scholarly journals Parameters Tuning of Adaptive Firefly Algorithm based Strategy for t-way Testing

2019 ◽  
Vol 9 (1) ◽  
pp. 4185-4191
Keyword(s):  
Evolutionary Algorithms ◽  
Execution Time ◽  
Firefly Algorithm ◽  
Search Algorithm ◽  
Covering Array ◽  
Tuning Parameters ◽  
Parameters Tuning ◽  
Parent Selection ◽  
Minimum Requirements ◽  
Small Set

The adaptive firefly algorithm (AFA) is developed based on an elitism operator. Elitism operators can perform the function of updating the effectiveness of diversification in a search algorithm. In this study, a strategy was proposed to upgrade the FA concerning static issues. Most traditionally, for evolutionary algorithms, elitism suggests that the best solution found is utilized to work for the next generation. Elitism involves the replication of a small set of the fittest candidate solutions, which remain unaltered, into succeeding generations. The condition can radically impact execution time by ensuring that the El waste no time on re-finding newly-disposed partial solutions. Candidates who stay protected and unmodified via elitism all meet the requirements for parent selection in terms of rearing the remainder of the succeeding generation. This study used different tuning parameters, such as the number of fireflies, iterations and switching probability. To ensure that AFA could perform for t-way testing as useful as other strategies to generate the best performance. Considering the standard covering array (N, 2,𝟓 𝟕 ) it demonstrates the tuning parameters for AFA to improve elitism. In this paper, the Findings show that AFA, as well as t-way testing, can deliver the minimum requirements and sufficient results

Evolutionary Algorithms

2016 ◽  
pp. 190-215 ◽  
Author(s):  
Ka-Chun Wong
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Real World ◽  
Fitness Function ◽  
Search Performance ◽  
Parallel Search ◽  
Survival Selection ◽  
Parent Selection ◽  
History Of ◽  
Search Capability

Inspired from nature, evolutionary algorithms have been proven effective and unique in different real world applications. Comparing to traditional algorithms, its parallel search capability and stochastic nature enable it to excel in search performance in a unique way. In this chapter, evolutionary algorithms are reviewed and discussed from concepts and designs to applications in bioinformatics. The history of evolutionary algorithms is first discussed at the beginning. An overview on the state-of-the-art evolutionary algorithm concepts is then provided. Following that, the related design and implementation details are discussed on different aspects: representation, parent selection, reproductive operators, survival selection, and fitness function. At the end of this chapter, real world evolutionary algorithm applications in bioinformatics are reviewed and discussed.

REVIEW ON DECOMPOSED FUZZY PID STRUCTURE FOR POWER INVERTERS REGULATION

2016 ◽  
Vol 78 (7) ◽  
Author(s):  
M. S. Arif ◽  
S. M. Ayob ◽  
Z. Salam
Keyword(s):  
Fuzzy Controller ◽  
Design Flow ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Distance Method ◽  
Control Laws ◽  
Control Structures ◽  
Tuning Parameters ◽  
Fuzzy Pid Controller ◽  
Parameters Tuning

The aim of this paper is to critically review prominent decomposed Fuzzy PID control structures. Structural construction and output control laws of these controllers will be discussed.  Their merits and drawbacks are highlighted.  Based on the critical discussions, a new structure of Fuzzy PID controller is proposed.  It is based on cascaded structure, which yields simpler design flow and parameters tuning.  Other advantages of the proposed Fuzzy PID structure are the reduction of tuning parameters and rules of the Fuzzy controller. In addition, the proposed structure allows the usage of signed distance method. The application of the method reduces the computation burden significantly as the power inverter regulation needs very fast and precise computations.

Modified Firefly Algorithm With Chaos Theory for Feature Selection

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Sujata Dash ◽  
Ruppa Thulasiram ◽  
Parimala Thulasiraman
Keyword(s):  
Feature Selection ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
High Dimensional ◽  
Support Vector ◽  
Combinatorial Optimization Problems ◽  
Meta Search ◽  
Combined Algorithm

Conventional algorithms such as gradient-based optimization methods usually struggle to deal with high-dimensional non-linear problems and often land up with local minima. Recently developed nature-inspired optimization algorithms are the best approaches for finding global solutions for combinatorial optimization problems like microarray datasets. In this article, a novel hybrid swarm intelligence-based meta-search algorithm is proposed by combining a heuristic method called conditional mutual information maximization with chaos-based firefly algorithm. The combined algorithm is computed in an iterative manner to boost the sharing of information between fireflies, enhancing the search efficiency of chaos-based firefly algorithm and reduces the computational complexities of feature selection. The meta-search model is implemented using a well-established classifier, such as support vector machine as the modeler in a wrapper approach. The chaos-based firefly algorithm increases the global search mobility of fireflies. The efficiency of the model is studied over high-dimensional disease datasets and compared with standard firefly algorithm, particle swarm optimization, and genetic algorithm in the same experimental environment to establish its superiority of feature selection over selected counterparts.

