Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

In developing multi-robot cooperative systems, there are often competing objectives that need to be met. For example in automating area defense systems, multiple robots must work together to explore the entire area, and maintain consistent communications to alert the other agents and ensure trust in the system. This research presents an algorithm that tasks robots to meet the two specific goals of exploration and communication maintenance in an uncoordinated environment reducing the need for a user to pre-balance the objectives. This multi-objective problem is defined as a constraint satisfaction problem solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Both goals of exploration and communication maintenance are described as fitness functions in the algorithm that would satisfy their corresponding constraints. The exploration fitness was described in three ways to diversify the way exploration was measured, whereas the communication maintenance fitness was calculated as the number of independent clusters of agents. Applying the algorithm to the area defense problem, results show exploration and communication without coordination are two diametrically opposed goals, in which one may be favored, but only at the expense of the other. This work also presents suggestions for anyone looking to take further steps in developing a physically grounded solution to this area defense problem

Multi-objective optimization for time-based preventive maintenance within the transport network: a review

Preventive maintenance in transportation is essential not only to safeguard billions in business and infrastructure investment, but also to guarantee safety, reliability and efficacy within the network. Government, industry and society have been increasingly recognising the importance of keeping transport units condition well-preserved. The challenge, however, is to achieve optimal performance of the existing transport systems within acceptable costs, effective workforce use and minimum disruption. Those are generally conflicting objectives. Multi-objective optimisation approaches have served as powerful tools to assist stakeholders to properly deploy preventive maintenance in industry. In this study, we review the research conducted in the application of multi-objective optimisation for preventive maintenance in transport-related activities. We focus on time-based preventive maintenance for production, infrastructure, rail and energy providers. In our review, we are interested in aspects such as the types of problems addressed, the existing objectives, the approaches to solutions, and how the outcomes obtained support decision

On the Modeling, Analysis and Development of PMSM: For Traction and Charging Application

Permanent magnet synchronous machines (PMSMs) are widely implemented commercially available traction motors owing to their high torque production capability and wide operating speed range. However, to achieve significant electric vehicle (EV) global market infiltration in the coming years, the technological gaps in the technical targets of the traction motor must be addressed towards further improvement of driving range per charge of the vehicle and reduced motor weight and cost. Thus, this thesis focuses on the design and development of a novel high speed traction PMSM with improved torque density, maximized efficiency, reduced torque ripple and increased driving range suitable for both traction and integrated charging applications. First, the required performance targets are determined using a drive cycle based vehicle dynamic model, existing literature and roadmaps for future EVs. An unconventional fractional–slot distributed winding configuration with a coil pitch of 2 is selected for analysis due to their short end–winding length, reduced winding losses and improved torque density. For the chosen baseline topology, a non–dominated sorting genetic algorithm based selection of optimal odd slot numbers is performed for higher torque production and reduced torque ripple. Further, for the selected odd slot–pole combination, a novel star–delta winding configuration is modeled and analyzed using winding function theory for higher torque density, reduced spatial harmonics, reduced torque ripple and machine losses. Thereafter, to analyze the motor performance with control and making critical decisions on inter–dependent design parameter variations for machine optimization, a parametric design approach using a novel coupled magnetic equivalent circuit model and thermal model incorporating current harmonics for fractional–slot wound PMSMs was developed and verified. The developed magnetic circuit model incorporates all machine non–linearities including effects of temperature and induced inverter harmonics as well as the space harmonics in the winding inductances of a fractional–slot winding configuration. Using the proposed model with a pareto ant colony optimization algorithm, an optimal rotor design is obtained to reduce the magnet utilization and obtain maximized torque density and extended operating range. Further, the developed machine structure is also analyzed and verified for integrated charging operation where the machine’s winding inductances are used as line inductors for charging the battery thereby eliminating the requirement of an on–board charger in the powertrain and hence resulting in reduced weight, cost and extended driving range. Finally, a scaled–down prototype of the proposed PMSM is developed and validated with experimental results in terms of machine inductances, torque ripple, torque–power–speed curves and efficiency maps over the operating speed range. Subsequently, understanding the capabilities and challenges of the developed scaled–down prototype, a full–scale design with commercial traction level ratings, will be developed and analyzed using finite element analysis. Further recommendations for design improvement, future work and analysis will also be summarized towards the end of the dissertation

