A New Selection-Integrated Optimization (SIO) Methodology for Adaptive Systems Optimization

Abstract

Adaptive systems belong to a class that offers the potential to achieve high performance under changing operating conditions or design requirements. These systems achieve superior performance through their ability to change design configuration during their operation. This change is facilitated by the so-called adaptive design variables – present in adaptive systems. Unfortunately, not all design variables can be made adaptive because of practical limitations, and hence a selection usually needs to be made regarding (i) adaptive, and (ii) non-adaptive (or fixed) design variables. The selection of design variables becomes critical when the adaptive system is designed with the aid of computational optimization. Under existing methods, the selection of these variables and the optimization of adaptive system are performed sequentially, thus yielding the likelihood of a sub-optimal design. In this paper, we propose a new Selection-Integrated Optimization (SIO) methodology that 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 the main source of sub-optimality. In this method, we propose a special Variable-Segregating Mapping-Function (VSMF) that serves to integrate the two key processes. The effectiveness of the SIO methodology is illustrated through the design of a four-bar-truss operating under changing loading conditions.