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Net Journal 6

The world's fastest-level hyper scale optimization - performing one hundred thousand to one million combinations of calculations

---- Learning functions and artificial life remind us of robots from science fiction, but autonomous systems engineering is a technology used in our immediate surroundings, isn't it?

Masashi Furukawa, Doctor of Engineering

Dr.Furukawa: Exactly. The third project, "hyper scale optimization," is used in our immediate surroundings. Optimization means finding the most suitable combination under certain conditions using computers. It is like finding the shortest route connecting 10 cities, for example. There are 300,000 ways of connecting 10 cities and the most efficient route will be found through the comparison of all those routes. I started research 15 years ago and have engaged myself in applied development, through industry-university cooperation, based on a GA scheduler, which achieves optimization using GA. Since this is very helpful for enhancing the efficiency of physical distribution systems, it has been actually used at manufacturers' delivery centers, in the transportation industry and in other areas. It is now possible for computers to calculate, based on optimization theory, what people have scheduled based on their empirical rules. In addition, it is now obvious that GA-based optimization has its own limits. For example, it cannot perform optimization across as many as 100,000 to one million combinations for encryption or large-scale scheduling of factory tasks. Therefore, we developed the Local Clustering Organization (LCO) of algorithms by combining and further evolving GA and self-organizing learning (Ex. 2 / Fig. 3). This has significantly reduced computation time. We have an optimization example with a salesperson covering 30,000 cities, and it was verified that the optimization can be done dramatically faster and more accurately compared with other methods. Since the LCO is a general solution applicable to various fields, not just physical distribution, we will continue our research efforts and materialize more practical systems.


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