scholarly journals Implicit Search-Space Aware Cofactor Expansion: A Novel Preimage Computation Technique

2006 ◽  
Author(s):  
Kameshwar Chandrasekar ◽  
Michael S. Hsiao
Keyword(s):  
Search Space ◽  
Computation Technique

scholarly journals A Future Perspective Survey on Bio-inspired algorithms based self-organization techniques for GA

2018 ◽  
Vol 7 (4.6) ◽  
pp. 4
Author(s):  
Santhoshini Banda ◽  
U. Sri Lakshmi ◽  
P. Victer Paul
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Search Space ◽  
Self Organization ◽  
Biological Behavior ◽  
Future Perspective ◽  
Organization Behavior ◽  
Combinatorial Optimization Problems ◽  
Computation Technique ◽  
New Algorithms

Genetic algorithms (GAs) are the most important evolutionary computation technique that is used to solve various complex problems that involve a large search space. To have a performance improvement over GA the concept of Hybrid genetic algorithms that were inspired by the biological behavior of different living beings was put to use to solve the NP-completeness problems. In this paper, a survey on the various recent working HGA with bio-inspired algorithms that exhibits self-organization behavior is performed. This paper discusses the various Biological self-organization behaviors and the generalized self-organization behaviors that are used to solve combinatorial optimization problems. This paper helps the scholars and researchers to have a better understanding on the bio-inspired based self-organization techniques for Genetic algorithm so that they can formulate new algorithms based on existing SO techniques.  

Study on erythrocytes by SEM photogrammetry (SEMP) technique

1990 ◽  
Vol 48 (3) ◽  
pp. 724-725
Author(s):  
Tong Wensheng ◽  
Lu Lianhuang ◽  
Zhang Zhijun
Keyword(s):  
Quantitative Analysis ◽  
Cell Membrane ◽  
Clinical Diagnosis ◽  
Human Body ◽  
Blood Cells ◽  
Three Dimensional ◽  
Normal Person ◽  
Blood Disease ◽  
Computation Technique ◽  
Important Basis

This is a combined study of two diffirent branches, photogrammetry and morphology of blood cells. The three dimensional quantitative analysis of erythrocytes using SEMP technique, electron computation technique and photogrammetry theory has made it possible to push the study of mophology of blood cells from LM, TEM, SEM to a higher stage, that of SEM P. A new path has been broken for deeply study of morphology of blood cells.In medical view, the abnormality of the quality and quantity of erythrocytes is one of the important changes of blood disease. It shows the abnormal blood—making function of the human body. Therefore, the study of the change of shape on erythrocytes is the indispensable and important basis of reference in the clinical diagnosis and research of blood disease.The erythrocytes of one normal person, three PNH Patients and one AA patient were used in this experiment. This research determines the following items: Height;Length of two axes (long and short), ratio; Crevice in depth and width of cell membrane; Circumference of erythrocytes; Isoline map of erythrocytes; Section map of erythrocytes.

Feature Selection Algorithm for High-dimensional Biomedical Data Using Information Gain and Improved Chemical Reaction Optimization

2021 ◽  
Vol 15 (8) ◽  
pp. 912-926
Author(s):  
Ge Zhang ◽  
Pan Yu ◽  
Jianlin Wang ◽  
Chaokun Yan
Keyword(s):  
Feature Selection ◽  
Chemical Reaction ◽  
Information Gain ◽  
Feature Selection Method ◽  
Search Space ◽  
Neighborhood Search ◽  
Biomedical Data ◽  
Chemical Reaction Optimization ◽  
Search Mechanism ◽  
Reaction Optimization

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

2016 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Ravichander Janapati ◽  
Ch. Balaswamy ◽  
K. Soundararajan
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Research Area ◽  
Population Based ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Algorithm ◽  
Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme  can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper  Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.  

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

scholarly journals Very Fast and Accurate Procedure for the Characterization of Photovoltaic Panels from Datasheet Information

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Antonino Laudani ◽  
Francesco Riganti Fulginei ◽  
Alessandro Salvini ◽  
Gabriele Maria Lozito ◽  
Salvatore Coco
Keyword(s):  
Numerical Methods ◽  
Search Space ◽  
Extraction Process ◽  
Reduced Form ◽  
Photovoltaic Panels ◽  
Approximation Techniques ◽  
Computational Costs ◽  
Minimization Technique ◽  
Accurate Procedure

In recent years several numerical methods have been proposed to identify the five-parameter model of photovoltaic panels from manufacturer datasheets also by introducing simplification or approximation techniques. In this paper we present a fast and accurate procedure for obtaining the parameters of the five-parameter model by starting from its reduced form. The procedure allows characterizing, in few seconds, thousands of photovoltaic panels present on the standard databases. It introduces and takes advantage of further important mathematical considerations without any model simplifications or data approximations. In particular the five parameters are divided in two groups, independent and dependent parameters, in order to reduce the dimensions of the search space. The partitioning of the parameters provides a strong advantage in terms of convergence, computational costs, and execution time of the present approach. Validations on thousands of photovoltaic panels are presented that show how it is possible to make easy and efficient the extraction process of the five parameters, without taking care of choosing a specific solver algorithm but simply by using any deterministic optimization/minimization technique.

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