Multi-Stage Genetic Algorithm for a Class of Large Scale Optimization Problems.

概要

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The multi-stage genetic algorithm is proposed to solve a class of large scale optimization problems. The original problem with complicated constraints is divided into a supervisary problem and local sub-problems with simple constraints. Every sub-problem is solved by GA to generate a set of suboptimal solutions. And in the supervisary problem, the elements of each set are optimally combined by GA to yield the optimal solution for the original problem. The empirical knowledge obtained by solving the problem is effectively utilized to solve similar problems. The method is thus regarded as a learning solution. The extended knapsac problem is formulated and solved as an example to demonstrate the proposed method, and the efficiency of the method is shown. In addition, the method is successfully applied to optimal operation planning problem at LNG(Liquefied Natural Gas) terminal.
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