scholarly journals Harmony Search-Based Approach for Multi-Objective Software Architecture Reconstruction

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1906
Author(s):  
Amarjeet Prajapati ◽  
Zong Woo Geem
Keyword(s):  
Evolutionary Algorithms ◽  
Software Architecture ◽  
Architectural Design ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Performance Criteria ◽  
Software System ◽  
Multi Objective

The success of any software system highly depends on the quality of architectural design. It has been observed that over time, the quality of software architectural design gets degraded. The software system with poor architecture design is difficult to understand and maintain. To improve the architecture of a software system, multiple design goals or objectives (often conflicting) need to be optimized simultaneously. To address such types of multi-objective optimization problems a variety of metaheuristic-oriented computational intelligence algorithms have been proposed. In existing approaches, harmony search (HS) algorithm has been demonstrated as an effective approach for numerous types of complex optimization problems. Despite the successful application of the HS algorithm on different non-software engineering optimization problems, it gained little attention in the direction of architecture reconstruction problem. In this study, we customize the original HS algorithm and propose a multi-objective harmony search algorithm for software architecture reconstruction (MoHS-SAR). To demonstrate the effectiveness of the MoHS-SAR, it has been tested on seven object-oriented software projects and compared with the existing related multi-objective evolutionary algorithms in terms of different software architecture quality metrics and metaheuristic performance criteria. The experimental results show that the MoHS-SAR performs better compared to the other related multi-objective evolutionary algorithms.

A Hybrid Technique for Selection and Minimization of Test Cases in Regression Testing

2019 ◽  
Vol 12 ◽  
Author(s):  
Leena Singh ◽  
Shailendra Narayan Singh ◽  
Sudhir Dawra
Keyword(s):  
Execution Time ◽  
Firefly Algorithm ◽  
Hybrid Approach ◽  
Regression Testing ◽  
Test Suite ◽  
Test Cases ◽  
Hybrid Technique ◽  
Time Saving ◽  
Superior Result ◽  
Software Practitioners

Background: In today’s era, modifications in a software is a common requirement by customers. When changes are made to existing software, re-testing of all the test cases is required to ensure that the newly introduced changes do not have any unwanted effect on the behavior of the software. However, re-testing of all the test cases would not only be time consuming but also expensive. Therefore, there is a need for a technique that reduces the number of tests to be performed. Regression testing is one of the ways to reduce the number of test cases. Selection technique is one such method which seeks to identify the test cases that are relevant to some set of recent changes. Objective: It is evident that most of the studies have used different selection techniques and have focused only on one parameter for achieving reduced test suite size without compromising the performance of regression testing. However, to the best of our knowledge, no study has taken two or more parameters of coverage, and/or execution time in a single testing. This paper presents a hybrid technique that combines both regression test selection using slicing technique and minimization of test cases using modified firefly algorithm with combination of parameters coverage and execution time in a single testing. Methods: A hybrid technique has been described that combines both selection and minimization. Selection of test cases is based upon slicing technique while minimization is done using firefly algorithm. Hybrid technique selects and minimizes the test suite using information on statement coverage and execution time. Results: The proposed technique gives 43.33% much superior result as compared to the other hybrid approach in terms of significantly reduced number of test cases. It shows that the resultant test cases were effective enough to cover 100% of the statements, for all the programs. The proposed technique was also tested on four different programs namely Quadratic, Triangle, Next day, Commission respectively for test suite selection and minimization which gave comparatively superior result in terms of reduction (%) in number of test cases required for testing. Conclusion: The combination of parameters used in slicing based approach, reduces the number of test cases making software testing an economical, feasible and time saving option without any fault in the source code. This proposed technique can be used by software practitioners/experts to reduce time, efforts and resources for selection and minimization of test cases.

Nelder-Mead Evolutionary Hybrid Algorithms

2011 ◽  
pp. 1191-1196 ◽  
Author(s):  
Sanjoy Das
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Simplex Algorithm ◽  
Stochastic Methods ◽  
Great Success ◽  
Local Minima ◽  
Swarm Optimization

Real world optimization problems are often too complex to be solved through analytic means. Evolutionary algorithms are a class of algorithms that borrow paradigms from nature to address them. These are stochastic methods of optimization that maintain a population of individual solutions, which correspond to points in the search space of the problem. These algorithms have been immensely popular as they are derivativefree techniques, are not as prone to getting trapped in local minima, and can be tailored specifically to suit any given problem. The performance of evolutionary algorithms can be improved further by adding a local search component to them. The Nelder-Mead simplex algorithm (Nelder & Mead, 1965) is a simple local search algorithm that has been routinely applied to improve the search process in evolutionary algorithms, and such a strategy has met with great success. In this article, we provide an overview of the various strategies that have been adopted to hybridize two wellknown evolutionary algorithms - genetic algorithms (GA) and particle swarm optimization (PSO).

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