Sensitivity Based Multiobjective Finite Element Model Calibration with the Results of Operational Modal Analysis

A new method is developed for finite element model calibration of structures with the results of modal testing. The proposed method applies multi-objective optimization to develop a set of calibrated models and employs sensitivity analysis to analyze and identify the most effective parameters for model calibration. The study consists of a full experimental study on modal identification of structures under ambient vibration conditions and an analytical study on finite element model calibration. The experimental study is focused on operational modal analysis of structures with covariance driven stochastic subspace identification in the time domain and frequency domain decomposition in the frequency domain. In the analytical part, the model calibration problem is defined as an optimization problem with the objective of minimizing the discrepancies between the modal frequencies and mode shapes identified from the test and estimated from the finite element model of the structure. Single objective and multi-objective formulations are developed for the model calibration problems and evolutionary algorithms are applied to solve these optimization problems. In order to reduce the complexity of the optimization problems and improve the quality of the calibration results, a variance-based sensitivity analysis is applied to examine the effectiveness of the updating parameters and remove unnecessary parameters from the calibration process.The effectiveness of the applied methodology is examined by conducting experimental and analytical studies on a three-span highway bridge on the Interstate 385 in Arlington, TN. Following the experimental study, a detailed finite element model of the bridge is developed and prepared for sensitivity analysis and model calibration with the proposed optimization techniques. A comparison of results is presented between the model calibration from single objective and multi-objective optimization. In addition, model calibration results with and without sensitivity analysis are presented to examine the effectiveness of this method. Finally, a comparison is presented of model calibrations for different cases

A review of asset management literature on multi-asset systems

This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options. Moreover, discerning differences in characteristics between cross-component and cross-asset interactions, we select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, we delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. We expect that the output of this review paper will not only stress research gaps on multi-asset systems, but more importantly help systematise future studies on this aspect

Finding the Optimal Balance between Over and Under Approximation of Models Inferred from Execution Logs

Models inferred from execution traces (logs) may admit more behaviours than those possible in the real system (over-approximation) or may exclude behaviours that can indeed occur in the real system (under-approximation). Both problems negatively affect model based testing. In fact, over-approximation results in infeasible test cases, i.e., test cases that cannot be activated by any input data. Under-approximation results in missing test cases, i.e., system behaviours that are not represented in the model are also never tested. In this paper we balance over- and under-approximation of inferred models by resorting to multi-objective optimization achieved by means of two search-based algorithms: A multi-objective Genetic Algorithm (GA) and the NSGA-II. We report the results on two open-source web applications and compare the multi-objective optimization to the state-of-the-art KLFA tool. We show that it is possible to identify regions in the Pareto front that contain models which violate fewer application constraints and have a higher bug detection ratio. The Pareto fronts generated by the multi-objective GA contain a region where models violate on average 2% of an application's constraints, compared to 2.8% for NSGA-II and 28.3% for the KLFA models. Similarly, it is possible to identify a region on the Pareto front where the multi-objective GA inferred models have an average bug detection ratio of 110: 3 and the NSGA-II inferred models have an average bug detection ratio of 101: 6. This compares to a bug detection ratio of 310928: 13 for the KLFA tool. © 2012 IEEE

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

Bi-objective optimization of the tactical allocation of job types to machines: mathematical modeling, theoretical analysis, and numerical tests

We introduce a tactical resource allocation model for a large aerospace engine system manufacturer aimed at long-term production planning. Our model identifies the routings a product takes through the factory, and which machines should be qualified for a balanced resource loading, to reduce product lead times. We prove some important mathematical properties of the model that are used to develop a heuristic providing a good initial feasible solution. We propose a tailored approach for our class of problems combining two well-known criterion space search algorithms, the bi-directional ε-constraint method and the augmented weighted Tchebycheff method. A computational investigation comparing solution times for several solution methods is presented for 60 numerical\ua0instances