scholarly journals Diabetic Foot Risk Classification using Decision Tree and Bio-Inspired Evolutionary Algorithms

2019 ◽  
Vol 9 (2S) ◽  
pp. 232-239
Keyword(s):  
Evolutionary Algorithms ◽  
Diabetic Foot ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cuckoo Search ◽  
Classification Problem ◽  
Risk Category ◽  
Diabetic Patients ◽  
Intervention Delivery ◽  
Treatment And Prevention

Diabetic foot complications are a burden to the Indian population which affects both financially and physically. The complications could be prevented if the risk of diabetic foot are detected well in advance before the peripheral nerves are damaged leading to amputation and limb loss. The quantification of severity plays an important role in timely intervention, delivery of appropriate treatment and prevention of amputation. This can be modeled as a classification problem where the risk category is stratified into different levels of severity. This paper is an approach to build such a system, capable of classifying the risk category of diabetic patients for suitable follow-up and care. Decision trees are used for the same with features selected using bio-inspired evolutionary algorithms like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search (CS), FireFly (FF), Dragon Fly (DF) and Gravitational Search Algorithm (GSA). The overall accuracy is 77% but it identifies the low risk and high risk cases effectively with 97% and 89% respectively.

Lévy-Enhanced Swarm Intelligence for Optimizing a Multiobjective Biofuel Supply Chain

2020 ◽  
pp. 287-309 ◽  
Author(s):  
T. Ganesan ◽  
Pandian Vasant
Keyword(s):  
Supply Chain ◽  
Swarm Intelligence ◽  
Large Scale ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cuckoo Search ◽  
Biofuel Supply Chain ◽  
Solution Methods ◽  
The Face ◽  
Chain Problem

Engineering systems are currently plagued by various complexities and uncertainties. Metaheuristics have emerged as an essential tool for effective engineering design and operations. Nevertheless, conventional metaheuristics still struggle to reach optimality in the face of highly complex engineering problems. Aiming to further boost the performance of conventional metaheuristics, strategies such as hybridization and various enhancements have been added into the existing solution methods. In this work, swarm intelligence techniques were employed to solve the real-world, large-scale biofuel supply chain problem. Additionally, the supply chain problem considered in this chapter is multiobjective (MO) in nature. Comparative analysis was then performed on the swarm techniques. To further enhance the search capability of the best solution method (GSA), the Lévy flight component from the Cuckoo Search (CS) algorithm was incorporated into the Gravitational Search Algorithm (GSA) technique; developing the novel Lévy-GSA technique. Measurement metrics were then utilized to analyze the results.

The optimal design and analysis of wideband second-order microwave integrator

2019 ◽  
Vol 11 (3) ◽  
pp. 227-236
Author(s):  
Usha Gautam ◽  
Tarun Kumar Rawat
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Wide Band ◽  
Absolute Magnitude ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Performance Measure ◽  
Second Order ◽  
Magnitude Response ◽  
The Ideal

AbstractThe implementation of stable, accurate, and wideband second-order microwave integrators (SOMIs) is presented in this paper. These designs of SOMIs are obtained by using different combinations of transmission line sections and shunt stubs in cascading. Particle swarm optimization (PSO), cuckoo search algorithm (CSA), and gravitational search algorithm (GSA) are applied to obtain the optimal values of the characteristic impedances of these line elements to approximate the magnitude response of ideal second-order integrator (SOI). The performance measure criteria for the proposed SOMIs are carried out based on magnitude response, absolute magnitude error, phase response, convergence rate, pole-zero plot, and improvement graph. The simulation results and statistical analysis demonstrate that GSA surpasses the PSO and CSA to approximate the ideal SOI in all state-of-the-art, that is eligible for wide-band microwave integrator. The designed SOMI is compact in size and suitable to cover microwave applications. The magnitude errors for the proposed SOMIs GSA based are as low as 4.9954 and 3.6573, respectively. The structure of the designed SOMI is implemented in the form of microstrip line on RT/Duroid substrate with dielectric constant 2.2 and having height 0.762 mm. The simulated and measured magnitude result agrees well with the ideal one in the frequency range of 3–15 GHz.

