Welcome to the Institute of Applied Optimization (IAO) [应用优化研究所] of the School of Artificial Intelligence and Big Data [人工智能与大数据学院] of the Hefei University [合肥学院]. Founded in December 2016, we are pursuing to become a strong research group in the field of applied mathematical and combinatorial optimization, operations research, Computational Intelligence machine learning, metaheuristics, and evolutionary computation.

We jointly organize the Special Session on Benchmarking of Computational Intelligence Algorithms (BOCIA'21) as parts of the 2021 IEEE Congress on Evolutionary Computation (CEC 2021), June 28-July 1, 2021 in Kraków, Poland (CfP).

The above is a continuation of our efforts in the field, after already jointly organizing the successful BOCIA'18, BB-DOB@GECCO'18, BB-DOB@PPSN'18, BB-DOB@GECCO'19, BENCHMARK@GECCO'20, and BENCHMARK@PPSN'20 workshops and the Special Issue on Benchmarking of Computational Intelligence Algorithms in the Applied Soft Computing journal (published 2020).

Mission Statement

The rise of vast computing power and data storage capacity together with the ubiquitous availability of the internet has brought us to the dawn of a new era, which leaves no aspect of industry, business, services, logistics, or even basic infrastructure unchanged. An enterprise must seek to take advantage of these new resources in order to stay ahead of the market, to become more efficient, to reduce its costs, and to increase its speed. Computational Intelligence conquers tasks which were previously pure human domains, ranging from driving vehicles, playing Go, to the automated planning of intermodal logistics tasks involving hundreds of trucks, trains, and ships and to the scheduling of the production of complete multi-production line factories.

Optimization and operations research are technologies which can be applied to increase the efficiency of virtually every single aspect of a company's operation. They can be used to improve the design of products and their robustness and quality. They can be used to reduce waste. They can be used to improve the scheduling of staff, work shifts, or the utilization of machines. They can improve the floor plan of workshops. They can be used for improving stockkeeping. They can improve virtually all aspects of logistics and even whole supply chains. And they can be combined with models obtained via Machine Learning and Data Mining to better plan ahead.

This is what we want to do: We want cooperate with partners from academia and different industries. We contribute both fundamental research on optimization as well as develop robust and reliable tailor-made software solutions. Our research aims to help to increase production efficiency and speed, to reduce costs and resource consumption, and to even improve the quality of products. We further believe that optimization is one of the key technologies for making concepts such as Industry 4.0 and Made in China 2025 [中国制造2025] successful, as it can enable both intelligent, automatic control of networked manufacturing and management processes as well as a greener production.