Neuroevolution for solving multiobjective knapsack problems

The multiobjective knapsack problem (MOKP) is an important combinatorial problem that arises in various applications, including resource allocation, computer science and finance. When tackling this problem by evolutionary multiobjective optimization algorithms (EMOAs), it has been demonstrated that traditional recombination operators acting on binary solution representations are susceptible to a loss of diversity and poor scalability. To address those issues, we propose to use artificial neural networks for generating solutions by performing a binary classification of items using the information about their profits and weights. As gradient-based learning cannot be used when target values are unknown, neuroevolution is adapted to adjust the neural network parameters. The main contribution of this study resides in developing a solution encoding and genotype-phenotype mapping for EMOAs to solve MOKPs. The proposal is implemented within a state-of-the-art EMOA and benchmarked against traditional variation operators based on binary crossovers. The obtained experimental results indicate a superior performance of the proposed approach. Furthermore, it is advantageous in terms of scalability and can be readily incorporated into different EMOAs.Portuguese “Fundação para a Ciência e Tecnologia” under grant PEst-C/CTM/LA0025/2013 (Projecto Estratégico - LA 25 - 2013-2014 - Strategic Project - LA 25 - 2013-2014

Xqx Based Modeling For General Integer Programming Problems

We present a new way to model general integer programming (IP) problems with in- equality and equality constraints using XQX. We begin with the definition of IP problems folloby their practical applications, and then present the existing XQX based models to handle such problems. We then present our XQX model for general IP problems (including binary IP) with equality and inequality constraints, and also show how this model can be applied to problems with just inequality constraints. We then present the local optima based solution procedure for our XQX model. We also present new theorems and their proofs for our XQX model. Next, we present a detailed literature survey on the 0-1 multidimensional knapsack problem (MDKP) and apply our XQX model using our simple heuristic procedure to solve benchmark problems. The 0-1 MDKP is a binary IP problem with inequality con- straints and variables with binary values. We apply our XQX model using a heuristics we developed on 0-1 MDKP problems of various sizes and found that our model can handle any problem sizes and can provide reasonable quality results in reasonable time. Finally, we apply our XQX model developed for general integer programming problems on the general multi-dimensional knapsack problems. The general MDKP is a general IP problem with inequality constraints where the variables are positive integers. We apply our XQX model on GMDKP problems of various sizes and find that it can provide reasonable quality results in reasonable time. We also find that it can handle problems of any size and provide fea- sible and good quality solutions irrespective of the starting solutions. We conclude with a discussion of some issues related with our XQX model

Meta-raps: Parameter Setting And New Applications

Recently meta-heuristics have become a popular solution methodology, in terms of both research and application, for solving combinatorial optimization problems. Meta-heuristic methods guide simple heuristics or priority rules designed to solve a particular problem. Meta-heuristics enhance these simple heuristics by using a higher level strategy. The advantage of using meta-heuristics over conventional optimization methods is meta-heuristics are able to find good (near optimal) solutions within a reasonable computation time. Investigating this line of research is justified because in most practical cases with medium to large scale problems, the use of meta-heuristics is necessary to be able to find a solution in a reasonable time. The specific meta-heuristic studied in this research is, Meta-RaPS; Meta-heuristic for Randomized Priority Search which is developed by DePuy and Whitehouse in 2001. Meta-RaPS is a generic, high level strategy used to modify greedy algorithms based on the insertion of a random element (Moraga, 2002). To date, Meta-RaPS had been applied to different types of combinatorial optimization problems and achieved comparable solution performance to other meta-heuristic techniques. The specific problem studied in this dissertation is parameter setting of Meta-RaPS. The topic of parameter setting for meta-heuristics has not been extensively studied in the literature. Although the parameter setting method devised in this dissertation is used primarily on Meta-RaPS, it is applicable to any meta-heuristic\u27s parameter setting problem. This dissertation not only enhances the power of Meta-RaPS by parameter tuning but also it introduces a robust parameter selection technique with wide-spread utility for many meta-heuristics. Because the distribution of solution values generated by meta-heuristics for combinatorial optimization problems is not normal, the current parameter setting techniques which employ a parametric approach based on the assumption of normality may not be appropriate. The proposed method is Non-parametric Based Genetic Algorithms. Based on statistical tests, the Non-parametric Based Genetic Algorithms (NPGA) is able to enhance the solution quality of Meta-RaPS more than any other parameter setting procedures benchmarked in this research. NPGA sets the best parameter settings, of all the methods studied, for 38 of the 41 Early/Tardy Single Machine Scheduling with Common Due Date and Sequence-Dependent Setup Time (ETP) problems and 50 of the 54 0-1 Multidimensional Knapsack Problems (0-1 MKP). In addition to the parameter setting procedure discussed, this dissertation provides two Meta-RaPS combinatorial optimization problem applications, the 0-1 MKP, and the ETP. For the ETP problem, the Meta-RaPS application in this dissertation currently gives the best meta-heuristic solution performance so far in the literature for common ETP test sets. For the large ETP test set, Meta-RaPS provided better solution performance than Simulated Annealing (SA) for 55 of the 60 problems. For the small test set, in all four different small problem sets, the Meta-RaPS solution performance outperformed exiting algorithms in terms of average percent deviation from the optimal solution value. For the 0-1 MKP, the present Meta-RaPS application performs better than the earlier Meta-RaPS applications by other researchers on this problem. The Meta-RaPS 0-1 MKP application presented here has better solution quality than the existing Meta-RaPS application (Moraga, 2005) found in the literature. Meta-RaPS gives 0.75% average percent deviation, from the best known solutions, for the 270 0-1 MKP test problems

