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

evolutionary COMPUTATION 2001年第3期9卷 329-354页

作者： Emmerich, M Grötzner, M Schütz, M Informat Centrum Dortmund Ctr Appl Syst Anal D-44227 Dortmund Germany Rhein Westfal TH Aachen Rhein Westfal TH Aachen Dept Tech Thermodynam D-52062 Aachen Germany

This paper describes the adaptation of evolutionary algorithms (EAs) to the structural optimization of chemical engineering plants, using rigorous process simulation combined with realistic costing procedures to calculate target function values. To represent chemical engineering plants, a network representation with typed vertices and variable structure will be introduced. For this representation, we introduce a technique on how to create problem specific search operators and apply them in stochastic optimization procedures. The applicability of the approach is demonstrated by a reference example. The design of the algorithms will be oriented at the systematic framework of metric-based evolutionary algorithms (MBEAs). MBEAs are a special class of evolutionary algorithms, fulfilling certain guidelines for the design of search operators, whose benefits have been proven in theory and practice. MBEAs rely upon a suitable definition of a metric on the search space. The definition 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 optimization, the algorithmic design can be easily transferred to similar network optimization problems. A useful distance measure for variable dimensionality search spaces is suggested.

关键词： evolutionary algorithms genetic programming chemical process optimization network representations metric-based evolutionary algorithms minimal moves

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Design of problem-specific evolutionary algorithm/mixed-integer programming hybrids: two-stage stochastic integer programming applied to chemical batch scheduling

Design of problem-specific evolutionary algorithm/mixed-inte...

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7th International Conference on Adaptive Computing in Design and Manufacture (ACDM)

作者： Urselmann, Maren Emmerich, Michael T. M. Till, Jochen Sand, Guido Engell, Sebastian Leiden Univ Leiden Inst Adv Comp Sci NL-2333 CA Leiden Netherlands Univ Dortmund Proc Control Lab BCI AST D-44221 Dortmund Germany ABB Corp Res Ctr D-68526 Ladenburg Germany

Engineering optimization often deals with large, mixed-integer search spaces with a rigid structure due to the presence of a large number of constraints. Metaheuristics, such as evolutionary algorithms (EAs), are frequently suggested as solution algorithms in such cases. In orderto exploit the full potential of these algorithms, it is important to choose an adequate representation of the search space and to integrate expert-knowledge into the stochastic search operators, without adding unnecessary bias to the search. Moreover, hybridisation with mathematical programming techniques such as mixed-integer programming (MIP) based on a problem decomposition can he considered for improving algorithmic performance. In order to design problem-specific EAs it is desirable to have a set of design guidelines that specify properties of search operators and representations. Recently, a set of guidelines has been proposed that gives rise to so-called metric-based EAs (MBEAs). Extended by the minimal moves mutation they allow for a generalization of EA with self-adaptive mutation strength in discrete search spaces. In this article, a problem-specific EA for process engineering task is designed, following the MBEA guidelines and minimal moves mutation. On the background of the application, the usefulness of the design framework is discussed, and further extensions and corrections proposed. As a case-study, a two-stage stochastic programming problem in chemical batch process scheduling is considered. The algorithm design problem can be viewed as the choice of a hierarchical decision structure, where on different layers of the decision process symmetries and similarities can be exploited for the design of minimal moves. After a discussion of the design approach and its instantiation for the case-study, the resulting problem-specific EA/MIP is compared to a straightforward application of a canonical EA/MIP and to a monolithic mathematical programming algorithm. In view of the results

关键词： metric-based evolutionary algorithms batch scheduling hybrid algorithms discrete optimization

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Design of problem-specific evolutionary algorithm/mixed-integer programming hybrids: two-stage stochastic integer programming applied to chemical batch scheduling

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ENGINEERING OPTIMIZATION 2007年第5期39卷 529-549页

关键词： metric-based evolutionary algorithms batch scheduling hybrid algorithms discrete optimization

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