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.

Sizing of current conveyors by means of an Ant Colony Optimization technique

2011 ◽  
Author(s):  
Bachir Benhala ◽  
Ali Ahaitouf ◽  
Abdellah Mechaqrane ◽  
Brahim Benlahbib ◽  
Farid Abdi ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Technique ◽  
Ant Colony ◽  
Current Conveyors

Adapting Ant Colony for Topology Optimization of Compliant Mechanisms With Variable Material Density

2017 ◽  
Author(s):  
Nadim Diab
Keyword(s):  
Topology Optimization ◽  
Ant Colony Optimization ◽  
High Performance ◽  
Compliant Mechanisms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Intensity Level ◽  
Material Density ◽  
Swarm Intelligence Optimization

Swarm intelligence optimization techniques are widely used in topology optimization of compliant mechanisms. The Ant Colony Optimization has been implemented in various forms to account for material density distribution inside a design domain. In this paper, the Ant Colony Optimization technique is applied in a unique manner to make it feasible to optimize for the beam elements’ cross-section and material density simultaneously. The optimum material distribution algorithm is governed by two various techniques. The first technique treats the material density as an independent design variable while the second technique correlates the material density with the pheromone intensity level. Both algorithms are tested for a micro displacement amplifier and the resulting optimized topologies are benchmarked against reported literature. The proposed techniques culminated in high performance and effective designs that surpass those presented in previous work.

ACO powered by Local Searches Algorithms for the Solution of TSP Problems

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Nisreen L. Ahmed
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Technique ◽  
General Purpose ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
New Approach ◽  
Local Searches ◽  
Probabilistic Technique ◽  
The Social ◽  
Great Deluge Algorithm

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.  Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

Dual Transmission Power and Ant Colony Optimization based Lifespan Maximization Protocol for Sensor Networks

2015 ◽  
Vol 11 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Rohini Sharma ◽  
D. K. Lobiyal
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Technique ◽  
Residual Energy ◽  
Base Station ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Transmission Power ◽  
Route Length ◽  
Leach Protocol ◽  
Dual Transmission

A main characteristic of wireless sensor network (WSN) is its limited battery power. Non-uniform energy depletion in WSN, leads to formation of energy holes in certain areas of network. For a uniform consumption of energy among sensor nodes, some points should be considered like the residual energy of the nodes, energy consumed in the communication and route length. In this work, the authors has achieved the uniform consumption of energy by using dissimilar transmission power levels for communication between cluster heads and the sink node, and for intra- cluster communication. Further, they have used ant colony optimization technique for routing between the base station and sensors which are not the member of any cluster. They have proposed dual transmission power levels and ant colony optimization based (DTP-ACO) protocol to improve the lifespan of the network. Results demonstrate that DTP-ACO protocol outperforms LEACH protocol in provisions of the life span, residual energy, packets sent to the base station and throughput of the network.

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