185 research outputs found

    A comparative study of evolutionary approaches to the bi-objective dynamic Travelling Thief Problem

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    Dynamic evolutionary multi-objective optimization is a thriving research area. Recent contributions span the development of specialized algorithms and the construction of challenging benchmark problems. Here, we continue these research directions through the development and analysis of a new bi-objective problem, the dynamic Travelling Thief Problem (TTP), including three modes of dynamic change: city locations, item profit values, and item availability. The interconnected problem components embedded in the dynamic problem dictate that the effective tracking of good trade-off solutions that satisfy both objectives throughout dynamic events is non-trivial. Consequently, we examine the relative contribution to the non-dominated set from a variety of population seeding strategies, including exact solvers and greedy algorithms for the knapsack and tour components, and random techniques. We introduce this responsive seeding extension within an evolutionary algorithm framework. The efficacy of alternative seeding mechanisms is evaluated across a range of exemplary problem instances using ranking-based and quantitative statistical comparisons, which combines performance measurements taken throughout the optimization. Our detailed experiments show that the different dynamic TTP instances present varying difficulty to the seeding methods tested. We posit the dynamic TTP as a suitable benchmark capable of generating problem instances with different controllable characteristics aligning with many real-world problems

    Exact and heuristic approaches for multi-component optimisation problems

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    Modern real world applications are commonly complex, consisting of multiple subsystems that may interact with or depend on each other. Our case-study about wave energy converters (WEC) for the renewable energy industry shows that in such a multi-component system, optimising each individual component cannot yield global optimality for the entire system, owing to the influence of their interactions or the dependence on one another. Moreover, modelling a multi-component problem is rarely easy due to the complexity of the issues, which leads to a desire for existent models on which to base, and against which to test, calculations. Recently, the travelling thief problem (TTP) has attracted significant attention in the Evolutionary Computation community. It is intended to offer a better model for multicomponent systems, where researchers can push forward their understanding of the optimisation of such systems, especially for understanding of the interconnections between the components. The TTP interconnects with two classic NP-hard problems, namely the travelling salesman problem and the 0-1 knapsack problem, via the transportation cost that non-linearly depends on the accumulated weight of items. This non-linear setting introduces additional complexity. We study this nonlinearity through a simplified version of the TTP - the packing while travelling (PWT) problem, which aims to maximise the total reward for a given travelling tour. Our theoretical and experimental investigations demonstrate that the difficulty of a given problem instance is significantly influenced by adjusting a single parameter, the renting rate, which prompted our method of creating relatively hard instances using simple evolutionary algorithms. Our further investigations into the PWT problem yield a dynamic programming (DP) approach that can solve the problem in pseudo polynomial time and a corresponding approximation scheme. The experimental investigations show that the new approaches outperform the state-of-the-art ones. We furthermore propose three exact algorithms for the TTP, based on the DP of the PWT problem. By employing the exact DP for the underlying PWT problem as a subroutine, we create a novel indicator-based hybrid evolutionary approach for a new bi-criteria formulation of the TTP. This hybrid design takes advantage of the DP approach, along with a number of novel indicators and selection mechanisms to achieve better solutions. The results of computational experiments show that the approach is capable to outperform the state-of-the-art results.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201

    Evolutionary Diversity Optimisation for The Traveling Thief Problem

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    There has been a growing interest in the evolutionary computation community to compute a diverse set of high-quality solutions for a given optimisation problem. This can provide the practitioners with invaluable information about the solution space and robustness against imperfect modelling and minor problems' changes. It also enables the decision-makers to involve their interests and choose between various solutions. In this study, we investigate for the first time a prominent multi-component optimisation problem, namely the Traveling Thief Problem (TTP), in the context of evolutionary diversity optimisation. We introduce a bi-level evolutionary algorithm to maximise the structural diversity of the set of solutions. Moreover, we examine the inter-dependency among the components of the problem in terms of structural diversity and empirically determine the best method to obtain diversity. We also conduct a comprehensive experimental investigation to examine the introduced algorithm and compare the results to another recently introduced framework based on the use of Quality Diversity (QD). Our experimental results show a significant improvement of the QD approach in terms of structural diversity for most TTP benchmark instances.Comment: To appear at GECCO 202

    Packing While Traveling: Mixed Integer Programming for a Class of Nonlinear Knapsack Problems

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    Packing and vehicle routing problems play an important role in the area of supply chain management. In this paper, we introduce a non-linear knapsack problem that occurs when packing items along a fixed route and taking into account travel time. We investigate constrained and unconstrained versions of the problem and show that both are NP-hard. In order to solve the problems, we provide a pre-processing scheme as well as exact and approximate mixed integer programming (MIP) solutions. Our experimental results show the effectiveness of the MIP solutions and in particular point out that the approximate MIP approach often leads to near optimal results within far less computation time than the exact approach

    Mathematical models and heuristic algorithms for routing problems with multiple interacting components.

