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.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

scholarly journals Modified SCA algorithm for SSSC damping Controller design in Power System

2017 ◽  
Vol 16 (1) ◽  
pp. 46-63 ◽  
Author(s):  
Bidyadhar Rout ◽  
B.B. Pati ◽  
S. Panda
Keyword(s):  
Power System ◽  
Transient Stability ◽  
Controller Design ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Operating Conditions ◽  
Bench Mark ◽  
Single Machine Infinite Bus ◽  
System Stabilizer

This paper studies the improvement of transient stability of a single-Machine Infinite-Bus (SMIB) power system using Proportional Derivative (PD) type Static Synchronous Series Compensator (SSSC) and damping controllers. The design problem has been considered as optimisation problem and a modified version of recently proposed Sine Cosine Algorithm (SCA) has been employed for determining the optimal controller parameters. Proposed modified SCA (mSCA) algorithm is first tested using bench mark test functions and compared with SCA, and other heuristic evolutionary optimization algorithms like Grey Wolf optimization (GWO), Particle Swarm optimization (PSO), Gravitational Search algorithm (GSA) and Differential Evolution algorithm to show its superiority. The proposed mSCA algorithm is then applied to optimize simultaneously the PD type lead lag controller parameters pertaining to SSSC and power system stabilizer(PSS). The proposed controller provides sufficient damping for power system oscillation in different operating conditions and disturbances. Results analysis reveal that proposed mSCA technique provides higher effectiveness and robustness in damping oscillations of the power system and increases the dynamic stability more.

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.

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