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Application of Activity Theory and Particle Swarm Optimization Technique in Cooperative Software Development
Abstract
The paper highlights the issue of cooperative Software Development using the concept of Activity Theory and Particle Swarm Optimization (PSo). The concept of Activity Theory refines the entire process of software development and the selection of Personality Traits of Myer Brigg Personality Traits (MBTI) along with MPSO provides a strong set of knowledgebase creation for varied type of software project.
... Keddara [9] has extended the above work by focusing on the modalities of changes which includes the duration of change and its time frame. Sasthyive have used the concept of Activity Theory [14,18] dealing with Engestrom Triangle. ...
The paper highlights correlation between Adaptive Business Environment and Web Semantic, Petridynamics, Adaptive Semantic Web. The Business environment use of Activity Theory and Web Semantic help in formatting the Ontology.
The paper discusses the application of Evolutionary Algorithm in determining the optimum solution of trade-off between Complexity of Software and its deliverables. It highlights the application of NSGA-II in determining the trade-off value. A set of software has been developed for banking management system with multiple values of complexities. The complexity of the software has been approximated by using proposed approach.
FMS ensures quality product at lowest cost. Layout arrangement of flexible manufacturing system is important to achieve high productivity. In this paper the design of loop layout in Flexible Manufacturing System is discussed. The objective of the loop layout problem is to determine the order of machines around a loop, and to minimize the automated guided vehicle movement. Particle Swarm Optimization technique is proposed to optimize the flexible manufacturing system layout. This paper also discusses the movement of AGV around the loop layout. AGV movement is considered as bidirectional. The clearance between the machines is also considered in the Flexible Manufacturing System loop layout that produces result which will be optimized.
This research work proposes application and implementation of artificial immune system approach to develop an algorithm for optimizing multi objective problems. The objective of this research work is to study, analyze and enhance the artificial immune system approach for developing an algorithm to solve various real life engineering multi-objective optimization problems.
In this paper, by the application of evolutionary multi-objective optimization (EMO) technique, we will be establishing a trade-off balance between the two conflicting aspects i.e. complexity of a software and the usability (business value) of the software.
The paper highlights the use of two basic components: Cognitive Radio and EMO in controlling Urban Traffic System. The coordination of the activities like: identification of route network, traffic jams and coordination of VMS for route diversion. The learning of central control system is achieved by software defined radios integrated with evolutionary Multiobjective optimization.
The sudden rise in population has brought a very heavy demand of vehicles, hence the need to control them. The paper highlights the issue of Intelligent Traffic monitoring system using the technologies like IoT, MultiAgent system and Semantic Web. The paper links IoT sensors using Zigbee protocol and traffic movements are continously monitored by control centre using Granular classification in Ontology.
The emergence of UML as a standard for modeling systems has encouraged the use of automated software tools that facilitate the development process from analysis through coding. In UML, the static structure of classes in a system is represented by a class diagram while the dynamic behaviour of the classes is represented by a set of sequence or statechart diagrams. To facilitate the software development process, it would be ideal to have tools that automatically design, fusion and generate or help to generate executable code from the models. The goal of this paper is to narrow the gap between UML scenarios and an implemented system. The narrowing of gap is achieved by automatically creating, compositing and generating low-level code from the UML sequence diagrams (use case or service of system).
In order to solve the problems of poor global convergence and falling into local optimum easily in Least Squares Support Vector Machine (LS-SVM) soft sensing, an improved hybrid particle swarm optimization algorithm is proposed. This algorithm combines crossover and mutation operation with speed-displacement formula, and optimizes the regularization parameter and the kernel function parameter in LS-SVM. On the basis of this hybrid particle swarm optimization algorithm, a LS-SVM soft sensing model is established to measure the flowrate of oil-gas-water three-phase flow. The experiments has been performed in three-phase flow simulation test device and the results have been compared with genetic algorithm, particle swarm optimization algorithm, ant colony optimization algorithm and traditional LS-SVM algorithm. The analysis demonstrates the hybrid particle swarm optimization algorithm is superior to the other algorithms, and it is an effective method for LS-SVM soft sensing.
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