Constraint Handling in the Evolutionary Optimization of Pipeless Chemical Batch Plants

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.