The bi-objective travelling salesman problem with profits and its connection to computer networks.

This is an interdisciplinary work in Computer Science and Operational Research. As it is well known, these two very important research fields are strictly connected. Among other aspects, one of the main areas where this interplay is strongly evident is Networking. As far as most recent decades have seen a constant growing of every kind of network computer connections, the need for advanced algorithms that help in optimizing the network performances became extremely relevant. Classical Optimization-based approaches have been deeply studied and applied since long time. However, the technology evolution asks for more flexible and advanced algorithmic approaches to model increasingly complex network configurations. In this thesis we study an extension of the well known Traveling Salesman Problem (TSP): the Traveling Salesman Problem with Profits (TSPP). In this generalization, a profit is associated with each vertex and it is not necessary to visit all vertices. The goal is to determine a route through a subset of nodes that simultaneously minimizes the travel cost and maximizes the collected profit. The TSPP models the problem of sending a piece of information through a network where, in addition to the sending costs, it is also important to consider what “profit” this information can get during its routing. Because of its formulation, the right way to tackled the TSPP is by Multiobjective Optimization algorithms. Within this context, the aim of this work is to study new ways to solve the problem in both the exact and the approximated settings, giving all feasible instruments that can help to solve it, and to provide experimental insights into feasible networking instances

Metaheuristic and Multiobjective Approaches for Space Allocation

This thesis presents an investigation on the application of metaheuristic techniques to tackle the space allocation problem in academic institutions. This is a combinatorial optimisation problem which refers to the distribution of the available room space among a set of entities (staff, research students, computer rooms, etc.) in such a way that the space is utilised as efficiently as possible and the additional constraints are satisfied as much as possible. The literature on the application of optimisation techniques to approach the problem mentioned above is scarce. This thesis provides a description and formulation of the problem. It also proposes and compares a range of heuristics for the initialisation of solutions and for neighbourhood exploration. Four well-known metaheuristics (iterative improvement, simulated annealing, tabu search and genetic algorithms) are adapted and tuned for their application to the problem investigated here. The performance of these techniques is assessed and benchmark results are obtained. Also, hybrid approaches are designed that produce sets of high quality and diverse solutions in much shorter time than those required by space administrators who construct solutions manually. The hybrid approaches are also adapted to tackle the space allocation problem from a two-objective perspective. It is also revealed that the use of aggregating functions or relaxed dominance to evaluate solutions in Pareto optimisation, can be more beneficial than the standard dominance relation to enhance the performance of some multiobjective optimisers in some problem domains. A range of single-solution metaheuristics are extended to create hybrid evolutionary approaches based on the scheme of cooperative local search. This scheme promotes the cooperation of a population of local searchers by means of mechanisms to share the information gained during the search. This thesis also reports the best results known so far for a set of test instances of the space allocation problem in academic institutions. This thesis pioneers the application of metaheuristics to solve the space allocation problem. The major contributions are: provides a formulation of the problem together with tests data sets, reports the best known results for these test instances, investigates the multiobjective nature of the problem and proposes a new form of hybridising metaheuristics

Understanding Complexity in Multiobjective Optimization

This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) communities. The purpose of the seminar was to bring together researchers from the two communities to take part in a wide-ranging discussion about the different sources and impacts of complexity in multiobjective optimization. The outcome was a clarified viewpoint of complexity in the various facets of multiobjective optimization, leading to several research initiatives with innovative approaches for coping with complexity

Metaheuristic and Multiobjective Approaches for Space Allocation

This thesis presents an investigation on the application of metaheuristic techniques to tackle the space allocation problem in academic institutions. This is a combinatorial optimisation problem which refers to the distribution of the available room space among a set of entities (staff, research students, computer rooms, etc.) in such a way that the space is utilised as efficiently as possible and the additional constraints are satisfied as much as possible. The literature on the application of optimisation techniques to approach the problem mentioned above is scarce. This thesis provides a description and formulation of the problem. It also proposes and compares a range of heuristics for the initialisation of solutions and for neighbourhood exploration. Four well-known metaheuristics (iterative improvement, simulated annealing, tabu search and genetic algorithms) are adapted and tuned for their application to the problem investigated here. The performance of these techniques is assessed and benchmark results are obtained. Also, hybrid approaches are designed that produce sets of high quality and diverse solutions in much shorter time than those required by space administrators who construct solutions manually. The hybrid approaches are also adapted to tackle the space allocation problem from a two-objective perspective. It is also revealed that the use of aggregating functions or relaxed dominance to evaluate solutions in Pareto optimisation, can be more beneficial than the standard dominance relation to enhance the performance of some multiobjective optimisers in some problem domains. A range of single-solution metaheuristics are extended to create hybrid evolutionary approaches based on the scheme of cooperative local search. This scheme promotes the cooperation of a population of local searchers by means of mechanisms to share the information gained during the search. This thesis also reports the best results known so far for a set of test instances of the space allocation problem in academic institutions. This thesis pioneers the application of metaheuristics to solve the space allocation problem. The major contributions are: provides a formulation of the problem together with tests data sets, reports the best known results for these test instances, investigates the multiobjective nature of the problem and proposes a new form of hybridising metaheuristics

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

Cardinality constraints and dimensionality reduction in optimization problems

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, junio de 201

Cooperation in self-organized heterogeneous swarms

Cooperation in self-organized heterogeneous swarms is a phenomenon from nature with many applications in autonomous robots. I specifically analyzed the problem of auto-regulated team formation in multi-agent systems and several strategies to learn socially how to make multi-objective decisions. To this end I proposed new multi-objective ranking relations and analyzed their properties theoretically and within multi-objective metaheuristics. The results showed that simple decision mechanism suffice to build effective teams of heterogeneous agents and that diversity in groups is not a problem but can increase the efficiency of multi-agent systems

Methodology and Software for Interactive Decision Support

These Proceedings report the scientific results of an International Workshop on "Methodology and Software for Interactive Decision Support" organized jointly by the System and Decision Sciences Program of IIASA and The National Committee for Applied Systems Analysis and Management in Bulgaria. Several other Bulgarian institutions sponsored the workshop -- The Committee for Science to the Council of Ministers, The State Committee for Research and Technology and The Bulgarian Industrial Association. The workshop was held in Albena, on the Black Sea Coast. In the first section, "Theory and Algorithms for Multiple Criteria Optimization," new theoretical developments in multiple criteria optimization are presented. In the second section, "Theory, Methodology and Software for Decision Support Systems," the principles of building decision support systems are presented as well as software tools constituting the building components of such systems. Moreover, several papers are devoted to the general methodology of building such systems or present experimental design of systems supporting certain class of decision problems. The third section addresses issues of "Applications of Decision Support Systems and Computer Implementations of Decision Support Systems." Another part of this section has a special character. Beside theoretical and methodological papers, several practical implementations of software for decision support have been presented during the workshop. These software packages varied from very experimental and illustrative implementations of some theoretical concept to well developed and documented systems being currently commercially distributed and used for solving practical problems