G. Milledge ◽  
William E. Dietrich ◽  
J. Taylor Perron ◽  
Jim McKean
Keyword(s):  
Spectral Clustering ◽  
Search Algorithm ◽  
Shallow Landslide ◽  
Natural Landscape ◽  
Clustering Search

scholarly journals A spectral clustering search algorithm for predicting shallow landslide size and location

2015 ◽  
Vol 120 (2) ◽  
pp. 300-324 ◽  
Author(s):  
Dino Bellugi ◽  
David G. Milledge ◽  
William E. Dietrich ◽  
Jim A. McKean ◽  
J. Taylor Perron ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Search Algorithm ◽  
Shallow Landslide ◽  
Clustering Search

Clustering search algorithm for the capacitated centered clustering problem

2010 ◽  
Vol 37 (3) ◽  
pp. 552-558 ◽  
Author(s):  
Antonio Augusto Chaves ◽  
Luiz Antonio Nogueira Lorena
Keyword(s):  
Search Algorithm ◽  
Clustering Problem ◽  
Clustering Search

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

The main principles and results of pollution monitoring of sediments in the river basin

2020 ◽  
pp. 14-20
Author(s):  
B. Korzhenevsky ◽  
Gleb Tolkachev ◽  
Nikolay Kolomiycev
Keyword(s):  
Heavy Metals ◽  
River Basin ◽  
Water Exchange ◽  
Pollution Monitoring ◽  
Small Rivers ◽  
Volga River ◽  
Natural Landscape ◽  
Volga River Basin ◽  
Ivankovo Reservoir ◽  
Selection Of

The problems of modern geological ecology associated with the study of pollution of sediments of water bodies by heavy metals are considered. The Volga River basin is quite heterogeneous, both in geomorphological and hydrological terms, and in thechnogenical development and usage. A fourrank taxonomy is presented for the selection of sites for monitoring, based on a combination of natural, landscape, climatic and thechnogenical factors. To the largest – the highest taxon – sites of the Ist category – bowls of reservoirs with the slopes and the urban zones, industrial and agricultural structures located within them are carried. Within these areas are allocated to smaller taxa, areas category IInd are the industrial and urban zones, areas category IIIrd are the small rivers without significant contamination and areas category IVth to conduct special observations. The examples of special observations in the study of the annual migration of heavy metals in the system «bottom sediments – water column» on the Ivankovo reservoir are highlighted. The investigations were carried out under the conditions of the standard flow rate for this reservoir and in the conditions of slow water exchange.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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