scholarly journals A Water-Area Recognition Approach Based on “Tuned” Texture Mask and Cuckoo Search Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Zhongwei Chen ◽  
Kangbo Peng ◽  
Lai Huang ◽  
Yichao Wang ◽  
Xiaozhong Wu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Texture Feature ◽  
Cuckoo Search ◽  
Water Area ◽  
Experimental Results ◽  
Local Optimum ◽  
Texture Feature Extraction ◽  
Scale Invariant

Texture feature extraction is a key topic in many applications of image analysis; a lot of techniques have been proposed to measure the characteristics of this field. Among them, texture energy extracted with a mask is a rotation and scale invariant texture descriptor. However, the tuning process is computationally intensive and easily trap into the local optimum. In the proposed approach, a “Tuned” mask is utilized to extract water and nonwater texture; the optimal “Tuned” mask is acquired by maximizing the texture energy value via a newly proposed cuckoo search (CS) algorithm. Experimental results on samples and images show that the proposed method is suitable for texture feature extraction, the recognition accuracy is higher than the genetic algorithm (GA), particle swarm optimization (PSO) and the gravitational search algorithm (GSA) optimized “Tuned” mask scheme, and the water area could be well recognized from the original image. Experimental results show that the proposed method could exhibit better performance than other methods involved in the paper in terms of optimization ability and recognition result.

scholarly journals An Efficient Optimization Algorithm for Modular Product Design

2021 ◽  
Vol 54 (2) ◽  
pp. 195-207
Author(s):  
Hayam G. Wahdan ◽  
Hisham M. Abdelslam ◽  
Sally S. Kassem
Keyword(s):  
Optimization Algorithm ◽  
System Analysis ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cuckoo Search ◽  
Optimal Number ◽  
Complex Products ◽  
Modular Product ◽  
Design Structure ◽  
Metaheuristic Optimization Algorithms

Modularity concepts play an important role in the process of developing new complex products. Modularization involves dividing a product into a set of modules - each of which consisting of a set of components - that are interdependent in the same cluster and independent between clusters. During this process, a product can be represented using a Design Structure Matrix (DSM). A DSM acts as a tool for system analysis to provide clear visualization of product elements. In addition, DSM, shows the interactions between these product elements. This paper aims to propose an efficient optimization algorithm that dynamically divides a DSM into an optimal number and size of clusters in a way that minimizes total coordination cost; the interactions inside clusters (modules) and interactions between clusters. Given problem complexity, five metaheuristic optimization algorithms are proposed and tested to solve it; these algorithms are used to determine: (1) the optimal clusters’ number within a DSM, and (2) the optimal components assignment clusters to minimize the total coordination cost. The five used metaheuristics are: Cuckoo Search, Modified Cuckoo Search, Particle Swarm Optimization, Simulated Annealing, and Gravitational Search Algorithm. Eighty problems with different properties are generated and used to examine the proposed algorithms for effectiveness and efficiency. Extensive comparisons are conducted and analyzed. Cuckoo Search is outperforming the other four algorithms.

scholarly journals Analysis of Wideband Second-Order Microwave Integrators

2021 ◽  
Author(s):  
Usha Gautam ◽  
Tarun Kumar Rawat
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Wide Band ◽  
Absolute Magnitude ◽  
Cuckoo Search ◽  
Second Order ◽  
Ultra Wideband ◽  
Magnitude Response ◽  
Improvement Graph ◽  
The Ideal

This chapter presents the implementation of stable, accurate, and wideband second-order microwave integrators (SOMIs). These SOMI designs are obtained by the use of various cascading combinations of transmission line sections and shunt stubs. In order to obtain the optimal values of the characteristic impedances of these line elements, the particle swarm optimization (PSO), cuckoo search algorithm (CSA) and gravitational search algorithm (GSA) are used to approximate the magnitude response of the ideal second-order integrator (SOI). Based on magnitude response, absolute magnitude error, phase response, convergence rate, pole-zero plot, and improvement graph, the performance measure criteria for the proposed SOMIs are performed. The results of the simulation and statistical analysis reveal that GSA exceeds the PSO and CSA in order to approximate the ideal SOI in all state-of-the-art eligible for wide-band microwave integrator. The designed SOMI is compact and suitable for applications covering ultra-wideband (UWB). The designed SOMI structure is also simulated on Advanced Design Software (ADS) in the form of a microstrip line on a dielectric constant 2.2 RT/Duroid substrate with a height of 0.762 mm. In the 3–15 GHz frequency range, the simulated magnitude result agrees well with the ideal one.

