Surrogate Assisted Optimisation for Travelling Thief Problems

The travelling thief problem (TTP) is a multi-component optimisation problem involving two interdependent NP-hard components: the travelling salesman problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP solvers modify the underlying TSP and KP solutions in an iterative and interleaved fashion. The TSP solution (cyclic tour) is typically changed in a deterministic way, while changes to the KP solution typically involve a random search, effectively resulting in a quasi-meandering exploration of the TTP solution space. Once a plateau is reached, the iterative search of the TTP solution space is restarted by using a new initial TSP tour. We propose to make the search more efficient through an adaptive surrogate model (based on a customised form of Support Vector Regression) that learns the characteristics of initial TSP tours that lead to good TTP solutions. The model is used to filter out non-promising initial TSP tours, in effect reducing the amount of time spent to find a good TTP solution. Experiments on a broad range of benchmark TTP instances indicate that the proposed approach filters out a considerable number of non-promising initial tours, at the cost of omitting only a small number of the best TTP solutions

Solving Travelling Thief Problems using Coordination Based Methods

A travelling thief problem (TTP) is a proxy to real-life problems such as postal collection. TTP comprises an entanglement of a travelling salesman problem (TSP) and a knapsack problem (KP) since items of KP are scattered over cities of TSP, and a thief has to visit cities to collect items. In TTP, city selection and item selection decisions need close coordination since the thief's travelling speed depends on the knapsack's weight and the order of visiting cities affects the order of item collection. Existing TTP solvers deal with city selection and item selection separately, keeping decisions for one type unchanged while dealing with the other type. This separation essentially means very poor coordination between two types of decision. In this paper, we first show that a simple local search based coordination approach does not work in TTP. Then, to address the aforementioned problems, we propose a human designed coordination heuristic that makes changes to collection plans during exploration of cyclic tours. We further propose another human designed coordination heuristic that explicitly exploits the cyclic tours in item selections during collection plan exploration. Lastly, we propose a machine learning based coordination heuristic that captures characteristics of the two human designed coordination heuristics. Our proposed coordination based approaches help our TTP solver significantly outperform existing state-of-the-art TTP solvers on a set of benchmark problems. Our solver is named Cooperation Coordination (CoCo) and its source code is available from https://github.com/majid75/CoCoComment: expanded and revised version of arXiv:1911.0312

Evolutionary Diversity Optimisation for Combinatorial Problems

Diversity optimisation explores a variety of solutions for the intended problem and is rapidly growing and getting more popular within the evolutionary computation community as a result. There can be found several studies that introduce and examine evolutionary approaches to compute a diverse set of solutions for optimisation problems in the continuous domain. To the best of our knowledge, the discrete problems are yet to be studied in the context of diversity optimisation. Thus, this thesis focuses on combinatorial optimisation problems with discrete solution spaces. Here, we compute and explore such solution sets for several noticeable combinatorial problems. We aim to introduce and design evolutionary algorithms capable of computing a diverse set of solutions for the given combinatorial optimisation problem. First, we begin with a comprehensive literature review of the recent developments and then dig deep into two prominent diverse paradigms in evolutionary computation: evolutionary diversity optimisation and quality diversity. These concepts have gained a considerable amount of attention in recent years. Quality diversity aims to achieve diversity in behavioural spaces, while evolutionary diversity optimisation sees diversity in the structural properties of solutions. We study the evolutionary algorithms for the travelling salesperson problem, the travelling thief program, the knapsack problem, and finally, the Boolean satisfiability problem. The prospective results demonstrate the capability of the introduced algorithms to achieve diverse and high-quality solutions.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202