Design of graph-based evolutionary algorithms: A case study for chemical process networks

This paper describes the adaptation of evolutionary algorithms (EAs) to the structural optimization of chemical engineering plants, using rigorous pro cess simulation combined with realistic costing procedures to calculate tar get function values. To represent chemical engineering plants, a network representation with typ ed vertices and variable structure will be introduced. For this representat ion, we introduce a technique on how to create problem specific search oper ators and apply them in stochastic optimization procedures. The applicabili ty of the approach is demonstrated by a reference example. The design of the algorithms will be oriented at the systematic framework o f metric-based evolutionary algorithms (MBEAs). MBEAs are a special class o f evolutionary algorithms, fulfilling certain guidelines for the design of search operators, whose benefits have been proven in theory and practice. M BEAs rely upon a suitable definition of a metric on the search space. The d efinition of a metric for the graph representation will be one of the main issues discussed in this paper. Although this article deals with the problem domain of chemical plant optim ization, the algorithmic design can be easily transferred to similar networ k optimization problems. A useful distance measure for variable dimensional ity search spaces is suggested.