Automatic Routing of Printed Circuit Board Traces Using Ant Colony Optimization Algorithm

The advancement of electronics engineering enables the reduction of component size and the increase in operating frequencies. Consequently, electromagnetic tolerance and limitations of circuits become increasingly restrictive. To mitigate these difficulties, strategies exploring computational intelligence are applied to obtain viable routing solutions in a timely manner. Among these strategies, swarm intelligence-based algorithms are highly relevant. In this work, a variation of the canonical ant colony optimization algorithm is applied. The algorithm is modified to perform routing on Printed Circuit Boards (PCBs), focusing on matching the lengths of circuit traces. The PCB area is viewed as a grid, representing the search space of the algorithm. The routing of each trace is performed by a separate ant colony. The ants seek the path that best satisfies a multi-objective optimization function that simultaneously minimizes two objectives: the number of times traces intersect and the length difference between these traces. The proposed algorithm is validated using three test scenarios. The achieved results demonstrate that the strategy is viable for routing traces on PCBs with trace length matching, producing innovative solutions.