A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problem

Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain. © 2016 Massachusetts Institute of Technolog

Применение генетического алгоритма для решения задач многомерной оптимизации

Сформулирована задача многомерной оптимизации и предложено ее решение, базирующееся на генетическом алгоритме. Рассмотрены основные достоинства и недостатки данного подхода

Electronic commerce logistics and the knapsack problem

The research object of this dissertation has been e-commerce and the knapsack problem from the perspective of logistics and controlling and consists of two parts, each of which contains two papers. In the first part, through analysis of and research on two specific issues, this dissertation has contributed mainly to the interpretation of the relationship between logistics and e-commerce. The second part of this dissertation provides for companies a free, efficient and easy-to-use optimization software for solving knapsack problems and based on genetic algorithms

Crossover control in selection hyper-heuristics: case studies using MKP and HyFlex

Hyper-heuristics are a class of high-level search methodologies which operate over a search space of heuristics rather than a search space of solutions. Hyper-heuristic research has set out to develop methods which are more general than traditional search and optimisation techniques. In recent years, focus has shifted considerably towards cross-domain heuristic search. The intention is to develop methods which are able to deliver an acceptable level of performance over a variety of different problem domains, given a set of low-level heuristics to work with. This thesis presents a body of work investigating the use of selection hyper-heuristics in a number of different problem domains. Specifically the use of crossover operators, prevalent in many evolutionary algorithms, is explored within the context of single-point search hyper-heuristics. A number of traditional selection hyper-heuristics are applied to instances of a well-known NP-hard combinatorial optimisation problem, the multidimensional knapsack problem. This domain is chosen as a benchmark for the variety of existing problem instances and solution methods available. The results suggest that selection hyper-heuristics are a viable method to solve some instances of this problem domain. Following this, a framework is defined to describe the conceptual level at which crossover low-level heuristics are managed in single-point selection hyper-heuristics. HyFlex is an existing software framework which supports the design of heuristic search methods over multiple problem domains, i.e. cross-domain optimisation. A traditional heuristic selection mechanism is modified in order to improve results in the context of cross-domain optimisation. Finally the effect of crossover use in cross-domain optimisation is explored

Interdependent Security and Compliance in Service Selection

Application development today is characterized by ever shorter release cycles and more frequent change requests. Hence development methods such as service composition are increasingly arousing interest as viable alternative approaches. While employing web services as building blocks rapidly reduces development times, it raises new challenges regarding security and compliance since their implementation remains a black box which usually cannot be controlled. Security in particular gets even more challenging since some applications require domainspecific security objectives such as location privacy. Another important aspect is that security objectives are in general no singletons but subject to interdependence. Hence this thesis addresses the question of how to consider interdependent security and compliance in service composition. Current approaches for service composition do neither consider interdependent security nor compliance. Selecting suiting services for a composition is a combinatorial problem which is known to be NP-hard. Often this problem is solved utilizing genetic algorithms in order to obtain near-optimal solutions in reasonable time. This is particularly the case if multiple objectives have to be optimized simultaneously such as price, runtime and data encryption strength. Security properties of compositions are usually verified using formal methods. However, none of the available methods supports interdependence effects or defining arbitrary security objectives. Similarly, no current approach ensures compliance of service compositions during service selection. Instead, compliance is verified afterwards which might necessitate repeating the selection process in case of a non-compliant solution. In this thesis, novel approaches for considering interdependent security and compliance in service composition are being presented and discussed. Since no formal methods exist covering interdependence effects for security, this aspect is covered in terms of a security assessment. An assessment method is developed which builds upon the notion of structural decomposition in order to assess the fulfillment of arbitrary security objectives in terms of a utility function. Interdependence effects are being modeled as dependencies between utility functions. In order to enable compliance-awareness, an approach is presented which checks compliance of compositions during service selection and marks non-compliant parts. This enables to repair the corresponding parts during the selection process by replacing the current services and hence avoids the necessity to repeat the selection process. It is demonstrated how to embed the presented approaches into a genetic algorithm in order to ease integration with existing approaches for service composition. The developed approaches are being compared to state-of-the-art genetic algorithms using simulations

Crossover control in selection hyper-heuristics: case studies using MKP and HyFlex

Hyper-heuristics are a class of high-level search methodologies which operate over a search space of heuristics rather than a search space of solutions. Hyper-heuristic research has set out to develop methods which are more general than traditional search and optimisation techniques. In recent years, focus has shifted considerably towards cross-domain heuristic search. The intention is to develop methods which are able to deliver an acceptable level of performance over a variety of different problem domains, given a set of low-level heuristics to work with. This thesis presents a body of work investigating the use of selection hyper-heuristics in a number of different problem domains. Specifically the use of crossover operators, prevalent in many evolutionary algorithms, is explored within the context of single-point search hyper-heuristics. A number of traditional selection hyper-heuristics are applied to instances of a well-known NP-hard combinatorial optimisation problem, the multidimensional knapsack problem. This domain is chosen as a benchmark for the variety of existing problem instances and solution methods available. The results suggest that selection hyper-heuristics are a viable method to solve some instances of this problem domain. Following this, a framework is defined to describe the conceptual level at which crossover low-level heuristics are managed in single-point selection hyper-heuristics. HyFlex is an existing software framework which supports the design of heuristic search methods over multiple problem domains, i.e. cross-domain optimisation. A traditional heuristic selection mechanism is modified in order to improve results in the context of cross-domain optimisation. Finally the effect of crossover use in cross-domain optimisation is explored

Adaptive binary artificial bee colony algorithm

Metaheuristics and swarm intelligence algorithms are bio-inspired algorithms, which have long standing track record of success in problem solving. Due to the nature and the complexity of the problems, problem solving approaches may not achieve the same success level in every type of problems. Artificial bee colony (ABC) algorithm is a swarm intelligence algorithm and has originally been developed to solve numerical optimisation problems. It has a sound track record in numerical problems, but has not yet been tested sufficiently for combinatorial and binary problems. This paper proposes an adaptive hybrid approach to devise ABC algorithms with multiple and complementary binary operators for higher efficiency in solving binary problems.} Three prominent operator selection schemes have been comparatively investigated for the best configuration in this regard. The proposed approach has been applied to uncapacitated facility location problems, a renown NP-Hard combinatorial problem type modelled with 0-1 programming, and successfully solved the well-known benchmarks outperforming state-of-art algorithms

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

Module reallocation problem in the context of multi-campus university course timetabling

Ph.DDOCTOR OF PHILOSOPH