scholarly journals Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

2018 ◽  
Vol 7 (4.6) ◽  
pp. 302
Author(s):  
Dr. Anandam Velagandula ◽  
P. Buddha Reddy ◽  
N. Hanuman Reddy ◽  
G. Srikanth Reddy ◽  
Ch Anil
Keyword(s):  
Particle Swarm Optimization ◽  
Service Oriented Architecture ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Harmony Search ◽  
Interaction Strength ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Test Cases ◽  
Swarm Optimization

As of late number of meta based heuristic algorithms are suggested to fill in as the premise of test era technique (where shows the interaction strength) embracing  with Simulated Annealing (SA), Ant Colony Optimization (ACO), Cuckoo Search (CS), Genetic Algorithms (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO). Albeit helpful methodologies are requiring particular area learning so as to permit successful tuning before great quality arrangements can be gotten. The multi-target molecule swarm optimization, and multithreading is utilized to overwhelm the compound judgement criteria for an ideal arrangement. The procedure and its related algorithms are assessed broadly utilizing diverse benchmarks and examinations. In our proposed technique test cases are advanced by utilizing Particle Swarm Optimization algorithm (PSO). At that point the streamlined test cases are organized by utilizing to enhanced Cuckoo Search algorithm (ECSA). As the quantity of inserted systems increments quickly, there has been developing interest for the utilization of Service Oriented Architecture (SOA) for some requests. At last, the enhanced outcome will be assessed by programming quality measures.

Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

2018 ◽  
Vol 7 (4.6) ◽  
pp. 302
Author(s):  
Dr. Anandam Velagandula ◽  
P. Buddha Reddy ◽  
N. Hanuman Reddy ◽  
G. Srikanth Reddy ◽  
Ch Anil
Keyword(s):  
Particle Swarm Optimization ◽  
Service Oriented Architecture ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Harmony Search ◽  
Interaction Strength ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Test Cases ◽  
Swarm Optimization

As of late number of meta based heuristic algorithms are suggested to fill in as the premise of test era technique (where shows the interaction strength) embracing  with Simulated Annealing (SA), Ant Colony Optimization (ACO), Cuckoo Search (CS), Genetic Algorithms (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO). Albeit helpful methodologies are requiring particular area learning so as to permit successful tuning before great quality arrangements can be gotten. The multi-target molecule swarm optimization, and multithreading is utilized to overwhelm the compound judgement criteria for an ideal arrangement. The procedure and its related algorithms are assessed broadly utilizing diverse benchmarks and examinations. In our proposed technique test cases are advanced by utilizing Particle Swarm Optimization algorithm (PSO). At that point the streamlined test cases are organized by utilizing to enhanced Cuckoo Search algorithm (ECSA). As the quantity of inserted systems increments quickly, there has been developing interest for the utilization of Service Oriented Architecture (SOA) for some requests. At last, the enhanced outcome will be assessed by programming quality measures.  

Two Meta-Heuristics for Solving Unconstrained Optimization Problems and Machinery Problems

2014 ◽  
Vol 1044-1045 ◽  
pp. 1418-1423
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Particle Swarm ◽  
Finite Sequence ◽  
Swarm Optimization ◽  
Machining Optimization

Machining optimization problem aims to optimize machinery conditions which are important for economic settings. The effective methods for solving these problems using a finite sequence of instructions can be categorized into two groups; exact optimization algorithm and meta-heuristic algorithms. A well-known meta-heuristic approach called Harmony Search Algorithm was used to compare with Particle Swarm Optimization. We implemented and analysed algorithms using unconstrained problems under different conditions included single, multi-peak, curved ridge optimization, and machinery optimization problem. The computational outputs demonstrated the proposed Particle Swarm Optimization resulted in the better outcomes in term of mean and variance of process yields.

