Soft Computing Techniques for Blood Bank Inventory Model for Decaying Items with Storage Using Particle Swarm Optimization

2020 ◽  
pp. 89-98
Author(s):  
Ajay Singh Yadav ◽  
Anupam Swami ◽  
Navin Ahlawat ◽  
Dhowmya Bhatt ◽  
Tripti Pandey
Keyword(s):  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Inventory Model ◽  
Blood Bank ◽  
Swarm Optimization ◽  
Soft Computing Techniques

scholarly journals Performance Analysis of Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization for solving the Travelling Salesman Problem

2019 ◽  
Vol 8 (2S4) ◽  
pp. 91-95
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Soft Computing ◽  
Travelling Salesman Problem ◽  
Particle Swarm ◽  
Ant Colony ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Soft Computing Techniques

The Travelling salesman problem also popularly known as the TSP, which is the most classical combinatorial optimization problem. It is the most diligently read and an NP hard problem in the field of optimization. When the less number of cities is present, TSP is solved very easily but as the number of cities increases it gets more and more harder to figure out. This is due to a large amount of computation time is required. So in order to solve such large sized problems which contain millions of cities to traverse, various soft computing techniques can be used. In this paper, we discuss the use of different soft computing techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and etc. to solve TSP.

scholarly journals Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

2017 ◽  
Vol 7 (1.1) ◽  
pp. 184
Author(s):  
Rincy Merlin Mathew ◽  
S. Purushothaman ◽  
P. Rajeswari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Band 3 ◽  
Landsat 8 ◽  
Swarm Optimization ◽  
Landsat 7 ◽  
Performance Comparisons

This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

scholarly journals Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects

2020 ◽  
Vol 6 (2) ◽  
pp. 384-401 ◽  
Author(s):  
Tarq Zaed Khalaf ◽  
Hakan Çağlar ◽  
Arzu Çağlar ◽  
Ammar Nasiri Hanoon
Keyword(s):  
Particle Swarm Optimization ◽  
Construction Projects ◽  
Particle Swarm ◽  
Building Design ◽  
Construction Costs ◽  
General Applicability ◽  
Swarm Optimization ◽  
Soft Computing Techniques ◽  
Concrete Footing ◽  
Duration Estimation

Cost and duration estimation is essential for the success of construction projects. The importance of decision making in cost and duration estimation for building design processes points to a need for an estimation tool for both designers and project managers. Particle swarm optimization (PSO), as the tools of soft computing techniques, offer significant potential in this field. This study presents the proposal of an approach to the estimation of construction costs and duration of construction projects, which is based on PSO approach. The general applicability of PSO in the formulated problem with cost and duration estimation is examined. A series of 60 projects collected from constructed government projects were utilized to build the proposed models. Eight input parameters, such as volume of bricks, the volume of concrete, footing type, elevators number, total floors area, area of the ground floor, floors number, and security status are used in building the proposed model. The results displayed that the PSO models can be an alternative approach to evaluate the cost and-or duration of construction projects. The developed model provides high prediction accuracy, with a low mean (0.97 and 0.99) and CoV (10.87% and 4.94%) values. A comparison of the models’ results indicated that predicting with PSO was importantly more precise.

A Comparative Study on Inverse Vibration Based Damage Assessment Techniques in Beam Structure Using Ant Colony Optimization and Particle Swarm Optimization

2020 ◽  
Vol 12 (7) ◽  
pp. 918-923
Author(s):  
Aditi Majumdar ◽  
Bharadwaj Nanda
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Damage Assessment ◽  
Damage Identification ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Soft Computing Techniques ◽  
Frequency Changes

Use of swarm intelligence has proliferated over previous couple of years for damage assessment in large and complex structures using vibration data. Available literatures shows ‘ant colony optimization’ (ACO) and ‘particle swarm optimization’ (PSO) are predominantly used for solving complex engineering problems including damage identification and quantification problems. The time requirement and accuracy of the vibration based damage identification algorithms depends on early exploration and late exploitation capabilities of soft computing techniques. However, there are not any literature available comparing algorithms on these bases. In the current study, an inverse problem is constructed using the natural frequency changes which is then solved using ACO and PSO algorithms. The algorithm is run for identification of single and multiple damages in simple support and cantilever beam structures. It's found that, both ACO and PSO based algorithms are capable of detecting and quantifying the damage accurately within the limited number of iterations. However, ACO based algorithm by virtue of its good exploration capability is able to identify near optimal region faster than PSO based algorithm, whereas PSO algorithm has good exploitation capability and hence able to provide better damage quantification than ACO algorithm at latter stages of iteration. Further, PSO based algorithm takes less time to reach at required accuracy level. It is also observed that, the time required for these algorithms are independent of numbers of damage and support conditions.

scholarly journals Genetic Algorithm and Particle Swarm Optimization Techniques for Inverted Pendulum Stabilization

2020 ◽  
Vol 9 (6) ◽  
pp. 657-660
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Evolutionary Computation ◽  
Inverted Pendulum ◽  
Biological Evolution ◽  
Particle Swarm ◽  
Continuous Variable ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Soft Computing Techniques

Inverted Pendulum is a popular non-linear, unstable control problem where implementation of stabilizing the pole angle deviation, along with cart positioning is done by using novel control strategies. Soft computing techniques are applied for getting optimal results. The evolutionary computation forms the key research area for adaptation and optimization. The approach of finding optimal or near optimal solutions to the problem is based on natural evolution in evolutionary computation. The genetic algorithm is a method based on biological evolution and natural selection for solving both constrained and unconstrained problems. Particle swarm optimization is a stochastic search method inspired by collective behavior of animals like flocking of birds, schooling of fishes, swarming of bees etc. that is suited to continuous variable problems. These methods are applied to the inverted pendulum problem and their performance studied.

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