Selection-Integrated Optimization (SIO) Methodology for Optimal Design of Adaptive Systems

Many engineering systems are required to operate under changing operating conditions. A special class of systems called adaptive systems has been proposed in the literature to achieve high performance under changing environments. Adaptive systems acquire this powerful feature by allowing their design configurations to change with operating conditions. In the optimization of the adaptive systems, designers are often required to select (i) adaptive and (ii) nonadaptive (or fixed) design variables of the design configuration. Generally, the selection of these variables and the optimization of adaptive systems are performed sequentially, thus being a source of suboptimality. In this paper, we propose the Selection-Integrated Optimization (SIO) methodology, which integrates the two key processes: (1) the selection of the adaptive and fixed design variables and (2) the optimization of the adaptive system, thereby eliminating a significant source of suboptimality from adaptive system optimization problems. A major challenge to integrating these two key processes is the selection ofappropriate fixed and adaptive design variables, which is discrete in nature. We propose the Variable-Segregating Mapping-Function (VSMF), which overcomes this challenge by progressively approximating the discreteness in the design variable selection process. This simple yet effective approach allows the SIO methodology to integrate the selection and optimization processes and helps avoid one significant source of suboptimality from the optimization procedure. The SIO methodology finds its applications in a variety of other engineering fields, such as product family optimization. However, in this paper, we limit the scope of our discussion to adaptive system optimization. The effectiveness of the SIO methodology is demonstrated by designing a new air-conditioning system called Active Building Envelope (ABE) system.