Design of an Urban Transport Network for the Optimal Location of Bus Stops in a Smart City Based on a Big Data Model and Spider Monkey Optimization Algorithm

2021 ◽  
pp. 167-201
Author(s):  
Marlyn Montalvo-Martel ◽  
Alberto Ochoa-Zezzatti ◽  
Elías Carrum ◽  
Denise Barzaga
Keyword(s):  
Big Data ◽  
Optimization Algorithm ◽  
Data Model ◽  
Smart City ◽  
Optimal Location ◽  
Urban Transport ◽  
Spider Monkey ◽  
Transport Network ◽  
Spider Monkey Optimization ◽  
Bus Stops

scholarly journals Sustainable Smart Cities: A Fog Computing Framework for a Smart Urban Transport Network

2018 ◽  
Vol 28 (4) ◽  
pp. 68-80 ◽  
Author(s):  
Ioan-Mădălin Neagu
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Fog Computing ◽  
Urban Transport ◽  
Transport Network ◽  
Social Implications ◽  
End User ◽  
Funding Sources ◽  
Computing Framework

Abstract In the present paper, a fog computing framework for smart urban transport is developed. The proposed framework is adapted to the smart city concept. It uses a collaborative multitude of end-user clients to carry out a substantial amount of communication and computation. It can be adapted for specific situations of smart cities in Romania, such as: Cluj-Napoca, Timișoara, Iași or Bucharest. Economic and social implications as well as available European funding sources are presented.

Ageist Spider Monkey Optimization algorithm

2016 ◽  
Vol 28 ◽  
pp. 58-77 ◽  
Author(s):  
Avinash Sharma ◽  
Akshay Sharma ◽  
B.K. Panigrahi ◽  
Deep Kiran ◽  
Rajesh Kumar
Keyword(s):  
Optimization Algorithm ◽  
Spider Monkey ◽  
Spider Monkey Optimization

scholarly journals Improve the local search using Automatic Modified Position in Improved Spider Monkey Optimization Algorithm

2019 ◽  
Vol 8 (6) ◽  
pp. 1634-1637
Keyword(s):  
Local Search ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Research Paper ◽  
Population Based ◽  
Spider Monkey ◽  
Alternative Technique ◽  
Spider Monkey Optimization ◽  
Smo Algorithm

Spider Monkey Optimization is the new field of Swarm Intelligence. The SMO algorithms well balanced for a good exploration. Algorithm based on Spider's extraordinary behavior. Monkeys the SMO algorithm is a population-based meta-heuristic. So these articles present automatic modifying the position of the local search to improve its position. Then we say the updating algorithm called Improved Spider Monkey Optimization algorithm. Using this alternative technique we improve speed convergence. Also this algorithm tested on the problems of reference. The research paper shows proposes a productive variant of SMO that improves the Number of function. Here we have some equations to resolve these problems also we compare the result between SMO and new ISMO

Hyperbolic Spider Monkey Optimization Algorithm

2020 ◽  
Vol 13 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Sandeep Kumar ◽  
Anand Nayyar ◽  
Nhu Gia Nguyen ◽  
Rajani Kumari
Keyword(s):  
Optimization Algorithm ◽  
Growth Function ◽  
Spider Monkey ◽  
Benchmark Problems ◽  
Spider Monkeys ◽  
Rapid Convergence ◽  
New Approach ◽  
Spider Monkey Optimization ◽  
Increasing Function ◽  
Search Capability

Background: Spider monkey optimization algorithm is recently developed natureinspired algorithm. It is based on fission-fusion social structure of spider monkeys. Perturbation rate is one of the important parameter of spider monkey optimization algorithm, which affects the convergence behavior of spider monkey optimization algorithm. Generally, perturbation rate is a linearly increasing function. However, due to the availability of non-linearity in different applications, a non-linear function may affect the performance of spider monkey optimization algorithm. Objective: This paper provides a detailed study on various perturbation techniques used in spider monkey optimization algorithm and recommends a novel alternative of hyperbolic spider monkey optimization algorithm. The new approach is named as hyperbolic Spider Monkey Optimization algorithm as the perturbation strategy inspired by hyperbolic growth function. Methods: The proposed algorithm is tested over a set of 23 CEC 2005 benchmark problems. Results: The experimental outcomes illustrate that the hyperbolic spider monkey optimization algorithm effectively increase the reliability of spider monkey optimization algorithm in comparison to the considered approaches. Conclusion: The hyperbolic spider monkey optimization algorithm provides improved perturbation rate, desirable convergence precision, rapid convergence rate, and improved global search capability.

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