Printed Circuit Board Design via Organizational-Learning Agents

Abstract

This paper proposes a novel evolutionary computation model: Organizational-Learning Oriented Classifier System (OCS), and describes its application to Printed Circuit Boards (PCBs) redesign problems in a computer aided design (CAD). Using the conventional CAD systems which explicitly decide the parts' placements by a knowledge base, the systems cannot effectively place the parts as done by human experts. Furthermore, the supports of human experts are intrinsically required to satisfy the constraints and to optimize a global objective function. However, in the proposed model OCS, the parts generate and acquire adaptive behaviors for an appropriate placement without explicit control. In OCS, we focus upon emergent processes in which the parts dynamically form an organized group with autonomously generating adaptive behaviors through local interaction among them. Using the model OCS, we have conducted intensive experiments on a practical PCB redesign problem for electric appliances. The experimental results have shown that: (1) it has found the feasible solutions of the same level as the ones by human experts, (2) solutions are locally optimal, and also globally better than the ones by human experts with regard to the total wiring length, and (3) the solutions are more preferable than those in the conventional CAD systems.