Ant colony algorithm and genetic algorithm optimization for test vector reordering

作者：Shang Jin^*; Zhang Liyong

来源：Information Technology Journal, 2012, 11(12): 1786-1789.

DOI：10.3923/itj.2012.1786.1789

• Ant colony algorithms
• Benchmark circuit
• Genetic-algorithm optimizations
• Normal modes
• Switching activities
• Test vector reordering

Power dissipation during testing has been found to be much more than during normal mode due to increased switching activity. Test vector reordering technique helps mitigate this problem as it enables the reduction of switching activity during testing. This study presents a new test vector reordering approach. This approach maps the reordering problem to Traveling Salesman Problem (TSP). A hybrid ant colony algorithm and genetic algorithm is presented to solve this problem. The proposed approach is effective and verified with ISCAS';85 benchmark circuits which shows that yield on an average of about 30.05% reduction in switching activity.

• 出版日期2012