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Related Experiment Videos

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

M Emmerich1, M Grötzner, M Schütz

  • 1Center for Applied Systems Analysis, Informatik Centrum Dortmund (ICD/CASA), Joseph von Fraunhofer Strasse 20, 44227 Dortmund, Germany. emmerich@ls11.cs.uni-dortmund.de

Evolutionary Computation
|August 28, 2001
PubMed
Summary
This summary is machine-generated.

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This study adapts evolutionary algorithms (EAs) for chemical plant structural optimization. It introduces a novel network representation and problem-specific search operators for efficient, metric-based evolutionary algorithm (MBEA) design, applicable to similar network optimization tasks.

Area of Science:

  • Chemical Engineering
  • Computational Science
  • Optimization Theory

Background:

  • Structural optimization of chemical engineering plants is complex.
  • Existing methods may lack efficiency for variable structures.
  • Evolutionary algorithms (EAs) offer a potential framework for such problems.

Purpose of the Study:

  • To adapt evolutionary algorithms (EAs) for the structural optimization of chemical engineering plants.
  • To introduce a novel network representation for chemical engineering plants.
  • To develop problem-specific search operators within the metric-based evolutionary algorithms (MBEA) framework.

Main Methods:

  • Utilizing rigorous process simulation and realistic costing for objective function evaluation.
  • Introducing a network representation with typed vertices and variable structure.

Related Experiment Videos

  • Developing problem-specific search operators for stochastic optimization procedures.
  • Orienting algorithm design within the systematic framework of metric-based evolutionary algorithms (MBEAs).

    • Main Results:

    • Demonstrated the applicability of the approach through a reference example.
    • Introduced a technique for creating problem-specific search operators for network representations.
    • Proposed a useful distance measure for variable dimensionality search spaces.
    • Showcased the transferability of the algorithmic design to similar network optimization problems.

    Conclusions:

    • The proposed adaptation of EAs, particularly MBEAs, is effective for chemical plant structural optimization.
    • The novel network representation and search operators facilitate efficient optimization.
    • The methodology is adaptable to a broader range of network optimization problems.