This paper presents a solution for the Quality-based Software-Selection and Hardware-Mapping problem using the ACO algorithm. ACO is one of the most successful swarm intelligence algorithms for solving discrete optimization problems. The evaluation results show that the proposed approach generates correct results for all evaluated test cases.
Also, better results in terms of performance and scalability are given in comparison with the ILP and EMFeR approaches.
Due to the increasing occurrence of unexpected events and the need for pre-crisis planning to reduce risks and losses, modeling emergency response environments (ERE) is needed more than ever. Modeling may lead to more careful planning for crisis-response operations, such as team formation, task assignment, and doing the task by teams. ERE-ML is a model-driven framework which allows a crisis manager to model an ERE, and to automatically generate the executable code of a multi-agent system (MAS) for that environment. However, the application generated by ERE-ML lacks the capability of supporting interactions among the agents and the organizations involved in the crisis management. In this paper, we propose ERE-ML 2.0 as an upgrade of the previous framework. The ERE-ML 2.0 framework supports the interactions by adding new features to the ERE-ML language, modifying the transformation code, and extending the platform. To evaluate the upgraded framework, the Plasco Tower Collapse incident is modeled, and then the model is transformed into the executable code of a MAS to visualize the run-time scenarios.
Our paper entitled “Towards a Model-Driven Framework for Simulating Interactive Emergency Response Environments” was accepted in Journal of Computing and Security.
Congratulations to our team members, Dr. Bahman Zamani, Dr. Kolahdouz Rahimi, Ms. Samaneh HoseinDoost, and Mr. Meysam Karimi for receiving the Most Scalable Solution and Audience awards in Transformation Tool Contest (TTC 2018).
In emergency response environments, variant entities with specific behaviors and interaction between them form a complex system that can be well modeled by multi-agent systems. To build such complex systems, instead of writing the code from scratch, one can follow the model-driven development approach, which aims to generate software from design models automatically. To achieve this goal, two important prerequisites are: a domain-specific modeling language for designing an emergency response environment model, and transformation programs for automatic code generation from a model. In addition, for modeling with the language, a modeling tool is required, and for executing the generated code there is a need to a platform. In this paper, a model-driven framework for developing multi-agent systems in emergency response environments is provided which includes several items. A domain-specific modeling language as well as a modeling tool is developed for this domain. The language and the tool are called ERE-ML and ERE-ML Tool, respectively. Using the ERE-ML Tool, a designer can model an emergency response situation and then validate the model against the predefined constraints. Furthermore, several model to code transformations are defined for automatic multi-agent system code generation from an emergency response environment model. For executing the generated code, an extension of JAMDER platform is also provided. To evaluate our framework, several case studies including the Victorian bushfire disaster are modeled to show the ability of the framework in modeling real-world situations and automatic transformation of the model into the code.
Selecting Samaneh Hoseindoost’s thesis as the honorable thesis in the 17th annual Iranian student thesis festival
Congratulations to Samaneh HoseinDoost for selecting her M.Sc. thesis as the honorable thesis in the computer engineering field in the 17th annual Iranian student thesis festival.