scholarly journals Improved Ant Algorithms for Software Testing Cases Generation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shunkun Yang ◽  
Tianlong Man ◽  
Jiaqi Xu
Keyword(s):  
Ant Colony Optimization ◽  
Software Testing ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Search Model ◽  
Search Efficiency ◽  
Improved Method ◽  
Random Algorithm ◽  
Positive Feedback Mechanism ◽  
Update Strategy

Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations.

scholarly journals The Improved Ant Colony Optimization Algorithm for MLP considering the Advantage from Relationship

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yabo Luo ◽  
Yongo P. Waden
Keyword(s):  
Ant Colony Optimization ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Rectangular Coordinate System ◽  
Design Variables ◽  
Pheromone Concentration ◽  
Machine Layout ◽  
Polar Coordinate ◽  
Positive Feedback Mechanism

An improved ant colony optimization (ACO) is presented to solve the machine layout problem (MLP), and the concept is categorized as follows: firstly, an ideology on “advantage from quantity” and “advantage from relationship” is proposed and an example is demonstrated. In addition, the strategy of attached variables under local polar coordinate systems is employed to maintain search efficiency, that is, “advantage from relationship”; thus, a mathematical model is formulated under a single rectangular coordinate system in which the relative distance and azimuth between machines are taken as attached design variables. Further, the aforementioned strategies are adopted into the ant colony optimization (ACO) algorithm, thereby employing the inverse feedback mechanism for dissemination of pheromone and the positive feedback mechanism for pheromone concentration. Finally, the effectiveness of the proposed improved ACO is tested through comparative experiments, in which the results have shown both the reliability of convergence and the improvement in optimization degree of solutions.

TUNINGS OF PARAMETERS AND PHEROMONE UPDATE STRATEGY IN ANT COLONY OPTIMIZATION

2010 ◽  
Vol 09 (01) ◽  
pp. 73-83
Author(s):  
A. TAMILARASI
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Parameter Tuning ◽  
Ant Colony ◽  
Shop Floor ◽  
Benchmark Problems ◽  
Ant Colony System ◽  
Update Strategy

Scheduling is considered to be a major task to improve the shop-floor productivity. The job shop problem is under this category and is combinatorial in nature. Research on optimization of job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the Ant Colony Optimization meta heuristic to job shop problem. The main characteristics of this model are positive feedback and distributed computation. The settings of parameter values have more influence in solving instances of job shop problem. An algorithm is introduced to improve the basic ant colony system by using a pheromone updating strategy and also to analyze the quality of the solution for different values of the parameters. In this paper, we present statistical analysis for parameter tuning and we compare the quality of obtained solutions by the proposed method with the competing algorithms given in the literature for well known benchmark problems in job shop scheduling.

Study of Sintering Blending Based on Swarm Intelligence Optimization Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1550-1553 ◽  
Author(s):  
Hui Zhao ◽  
Ming Wang ◽  
Hong Jun Wang ◽  
You Jun Yue
Keyword(s):  
Exact Solution ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Positive Feedback Mechanism ◽  
Swarm Intelligence Optimization

The sintering blending is a complex nonlinear optimization problem. The traditional single algorithm can not meet the requirement of good quality of sinter and lowest costs well. So, a hybrid optimization method of particle swarm and ant colony algorithm was proposed. The method gives full play to the global convergence of particle swarm optimization algorithm, takes it as a preliminary search, then use the positive feedback mechanism of ant colony algorithm for the exact solution, to make these two algorithms to reach a complementary, in order to get a rapid exact solution. The simulation results show that the proposed hybrid algorithm has fast convergence and high accuracy, which can effectively reduce the sintering cost.

Automatic Test Sequence Generation for State Transition Testing via Ant Colony Optimization

2010 ◽  
pp. 161-183 ◽  
Author(s):  
Praveen Ranjan Srivastava ◽  
Baby
Keyword(s):  
Ant Colony Optimization ◽  
Software Testing ◽  
State Transition ◽  
Optimization Technique ◽  
Ant Colony ◽  
State Transitions ◽  
Test Sequence ◽  
Software Systems ◽  
Web Based ◽  
Test Suit

Software testing is a key part of software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible, that’s why there is need to automate the testing process. Generation of the automated and effective test suit is a very difficult task in the software testing process. Effective test suite can decrease the overall cost of testing as well as increase the probability of finding defects in software systems. Testing effectiveness can be achieved by the State Transition Testing which is commonly used in, real time, embedded and web-based kind of software system. State transition testing focuses upon the testing of transitions from one state of an object to other states. The tester’s main job is to test all the possible transitions in the system. This chapter proposed an Ant Colony Optimization technique for automated and fully coverage state-transitions in the system. Through proposed algorithm all the transitions are easily traversed at least once in the test-sequence.

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