Comparative study on nature inspired algorithms for optimization problem

2017 ◽  
Author(s):  
Ishani Luthra ◽  
Shubham Krishna Chaturvedi ◽  
Divya Upadhyay ◽  
Richa Gupta
Keyword(s):  
Comparative Study ◽  
Optimization Problem ◽  
Nature Inspired Algorithms

Nature-Inspired Algorithms in Wireless Sensor Networks

2019 ◽  
pp. 246-275
Author(s):  
Ajay Kaushik ◽  
S. Indu ◽  
Daya Gupta
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Sensor Node ◽  
Optimization Problem ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Nature Inspired Algorithms

Wireless sensor networks (WSNs) are becoming increasingly popular due to their applications in a wide variety of areas. Sensor nodes in a WSN are battery operated which outlines the need of some novel protocols that allows the limited sensor node battery to be used in an efficient way. The authors propose the use of nature-inspired algorithms to achieve energy efficient and long-lasting WSN. Multiple nature-inspired techniques like BBO, EBBO, and PSO are proposed in this chapter to minimize the energy consumption in a WSN. A large amount of data is generated from WSNs in the form of sensed information which encourage the use of big data tools in WSN domain. WSN and big data are closely connected since the large amount of data emerging from sensors can only be handled using big data tools. The authors describe how the big data can be framed as an optimization problem and the optimization problem can be effectively solved using nature-inspired algorithms.

Layout Optimization Base on Election Campaign Algorithm

2013 ◽  
Vol 655-657 ◽  
pp. 2386-2391
Author(s):  
Qing Hua Xie ◽  
Xiang Wei Zhang ◽  
Wen Ge Lv ◽  
Chun Tao Lin
Keyword(s):  
Comparative Study ◽  
Optimization Problem ◽  
Layout Optimization ◽  
Election Campaign ◽  
Recycling System ◽  
System Process

Waste appliances have become new urban polluter because of the replacing of appliances households and corporates throw away. A case of recycling system of waste appliances in China is on the agenda. As the recycling system process, the layout optimization problem come out, we present a solution by using a new algorithm named Election campaign algorithm (ECA). ECA acts by simulating the behavior that the election candidates pursue the highest support in election campaign. Our comparative study indicates that ECA has good performance in this kind of optimization cases.

scholarly journals Stabilization of Inverted Cart-Pendulum System Using Controller: A Frequency-Domain Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Sunil Kumar Mishra ◽  
Dinesh Chandra
Keyword(s):  
Particle Swarm Optimization ◽  
Comparative Study ◽  
Frequency Domain ◽  
Pid Controller ◽  
Optimization Problem ◽  
Optimization Technique ◽  
Swarm Optimization ◽  
Pendulum System ◽  
Particle Swarm Optimization Technique ◽  
Frequency Domain Approach

This paper focuses on the angular stabilization of inverted cart-pendulum system using controller. The tuning of controller is formulated as a nonlinear optimization problem, in which the objective function is composed of five design conditions in frequency domain. Particle swarm optimization technique has been used for optimizing parameters. Also a PID controller has been designed based on same specifications, and a comparative study has been carried out. All the responses have been calculated using FOMCON toolbox of Matlab/Simulink.

scholarly journals A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 874
Author(s):  
Zhenwu Wang ◽  
Chao Qin ◽  
Benting Wan ◽  
William Wei Song
Keyword(s):  
Comparative Study ◽  
Heuristic Algorithms ◽  
Black Box ◽  
Function Optimization ◽  
Great Effort ◽  
Research Directions ◽  
Contrastive Study ◽  
Comprehensive Survey ◽  
Common Nature ◽  
Nature Inspired Algorithms

Over previous decades, many nature-inspired optimization algorithms (NIOAs) have been proposed and applied due to their importance and significance. Some survey studies have also been made to investigate NIOAs and their variants and applications. However, these comparative studies mainly focus on one single NIOA, and there lacks a comprehensive comparative and contrastive study of the existing NIOAs. To fill this gap, we spent a great effort to conduct this comprehensive survey. In this survey, more than 120 meta-heuristic algorithms have been collected and, among them, the most popular and common 11 NIOAs are selected. Their accuracy, stability, efficiency and parameter sensitivity are evaluated based on the 30 black-box optimization benchmarking (BBOB) functions. Furthermore, we apply the Friedman test and Nemenyi test to analyze the performance of the compared NIOAs. In this survey, we provide a unified formal description of the 11 NIOAs in order to compare their similarities and differences in depth and a systematic summarization of the challenging problems and research directions for the whole NIOAs field. This comparative study attempts to provide a broader perspective and meaningful enlightenment to understand NIOAs.

A Survey of Nature-Inspired Algorithms With Application to Well Placement Optimization

2020 ◽  
pp. 32-45 ◽  
Author(s):  
Jahedul Islam ◽  
Pandian M. Vasant ◽  
Berihun Mamo Negash ◽  
Moacyr Bartholomeu Laruccia ◽  
Myo Myint
Keyword(s):  
Optimization Problem ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Economic Factors ◽  
Well Placement ◽  
Field Development ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Nature Inspired Algorithms ◽  
Metaheuristic Techniques

Well placement optimization is one of the major challenging factors in the field development process in the oil and gas industry. This chapter aims to survey prominent metaheuristic techniques, which solve well the placement optimization problem. The well placement optimization problem is considered as high dimensional, discontinuous, and multi-model optimization problem. Moreover, the computational expenses further complicate the issue. Over the last decade, both gradient-based and gradient-free optimization methods were implemented. Gradient-free optimization, such as the particle swarm optimization, genetic algorithm, is implemented in this area. These optimization techniques are utilized as standalone or as the hybridization of optimization methods to maximize the economic factors. In this chapter, the authors survey the two most popular nature-inspired metaheuristic optimization techniques and their application to maximize the economic factors.

scholarly journals A comparative study of smoothing approximations

1996 ◽  
Vol 38 (2) ◽  
pp. 194-200 ◽  
Author(s):  
X. Q. Yang
Keyword(s):  
Comparative Study ◽  
Optimization Problem ◽  
Nondifferentiable Optimization ◽  
Optimization Problems ◽  
Approximation Methods ◽  
Computational Results ◽  
Composite Optimization ◽  
Error Analyses

AbstractIt is known that many optimization problems can be reformulated as composite optimization problems. In this paper error analyses are provided for two kinds of smoothing approximation methods of a unconstrained composite nondifferentiable optimization problem. Computational results are presented for nondifferentiable optimization problems by using these smoothing approximation methods. Comparisons are made among these methods.

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