Test Case Optimization and Prioritization Using Improved Cuckoo Search and Particle Swarm Optimization Algorithm

2018 ◽  
Vol 7 (4.6) ◽  
pp. 275
Author(s):  
Chandrasekhara Reddy T ◽  
Srivani V ◽  
A. Mallikarjuna Reddy ◽  
G. Vishnu Murthy
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Test Suite ◽  
Swarm Optimization ◽  
Cuckoo Algorithm

For minimized t-way test suite generation (t indicates more strength of interaction) recently many meta-heuristic, hybrid and hyper-heuristic algorithms are proposed which includes Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Genetic Algorithms (GA), Simulated Annealing (SA), Cuckoo Search (CS), Harmony Elements Algorithm (HE), Exponential Monte Carlo with counter (EMCQ), Particle Swarm Optimization (PSO), and Choice Function (CF). Although useful strategies are required specific domain knowledge to allow effective tuning before good quality solutions can be obtained. In our proposed technique test cases are optimized by utilizing Improved Cuckoo Algorithm (ICSA). At that point, the advanced experiments are organized or prioritized by utilizing Particle Swarm Optimization algorithm (PSO). The Particle Swarm Optimization and Improved Cuckoo Algorithm (PSOICSA) estimation is a blend of Improved Cuckoo Search Algorithm(ICSA) and Particle Swarm Optimization (PSO). PSOICSA could be utilized to advance the test suite, and coordinate both ICSA and PSO for a superior outcome, when contrasted with their individual execution as far as experiment improvement. 

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

2015 ◽  
Vol 29 (1) ◽  
pp. 1-18
Author(s):  
Asgarali Bouyer ◽  
Nacer Farajzadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Fast Convergence ◽  
Data Sets ◽  
Swarm Optimization ◽  
Local Optima ◽  
Harmonic Means

Abstract Among the data clustering algorithms, the k-means (KM) algorithm is one of the most popular clustering techniques because of its simplicity and efficiency. However, KM is sensitive to initial centers and it has a local optima problem. The k-harmonic means (KHM) clustering algorithm solves the initialization problem of the KM algorithm, but it also has a local optima problem. In this paper, we develop a new algorithm for solving this problem based on a modified version of particle swarm optimization (MPSO) algorithm and KHM clustering. In the proposed algorithm, MPSO is equipped with the cuckoo search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence, and escape from local optima. MPSO updates the positions of particles based on a combination of global worst, global best with personal worst, and personal best to dynamically be used in each iteration of the MPSO. The experimental result on eight real-world data sets and two artificial data sets confirms that this modified version is superior to KHM and the regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy, and correctness while markedly improving the processing time.

scholarly journals The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 862 ◽  
Author(s):  
José García ◽  
José V. Martí ◽  
Víctor Yepes
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Particle Swarm ◽  
Retaining Walls ◽  
Harmony Search Algorithm ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Reinforced Earth

The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO 2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10 20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.

scholarly journals An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

2016 ◽  
Vol 41 (2) ◽  
pp. 99-121 ◽  
Author(s):  
Asgarali Bouyer
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Fast Convergence ◽  
Experimental Result ◽  
Swarm Optimization ◽  
Local Optima ◽  
Harmonic Means

AbstractAmong the data clustering algorithms, k-means (KM) algorithm is one of the most popular clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to initial centers and it has the local optima problem. K-harmonic-means (KHM) clustering algorithm solves the initialization problem of k-means algorithm, but it also has local optima problem. In this paper, we develop a new algorithm for solving this problem based on an improved version of particle swarm optimization (IPSO) algorithm and KHM clustering. In the proposed algorithm, IPSO is equipped with Cuckoo Search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence and escape from local optima. IPSO updates positions of particles based on a combination of global worst, global best with personal worst and personal best to dynamically be used in each iteration of the IPSO. The experimental result on five real-world datasets and two artificial datasets confirms that this improved version is superior to k-harmonic means and regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy and correctness while markedly improving the processing time.

Sign in / Sign up

Export Citation Format

Share Document