Constraint handling in the evolutionary optimization of pipeless chemical batch plants

Authors:
Sabine Piana;Sebastian Engell
Affiliations:
Process Dynamics and Operations Group, TU Dortmund and Department of Biochemical and Chemical Engineering, Technische Universität Dortmund, Germany;Process Dynamics and Operations Group, TU Dortmund and Department of Biochemical and Chemical Engineering, Technische Universität Dortmund, Germany
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 3
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Lamarckian Evolution, The Baldwin Effect and Function Optimization

PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Feature Article: Optimization for simulation: Theory vs. Practice

INFORMS Journal on Computing
Introduction to Evolutionary Computing

Introduction to Evolutionary Computing

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Abstract

Evolutionary algorithms were originally designed for the optimization of unconstrained problems. When applied to constrained real-world problems, for example to the optimization of the operation of pipeless chemical batch plants, the constraints have to be taken into account to generate feasible solutions. This paper examines different approaches of constraint handling within the framework of an evolutionary scheduling algorithm and a heuristic schedule builder. Repair algorithms eliminate most infeasibilities before passing a candidate solution to the schedule builder. This is shown to be more efficient than dealing with the constraints inside the schedule builder or simply rejecting infeasible solutions.