We present CASE (complex adaptive systems evolver), a framework devised to conduct the design of agent-based simulation experiments using evolutionary computation techniques. This framework enables one to optimize complex agent-based systems, to exhibit pre-specified behavior of interest, through the use of multi-objective evolutionary algorithms and cloud computing facilities.
We examine cloud computing, using the MapReduce framework, to assist the evolutionary design of experiments method (EvoDOE). Cloud computing has recently attracted considerable attention due to the massive and scalable computational resources it can deliver. These features may potentially benefit EvoDOE, a highly computationally intensive methodology in which many computer simulations are generated and evaluated using evolutionary computation techniques. To assist this research, we implement a selection of distributed evolutionary computation techniques using the MapReduce framework. The aim of this paper is to identify the evolutionary computing model which may most efficiently exploit cloud computing for EvoDOE. Multiple series of experiments are conducted using a case study from the military domain. Specifically, red teaming experiments using an agent-based simulation of a maritime anchorage protection scenario are performed.
Evolving agent-based simulations enables one to automate the difficult iterative process of modeling complex adaptive systems to exhibit pre-specified/desired behaviors. Nevertheless this emerging technology, combining research advances in agent-based modeling/simulation and evolutionary computation, requires significant computing resources (i.e., high performance computing facilities) to evaluate simulation models across a large search space. Moreover, such experiments are typically conducted in an infrequent fashion and may occur when the computing facilities are not fully available. The user may thus be confronted with a computing budget limiting the use of these "evolvable simulation" techniques. We propose the use of the cloud computing paradigm to address these budget and flexibility issues. To assist this research, we utilize a modular evolutionary framework coined CASE (for complex adaptive system evolver) which is capable of evolving agent-based models using nature-inspired search algorithms. In this paper, we present an adaptation of this framework which supports the cloud computing paradigm. An example evolutionary experiment, which examines a simplified military scenario modeled with the agent-based simulation platform MANA, is presented. This experiment refers to Automated Red Teaming: a vulnerability assessment tool employed by defense analysts to study combat operations (which are regarded here as complex adaptive systems). The experimental results suggest promising research potential in exploiting the cloud computing paradigm to support computing intensive evolvable simulation experiments. Finally, we discuss an additional extension to our cloud computing compliant CASE in which we propose to incorporate a distributed evolutionary approach, e.g., the island-based model to further optimize the evolutionary search.
We present CASE (complex adaptive systems evolver), a framework devised to conduct the design of agent-based simulation experiments using evolutionary computation techniques. This framework enables one to optimize complex agent-based systems, to exhibit pre-specified behavior of interest, through the use of multi-objective evolutionary algorithms and cloud computing facilities.
since large engineering equipment cannot meet customers' personalized demands or respond fast to market changes, the method to conduct innovative design of engineering equipment based on modular strategy is proposed here.With the innovative design of excavator for example and by combining the product characteristics of excavator, the modular design study was carried out and the modular design process worked out to utilize modular design method for excavator.By combining different function modules, effects with various functions can be achieved, providing an entry point for the sustainable development design of engineering machinery.
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