Incorporating Memory and Learning Mechanisms Into Meta-RaPS

Due to the rapid increase of dimensions and complexity of real life problems, it has become more difficult to find optimal solutions using only exact mathematical methods. The need to find near-optimal solutions in an acceptable amount of time is a challenge when developing more sophisticated approaches. A proper answer to this challenge can be through the implementation of metaheuristic approaches. However, a more powerful answer might be reached by incorporating intelligence into metaheuristics. Meta-RaPS (Metaheuristic for Randomized Priority Search) is a metaheuristic that creates high quality solutions for discrete optimization problems. It is proposed that incorporating memory and learning mechanisms into Meta-RaPS, which is currently classified as a memoryless metaheuristic, can help the algorithm produce higher quality results. The proposed Meta-RaPS versions were created by taking different perspectives of learning. The first approach taken is Estimation of Distribution Algorithms (EDA), a stochastic learning technique that creates a probability distribution for each decision variable to generate new solutions. The second Meta-RaPS version was developed by utilizing a machine learning algorithm, Q Learning, which has been successfully applied to optimization problems whose output is a sequence of actions. In the third Meta-RaPS version, Path Relinking (PR) was implemented as a post-optimization method in which the new algorithm learns the good attributes by memorizing best solutions, and follows them to reach better solutions. The fourth proposed version of Meta-RaPS presented another form of learning with its ability to adaptively tune parameters. The efficiency of these approaches motivated us to redesign Meta-RaPS by removing the improvement phase and adding a more sophisticated Path Relinking method. The new Meta-RaPS could solve even the largest problems in much less time while keeping up the quality of its solutions. To evaluate their performance, all introduced versions were tested using the 0-1 Multidimensional Knapsack Problem (MKP). After comparing the proposed algorithms, Meta-RaPS PR and Meta-RaPS Q Learning appeared to be the algorithms with the best and worst performance, respectively. On the other hand, they could all show superior performance than other approaches to the 0-1 MKP in the literature

Multi-stage stochastic optimization and reinforcement learning for forestry epidemic and covid-19 control planning

This dissertation focuses on developing new modeling and solution approaches based on multi-stage stochastic programming and reinforcement learning for tackling biological invasions in forests and human populations. Emerald Ash Borer (EAB) is the nemesis of ash trees. This research introduces a multi-stage stochastic mixed-integer programming model to assist forest agencies in managing emerald ash borer insects throughout the U.S. and maximize the public benets of preserving healthy ash trees. This work is then extended to present the first risk-averse multi-stage stochastic mixed-integer program in the invasive species management literature to account for extreme events. Significant computational achievements are obtained using a scenario dominance decomposition and cutting plane algorithm.The results of this work provide crucial insights and decision strategies for optimal resource allocation among surveillance, treatment, and removal of ash trees, leading to a better and healthier environment for future generations. This dissertation also addresses the computational difficulty of solving one of the most difficult classes of combinatorial optimization problems, the Multi-Dimensional Knapsack Problem (MKP). A novel 2-Dimensional (2D) deep reinforcement learning (DRL) framework is developed to represent and solve combinatorial optimization problems focusing on MKP. The DRL framework trains different agents for making sequential decisions and finding the optimal solution while still satisfying the resource constraints of the problem. To our knowledge, this is the first DRL model of its kind where a 2D environment is formulated, and an element of the DRL solution matrix represents an item of the MKP. Our DRL framework shows that it can solve medium-sized and large-sized instances at least 45 and 10 times faster in CPU solution time, respectively, with a maximum solution gap of 0.28% compared to the solution performance of CPLEX. Applying this methodology, yet another recent epidemic problem is tackled, that of COVID-19. This research investigates a reinforcement learning approach tailored with an agent-based simulation model to simulate the disease growth and optimize decision-making during an epidemic. This framework is validated using the COVID-19 data from the Center for Disease Control and Prevention (CDC). Research results provide important insights into government response to COVID-19 and vaccination strategies

