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On October 10, 2018, I gave the research talk Automating Scientific Research in Optimization at the Computational Intelligence Group of Prof. Dr. Sanaz Mostaghim at the Institute of Intelligent Cooperating Systems, Faculty of Computer Science of the Otto von Guericke University Magdeburg (OVGU, Otto-von-Guericke-Universität Magdeburg).

The Computational Intelligence Group has a long and very successful tradition in the field of Computational Intelligence, both in theoretical research as well as in practical application. They focus on the fields of swarm intelligence and swarm robotics. The group also contributes excellent research on Evolutionary Algorithms (EAs), multi-objective optimization and decision making, artificial life, and evolutionary robotics. Multi-objective decision making is a very important branch of optimization, since most problems in the real world present themselves as trade-offs between different goals, e.g., cost vs. speed, cost vs. quality. Multi-objective EAs are currently among the best methods to tackle such problems. Swarm intelligence and swarm robotics play a bigger and bigger role, e.g., in logistics as well as in traffic planning. Since I work in the domain of EAs for more than dozen years now, and our presented benchmarking method directly applies there, this gave us a good starting point for interesting discussions.

I had a nice visit to the group last year, too, and back then was hosted by Prof. Dr. Rudolf Kruse, who now has become an emeritus member but still attended my talk this time. It was a real pleasure to visit this research group again and see the great work that Prof. Mostaghim is doing. I am very thankful for their hospitality.

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On October 8, 2018, I gave the research talk Automating Scientific Research in Optimization at the Mathematics of Transportation and Logistics group of Prof. Dr. Ralf Borndörfer at the Mathematical Optimization department, Mathematical Optimization and Scientific Information division of the Zuse Institute Berlin (ZIB) in Berlin, Germany.

The Mathematical Optimization department of the ZIB contributes research on modeling, simulation, and optimization methods for difficult problems in transport and logistics, telecommunications, energy supply, and healthcare. They combine theoretical insight and practical experience to optimization software for cooperation partners such as Lufthansa or Deutsche Bahn. The group is strong in fundamental research on branch-and-cut-and-price algorithms, graph theory, combinatorics, algorithmic game theory, and convex optimization, especially applicable to large-scale models. They often combine multiple different aspects of a real-world optimization problem, such as different objectives, handle data uncertainty, parallelize algorithms, and develop new decomposition techniques, as well as adaptive and dynamic methods.

Prof. Borndörfer has worked on many interesting projects, especially with the goal to improve the efficiency of public and private transportation systems, such as optimized infrastructure design with respect to passenger behavior in public transport, rolling stock roster planning for railways, multi-day cyclic rotations for trains, service design in public transport, solving the vehicle positioning problem, airline crew scheduling, and cyclic roster planning in public transport.

The background of this group is highly interesting for me. On one hand, our team member Dr. Zijun Wu, works on algorithmic game theory especially with respect to traffic optimization. On the other hand, we also have conducted quite a lot of research on logistics too, e.g., for the Traveling Salesman Problem. There was much to discuss – especially since the algorithm performance analysis methods we develop also fit to the iterative natures of algorithms of the branch-and-bound family.

It was a real pleasure to visit this research group, to meet Prof. Borndörfer and to meet Prof. Möhring again, who helped in arranging the meeting. I am very thankful to both professors and to the kind audience. really enjoyed our talk.

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Today, our article "Automatically discovering clusters of algorithm and problem instance behaviors as well as their causes from experimental data, algorithm setups, and instance features" has appeared in the Applied Soft Computing journal published by Elsevier, which describes our research topic Automating Scientific Research in Optimization. This article maybe marks the first contribution where a significant part of the high-level work of a researcher in the fields of optimization and machine learning is automated by a process applying different machine learning steps.

Thomas Weise, Xiaofeng Wang, Qi Qi, Bin Li, and Ke Tang. Automatically discovering clusters of algorithm and problem instance behaviors as well as their causes from experimental data, algorithm setups, and instance features. Applied Soft Computing Journal (ASOC), 73:366–382, December 2018.
doi:10.1016/j.asoc.2018.08.030 / share link (valid until November 6, 2018)

In the fields of heuristic optimization, we aim to get good solutions for computationally hard problems. Solving the Travelling Salesman Problem, for instance, means to find the shortest tour that goes through n cities and returns back to the starting point. Such problems often cannot be solved to optimality in feasible time due to their complexity. This means that algorithms often start with a more or less random initial guess about the solution and then step-by-step improve it. This means performance has two dimensions: the runtime we grant to the algorithm until we stop it and take the best-so-far result and the solution quality of that best-so-far result. Since there currently are not yet sufficient theoretical tools to assess the performance of such algorithms, researchers conduct many experiments and compare the results. This often means to apply many different setups of an algorithm to many different instances of a problem type. Since optimization algorithms are often randomized, multiple repetitions of the experiments are needed. Evaluating such experimental data is not easy. Moreover, as evaluation result, we do not just want to know which algorithm performs best and which problem is the hardest ― a researcher wants to know why.

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I am happy to announce that our group has moved into our new offices in the new building 52 with the nice name [合肥学院综合实验楼]. While the new offices are still temporary, they are very nice and modern. We not just have much more space than before, all the members of our young group can now also finally be co-located, which will make collaboration much easier. We are very thankful for the support of our university and faculty. The new building is quite beautiful, modern, and even equipped with solar panels on the facade! Our campus is now growing quickly and, with the generous support of the city and the government, our university adds more and more facilities and buildings.

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Introduction

A long time ago, when I was a PhD student, I wrote the book Global Optimization Algorithms – Theory and Application, which I published on my personal website as pdf. Since I recently developed the short course Metaheuristics for Smart Manufacturing and had a very nice experience teaching it, I have decided to begin to write a new book about optimization, "An Introduction to Optimization Algorithms," to incorporate my experience during the past ten years working in the field. When writing such a book, there are a couple of desirable features to improve the workflow and results, such as:

  • using a version control and distributed authoring system, which allows me to easily work on and extend the book as well as to make changes to the book and commit them wherever I am,
  • automated conversion of the book's sources to PDF, ideally whenever I commit a change,
  • automated provision of the PDF version of the book at an online location,
  • the generation of an electronic version of the book more suitable for handheld devises like mobile phones, i.e., an EPUB version, which should be automatically be built and provisioned like the PDF version,
  • maybe even the possibility that readers can file change requests, ask questions, or propose content to add in a structured way, and
  • the option to edit source code examples in an indepented repository and update the book whenever they change.

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