scholarly journals REDUCING THE ORDER OF INTERVAL SYSTEM BY FIREFLY OPTIMIZATION TECHNIQUE

2020 ◽  
Vol spl5 (1) ◽  
Author(s):  
V Pardha Saradhi
Keyword(s):  
Optimization Technique ◽  
Interval System ◽  
Firefly Optimization

Array Design for the Synthesis of Stair Step Patterns Using Firefly Optimization Technique

2019 ◽  
Vol 9 (4) ◽  
pp. 285
Author(s):  
Krishna Rudraraju Chaitanya ◽  
P. Mallikarjuna Rao ◽  
K. V. S. N. Raju ◽  
G. S. N. Raju
Keyword(s):  
Optimization Technique ◽  
Array Design ◽  
Firefly Optimization

scholarly journals Implementation of Hybrid ACO-PSO-GA-DE Algorithm for Mammogram Classification

2019 ◽  
Vol 8 (2) ◽  
pp. 3944-3948
Keyword(s):  
Breast Cancer ◽  
Optimization Technique ◽  
Texture Features ◽  
Svm Classifier ◽  
De Algorithm ◽  
Specificity And Sensitivity ◽  
Firefly Optimization ◽  
Mammogram Image ◽  
Mammogram Images ◽  
State Of Art

Breast Cancer is one of the fastest growing cancer that causes women to death in the world. The early detection of breast cancer improves the chances of its cure. The malignant tumor that is the sign of breast cancer can be detected by mammography. This paper develops a technique to classify the mammogram images as normal, benign or malignant. This paper applies HAPGD (Hybrid ACO (Ant Colony Optimization), PSO (Particle Swarm Optimization), GA (Genetic Algorithm), and DE (Differential Evolution)) classification algorithm to texture features extracted from the mammogram image. The analysis has been done on the DDSM and MIAS dataset by using classification accuracy, specificity, and sensitivity as the parameter with three state of art algorithms i.e. SVM classifier (without any optimization technique), Firefly (SVM with Firefly optimization), ACO-PSO-GA (SVM with hybrid ACO-PSO-GA optimization). The improvement in the performance measures against three state of art techniques shows the significance of the algorithm.

scholarly journals Firefly optimization technique based test scenario generation and prioritization

2018 ◽  
Vol 16 (6) ◽  
Author(s):  
Vikas Panthi ◽  
Durga Prasad Mohapatra
Keyword(s):  
Optimization Technique ◽  
State Machine ◽  
Scenario Generation ◽  
Test Scenario ◽  
Average Percentage ◽  
Model Based Testing ◽  
Composite Models ◽  
Firefly Optimization ◽  
Test Scenarios ◽  
Firefly Optimization Algorithm

Model-based testing shows a significant role-play in the area of software testing. This paper presents a new automatic test scenarios generation technique using UML state machine diagram having composite states. The intention of this research is to generate test scenarios for concurrent and composite states in state machines using the proposed algorithm SMToTSG (State Machine To Test Scenarios Generation). We have prioritized the test scenarios using Firefly optimization algorithm. We have used state-based coverage criteria such as state, transition, transition pair coverage to evaluate the efficiency of the proposed algorithm. The proposed approach is useful for feasible test scenario generation. Generating exhaustive test scenarios for all concurrent interdependent sequences is very difficult. In this paper, we generate the important test scenarios in the presence of concurrency in composite models. After prioritization, we apply Average Percentage Fault Detection (APFD) metric to calculate the efficiency of the prioritized test scenarios.  

scholarly journals Application of Firefly Optimization Technique for Solving Convex Economic Load Dispatch of Generation On Nigerian 330 kV, 24-Bus Grid System

2018 ◽  
Vol 3 (5) ◽  
pp. 77 ◽  
Author(s):  
Ganiyu Adedayo Ajenikoko ◽  
O. E. Olabode ◽  
A. E. Lawal
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Cost Model ◽  
Optimization Technique ◽  
Inequality Constraints ◽  
Population Based ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Firefly Optimization ◽  
Bus System

Firefly optimization is a population based technique in which the attractiveness of a firefly is determined by its attractiveness which is then encoded as the objective function of the optimization problems. Firefly algorithm is one of the newest meta-heuristic algorithms based on the mating or flashing behavior of fireflies. Economic load dispatch of generation allocates power generation to match load demand at minimal possible cost without violating power units and system constraints. This paper presents application of firefly optimization technique (FFOT) for solving convex economic load dispatch of generation. The economic load dispatch problem was formulated to minimize the total fuel cost for the heat optimal combination of thermal generators without violating any of the system constraints using quadratic fuel cost model of Sapele, Delta, Afam and Egbin power stations as case studies. The equality and inequality constraints used on the system were the power balance equation and the transmission line constraints respectively. Firefly optimization technique was then developed using appropriate control parameters for a faster convergence of the technique. The optimization technique was tested and validated on the IEEE 30-bus system and Nigerian 24-bus system. The results obtained from the IEEE 30-bus system were compared to published results obtained via Differential Evolution (DE), Ant Colony Optimization (ACO) and Genetic Algorithm (GA). The comparison confirms the superiority, fast convergence and proficiency of the algorithm.

scholarly journals A New Reversible Database Watermarking Approach with Firefly Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Mustafa Bilgehan Imamoglu ◽  
Mustafa Ulutas ◽  
Guzin Ulutas
Keyword(s):  
Relational Databases ◽  
Data Centers ◽  
Optimization Technique ◽  
Access Network ◽  
Public Access ◽  
Difference Expansion ◽  
Digital Rights ◽  
Firefly Optimization ◽  
Embed Watermark ◽  
Firefly Optimization Algorithm

Up-to-date information is crucial in many fields such as medicine, science, and stock market, where data should be distributed to clients from a centralized database. Shared databases are usually stored in data centers where they are distributed over insecure public access network, the Internet. Sharing may result in a number of problems such as unauthorized copies, alteration of data, and distribution to unauthorized people for reuse. Researchers proposed using watermarking to prevent problems and claim digital rights. Many methods are proposed recently to watermark databases to protect digital rights of owners. Particularly, optimization based watermarking techniques draw attention, which results in lower distortion and improved watermark capacity. Difference expansion watermarking (DEW) with Firefly Algorithm (FFA), a bioinspired optimization technique, is proposed to embed watermark into relational databases in this work. Best attribute values to yield lower distortion and increased watermark capacity are selected efficiently by the FFA. Experimental results indicate that FFA has reduced complexity and results in less distortion and improved watermark capacity compared to similar works reported in the literature.

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