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

The quality-aware service selection problem: an adaptive evolutionary approach

Die Qualität der Serviceerbringung (kurz QoS) ist ein wichtiger Aspekt in verteilten, Service-orientierten Systemen. Wenn mehrere Implementierungen einer Funktionalität koexistieren, kann die Wahl eines konkreten Services aufgrund von QoS-Aspekten getroffen werden. Leistung, Verfügbarkeit und Kosten sind Beispiele für QoS-Attribute eines Services. In der vorliegenden Dissertation werden Aspekte dieses Selektionsproblems anhand eines konkreten, Service-orientieren Systems vertieft. Es handelt sich dabei um das TAG-System in ATLAS, einem Hochenergiephysikexperiment am CERN, der Europäischen Organisation für Kernforschung. Die Daten und Services des TAG-Systems sind weltweit verteilt und müssen auf Anfrage selektiert und zu einem Workflow zusammengesetzt werden. Die Optimierung wird aus zwei unterschiedlichen Blickwinkeln. Die Selektion wird als ein dynamisches Pfadoptimierungsproblem unter Nebenbedingungen modelliert, wodurch QoS-Attribute sowohl der Knoten (Services) als auch der Kanten (Netzwerk) berücksichtigt werden können. Dynamische Aspekte des verteilten sind in der Problemformulierung integriert, da sie eine spezifische Herausforderung und Anforderung an Lösungsalgorithmen stellen. Für die dynamische Pareto-Optimierung von Serviceselektionsproblemen wird im Rahmen dieser Arbeit ein Optimierungsansatz mit einem genetischen Algorithmus präsentiert, der über einen persistenten Speicher von früheren Lösungen sowie eine automatische Adaptierung der Mutationsrate eine effiziente Anpassung an das sich ständig verändernde System gewährleistet. Eine Ontologie der Systemkomponenten sowie deren QoS-Attribute bildet die Basis für die Optimierung. Der Ansatz wird im Rahmen der Dissertation hinsichtlich der Qualität der erzielten Lösungen, der Adaptierung an änderungen sowie der Laufzeit evaluiert. Teile des Ansatzes wurden schließ lich in das TAG-System integriert und darin evaluiert.Quality of Service (QoS) is an important aspect in distributed, service-oriented systems. When several concrete services exist that implement the same functionality, the choice of a service instance among many can be made based on QoS considerations, objectives and constraints. Typically considered properties are performance, availability, and costs. In this thesis, aspects of the QoS-aware service selection problem are studied in the context of a distributed, service-oriented system from ATLAS, a high-energy physics experiment at CERN, the European Organization for Nuclear Research. In this so-called TAG system, data and modular services are distributed world-wide and need to be selected and composed on the fly, as a user starts a request. There are two conflicting optimization viewpoints. The service selection is modeled as a dynamic multi-constrained optimal path problem, which allows considering QoS attributes of service instances and of the network. The dynamic aspects of the system are included in the problem definition, as they represent a specific challenge. To address these issues regarding dynamics and conflicting viewpoints, this work proposes a service selection optimization framework based on a multi-objective genetic algorithm capable of efficiently dealing with changing conditions by using a persistent memory of good solutions, and a stepwise adaptation of the mutation rate. A system and QoS attribute ontology as well as a description of dynamics of distributed systems build the basis of the framework. The presented approach is evaluated in terms of optimization quality, adaptability to changes, runtime performance and scalability

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

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