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    Programa de P?s-Gradua??o em Ci?ncia da Computa??o. Departamento de Ci?ncia da Computa??o, Instituto de Ci?ncias Exatas e Biol?gicas, Universidade Federal de Ouro Preto.Muitos problemas de otimiza??o com aplica??es reais t?m v?rios componentes de intera??o. Cada um deles pode ser um problema pertencente ? classe N P-dif?cil, e eles podem estar em conflito um com o outro, ou seja, a solu??o ?tima para um componente n?o representa necessariamente uma solu??o ?tima para os outros componentes. Isso pode ser um desafio devido ? influ?ncia que cada componente tem na qualidade geral da solu??o. Neste trabalho, foram abordados quatro problemas de roteamento complexos com v?rios componentes de intera??o: o Double Vehicle Routing Problem with Multiple Stacks (DVRPMS), o Double Traveling Salesman Problem with Partial Last-InFirst-Out Loading Constraints (DTSPPL), o Traveling Thief Problem (TTP) e Thief Orienteering Problem (ThOP). Enquanto os DVRPMS e TTP j? s?o bem conhecidos na literatura, os DTSPPL e ThOP foram recentemente propostos a fim de introduzir e estudar variantes mais realistas dos DVRPMS e TTP, respectivamente. O DTSPPL foi proposto a partir deste trabalho, enquanto o ThOP foi proposto de forma independente. Neste trabalho s?o propostos modelos matem?ticos e/ou algoritmos heur?sticos para a solu??o desses problemas. Dentre os resultados alcan?ados, ? poss?vel destacar que o modelo matem?tico proposto para o DVRPMS foi capaz de encontrar inconsist?ncias nos resultados dos algoritmos exatos previamente propostos na literatura. Al?m disso, conquistamos o primeiro e o segundo lugares em duas recentes competi??es de otimiza??o combinat?ria que tinha como objetivo a solu??o de uma vers?o bi-objetiva do TTP. Em geral, os resultados alcan?ados por nossos m?todos de solu??es mostraram-se melhores do que os apresentados anteriormente na literatura considerando cada problema investigado neste trabalho.I would like to express my greatest thanks to my parents, Jo?o Batista and Adelma, and my sister, Jaqueline, for their wise counsel. They have always supported me and given me the strength to continue towards my goals. To Bruna Vilela, I am grateful for her fondness, for always listening to my complaints, and for celebrating with me my personal and academic achievements. I love you all demais da conta1 ! Throughout the writing of this thesis, I have received great assistance. I would like to acknowledge my advisors, Prof. Ph.D. Marcone J. F. Souza, and Prof. Ph.D. Andr? G. Santos, for their support and guidance over these years. I would also like to thank all the authors who have contributed to the research papers produced from this work, in particular, to Prof. Ph.D. Markus Wagner for his great collaboration in some of my projects. I would like to thank Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES), and Universidade Federal de Ouro Preto (UFOP) for funding this project. I thank the Universidade Federal de Vi?osa (UFV) for receiving me as a collaborating researcher over these last two years. I could not but offer up my thanks to the HassoPlattner-Institut (HPI) Future SOC Lab, the Divis?o de Suporte ao Desenvolvimento Cient?fico e Tecnol?gico (DCT/UFV), and the Programa de P?s-gradua??o em Ci?ncia da Computa??o (PPGCC/UFOP) for enabling this research by providing access to their computing infrastructure

    On the Impact of Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem

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    Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments. It is important to understand the behaviour of evolutionary algorithms for complex optimization problems that also involve dynamic and/or stochastic components in a systematic way in order to further increase their applicability to real-world problems. We investigate the node weighted traveling salesperson problem (W-TSP), which provides an abstraction of a wide range of weighted TSP problems, in dynamic settings. In the dynamic setting of the problem, items that have to be collected as part of a TSP tour change over time. We first present a dynamic setup for the dynamic W-TSP parameterized by different types of changes that are applied to the set of items to be collected when traversing the tour. Our first experimental investigations study the impact of such changes on resulting optimized tours in order to provide structural insights of optimization solutions. Afterwards, we investigate simple mutation-based evolutionary algorithms and study the impact of the mutation operators and the use of populations with dealing with the dynamic changes to the node weights of the problem
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