Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Automated design of population-based algorithms: a case study in vehicle routing

Metaheuristics have been extensively studied to solve constraint combinatorial optimisation problems such as vehicle routing problems. Most existing algorithms require considerable human effort and different kinds of expertise in algorithm design. These manually designed algorithms are discarded after solving the specific instances. It is highly desirable to automate the design of search algorithms, thus to solve problem instances effectively with less human intervention. This thesis develops a novel general search framework to formulate in a unified way a range of population-based algorithms. Within this framework, generic algorithmic components such as selection heuristics on the population and evolution operators are defined, and can be composed using machine learning to generate effective search algorithms automatically. This unified framework aims to serve as the basis to analyse algorithmic components, generating effective search algorithms for complex combinatorial optimisation problems. Three key research issues within the general search framework are identified: automated design of evolution operators, of selection heuristics, and of both. To accurately describe the search space of algorithm design as a new task for machine learning, this thesis identifies new key features, namely search-dependent and instance-dependent features. These features are identified to assist effective algorithm design. With these features, a set of state-of-the-art reinforcement learning techniques, such as deep Q-network based and proximal policy optimisation based models and maximum entropy mechanisms have been developed to intelligently select and combine appropriate evolution operators and selection heuristics during different stages of the optimisation process. The effectiveness and generality of these algorithms automatically designed within the proposed general search framework are validated comprehensively across different capacitated vehicle routing problem with time windows benchmark instances. This thesis contributes to making a key step towards automated algorithm design with a general framework supporting fundamental analysis by effective machine learning

Automated design of population-based algorithms: a case study in vehicle routing

Metaheuristics have been extensively studied to solve constraint combinatorial optimisation problems such as vehicle routing problems. Most existing algorithms require considerable human effort and different kinds of expertise in algorithm design. These manually designed algorithms are discarded after solving the specific instances. It is highly desirable to automate the design of search algorithms, thus to solve problem instances effectively with less human intervention. This thesis develops a novel general search framework to formulate in a unified way a range of population-based algorithms. Within this framework, generic algorithmic components such as selection heuristics on the population and evolution operators are defined, and can be composed using machine learning to generate effective search algorithms automatically. This unified framework aims to serve as the basis to analyse algorithmic components, generating effective search algorithms for complex combinatorial optimisation problems. Three key research issues within the general search framework are identified: automated design of evolution operators, of selection heuristics, and of both. To accurately describe the search space of algorithm design as a new task for machine learning, this thesis identifies new key features, namely search-dependent and instance-dependent features. These features are identified to assist effective algorithm design. With these features, a set of state-of-the-art reinforcement learning techniques, such as deep Q-network based and proximal policy optimisation based models and maximum entropy mechanisms have been developed to intelligently select and combine appropriate evolution operators and selection heuristics during different stages of the optimisation process. The effectiveness and generality of these algorithms automatically designed within the proposed general search framework are validated comprehensively across different capacitated vehicle routing problem with time windows benchmark instances. This thesis contributes to making a key step towards automated algorithm design with a general framework supporting fundamental analysis by effective machine learning

A Review of Methodological Approaches for the Design and Optimization of Wind Farms

This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research

OptPlatform: metaheuristic optimisation framework for solving complex real-world problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment

Proceedings of the XIII Global Optimization Workshop: GOW'16

[Excerpt] Preface: Past Global Optimization Workshop shave been held in Sopron (1985 and 1990), Szeged (WGO, 1995), Florence (GO’99, 1999), Hanmer Springs (Let’s GO, 2001), Santorini (Frontiers in GO, 2003), San José (Go’05, 2005), Mykonos (AGO’07, 2007), Skukuza (SAGO’08, 2008), Toulouse (TOGO’10, 2010), Natal (NAGO’12, 2012) and Málaga (MAGO’14, 2014) with the aim of stimulating discussion between senior and junior researchers on the topic of Global Optimization. In 2016, the XIII Global Optimization Workshop (GOW’16) takes place in Braga and is organized by three researchers from the University of Minho. Two of them belong to the Systems Engineering and Operational Research Group from the Algoritmi Research Centre and the other to the Statistics, Applied Probability and Operational Research Group from the Centre of Mathematics. The event received more than 50 submissions from 15 countries from Europe, South America and North America. We want to express our gratitude to the invited speaker Panos Pardalos for accepting the invitation and sharing his expertise, helping us to meet the workshop objectives. GOW’16 would not have been possible without the valuable contribution from the authors and the International Scientific Committee members. We thank you all. This proceedings book intends to present an overview of the topics that will be addressed in the workshop with the goal of contributing to interesting and fruitful discussions between the authors and participants. After the event, high quality papers can be submitted to a special issue of the Journal of Global Optimization dedicated to the workshop. [...