scholarly journals Performance Comparison Of Evolutionary Algorithms For Image Clustering

2014 ◽  
Vol XL-7 ◽  
pp. 71-74
Author(s):  
P. Civicioglu ◽  
U. H. Atasever ◽  
C. Ozkan ◽  
E. Besdok ◽  
A. E. Karkinli ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Data Clustering ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Convergence Time ◽  
Image Clustering ◽  
Clustering Techniques ◽  
Sensing Applications ◽  
Clustering Validation

Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Differential Evolution Algorithm (JADE), Differential Search Algorithm (DSA) and Backtracking Search Optimization Algorithm (BSA)) and some classical image clustering techniques (i.e., k-means, fcm, som networks) have been used to cluster images and their performances have been compared by using four clustering validation indexes. Experimental test results exposed that evolutionary algorithms give more reliable cluster-centers than classical clustering techniques, but their convergence time is quite long.

scholarly journals A Comparative Analysis of Evolutionary Algorithms for Synthesis of Scanned Linear Array of Mutually Coupled Parallel Dipole Antennas

2017 ◽  
Vol 63 (2) ◽  
pp. 151-157
Author(s):  
Hemant Patidar ◽  
Gautam Kumar Mahanti ◽  
Ramalingam Muralidharan
Keyword(s):  
Comparative Analysis ◽  
Evolutionary Algorithms ◽  
Linear Array ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Dipole Antennas ◽  
Statistical Parameters ◽  
Fitness Value

Abstract This paper presents a comparative analysis of three evolutionary algorithms, namely, Backtracking Search Algorithm, Cuckoo Search Algorithm and Artificial Bee Colony Algorithms for synthesis of a scanned linear array of uniformly spaced parallel half wavelength dipole antennas. Here, antenna parameters, namely Side Lobe Level, reflection coefficient and wide null depth are taken into consideration for comparison between algorithms. In addition to it, statistical parameters, namely best fitness value, mean and standard deviation of the fitness values obtained from algorithms are compared. Mutual coupling that exists among the antenna elements is included in obtaining radiation patterns and the self-impedances along with the mutual impedances are calculated by induced Electro-Motive Force method. Two different examples are shown in this paper to validate the effectiveness of the utilized approach. Although, this approach is applied to a linear array of dipole antennas; this can be utilized for other array geometries as well.

scholarly journals Mendelian Evolutionary Theory Optimization Algorithm

2020 ◽  
Author(s):  
Neeraj Gupta ◽  
Mahdi Khosravy ◽  
Nilesh Patel ◽  
Nilanjan Dey ◽  
Om Prakash Mahela
Keyword(s):  
Evolutionary Theory ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Invasive Weed ◽  
Covariance Matrix Adaptation ◽  
Successive Generations ◽  
Evolutionary Operators ◽  
Benchmark Test Functions

<div>This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators – 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation – are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal Wallis statistical rank-based non-parametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.</div>

scholarly journals Mendelian Evolutionary Theory Optimization Algorithm

2020 ◽  
Author(s):  
Neeraj Gupta ◽  
Mahdi Khosravy ◽  
Nilesh Patel ◽  
Nilanjan Dey ◽  
Om Prakash Mahela
Keyword(s):  
Evolutionary Theory ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Invasive Weed ◽  
Covariance Matrix Adaptation ◽  
Successive Generations ◽  
Evolutionary Operators ◽  
Benchmark Test Functions

<div>This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators – 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation – are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal Wallis statistical rank-based non-parametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.</div>

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.

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