An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

A multi-objective model for a multi-period distribution management problem

The problems arising in commercial distribution are complex and involve several players and decision levels. One important decision is relatedwith the design of the routes to distribute the products, in an efficient and inexpensive way.This article deals with a complex vehicle routing problem that can beseen as a new extension of the basic vehicle routing problem. The proposed model is a multi-objective combinatorial optimization problemthat considers three objectives and multiple periods, which models in a closer way the real distribution problems. The first objective is costminimization, the second is balancing work levels and the third is amarketing objective. An application of the model on a small example, with5 clients and 3 days, is presented. The results of the model show the complexity of solving multi-objective combinatorial optimization problems and the contradiction between the several distribution management objective.

Inpatient diabetes care in a Swiss multi-site hospital: is there room for improvement?

Background and objective: Optimal care of diabetic patients (DPs) decreases the risk of complications. Close blood glucose monitoring can improve patient outcomes and shorten hospital stay. The objective of this pilot study was to evaluate the treatment of hospitalized DPs according to the current standards, including their diabetic treatment and drugs to prevent diabetes related complications [=guardian drugs: angiotensin converting enzyme inhibitors (ACEI) or Angiotensin II Receptor Blockers (ARB), antiplatelet drugs, statins]. Guidelines of the American Diabetes Association (ADA) [1] were used as reference as they were the most recent and exhaustive for hospital care. Design: Observational pilot study: analysis of the medical records of all DPs seen by the clinical pharmacists during medical rounds in different hospital units. An assessment was made by assigning points for fulfilling the different criteria according to ADA and then by dividing the total by the maximum achievable points (scale 0-1; 1 = all criteria fulfilled). Setting: Different Internal Medicine and Geriatric Units of the (multi-site) Ho^pital du Valais. Main outcome measures: - Completeness of diabetes-related information: type of diabetes, medical history, weight, albuminuria status, renal function, blood pressure, (recent) lipid profile. - Management of blood glucose: Hb1Ac, glycemic control, plan for treating hyper-/hypoglycaemia. - Presence of guardian drugs if indicated. Results: Medical records of 42 patients in 10 different units were analysed (18 women, 24 men, mean age 75.4 ± 11 years). 41 had type 2 diabetes. - Completeness of diabetes-related information: 0.8 ± 0.1. Information often missing: insulin-dependence (43%) and lipid profile (86%). - Management of blood glucose: 0.5 ± 0.2. 15 patients had suboptimal glycemic balance (target glycaemia 7.2-11.2 mmol/ l, with values[11.2 or\3.8 mmol/l, or Hb1Ac[7%), 10 patients had a deregulated balance (more than 10 values[11.2 mmol/l or \3.8 mmol/l and even values[15 mmol/l). - Presence of guardian drugs if indicated: ACEI/ARB: 19 of 23 patients (82.6%), statin: 16 of 40 patients (40%), antiplatelet drug: 16 of 39 patients (41%). Conclusions: Blood glucose control was insufficient in many DPs and prescription of statins and antiplatelet drugs was often missing. If confirmed by a larger study, these two points need to be optimised. As it is not always possible and appropriate to make those changes during hospital stay, a further project should assess and optimise diabetes care across both inpatient and outpatient settings.

Objective evaluation of shoulder function using body-fixed sensors: a new way to detect early treatment failures?

Background: Variable definitions of outcome (Constant score, Simple Shoulder Test [SST]) have been used to assess outcome after shoulder treatment, although none has been accepted as the universal standard. Physicians lack an objective method to reliably assess the activity of their patients in dynamic conditions. Our purpose was to clinically validate the shoulder kinematic scores given by a portable movement analysis device, using the activities of daily living described in the SST as a reference. The secondary objective was to determine whether this device could be used to document the effectiveness of shoulder treatments (for glenohumeral osteoarthritis and rotator cuff disease) and detect early failures.Methods: A clinical trial including 34 patients and a control group of 31 subjects over an observation period of 1 year was set up. Evaluations were made at baseline and 3, 6, and 12 months after surgery by 2 independent observers. Miniature sensors (3-dimensional gyroscopes and accelerometers) allowed kinematic scores to be computed. They were compared with the regular outcome scores: SST; Disabilities of the Arm, Shoulder and Hand; American Shoulder and Elbow Surgeons; and Constant.Results: Good to excellent correlations (0.61-0.80) were found between kinematics and clinical scores. Significant differences were found at each follow-up in comparison with the baseline status for all the kinematic scores (P < .015). The kinematic scores were able to point out abnormal patient outcomes at the first postoperative follow-up.Conclusion: Kinematic scores add information to the regular outcome tools. They offer an effective way to measure the functional performance of patients with shoulder pathology and have the potential to detect early treatment failures.Level of evidence: Level II, Development of Diagnostic Criteria, Diagnostic Study. (C) 2011 Journal of Shoulder and Elbow Surgery Board of Trustees.

Identifying strategies for mitigating the global warming impact of the EU-25 economy using a multi-objective input-output approach

Global warming mitigation has recently become a priority worldwide. A large body of literature dealing with energy related problems has focused on reducing greenhouse gases emissions at an engineering scale. In contrast, the minimization of climate change at a wider macroeconomic level has so far received much less attention. We investigate here the issue of how to mitigate global warming by performing changes in an economy. To this end, we make use of a systematic tool that combines three methods: linear programming, environmentally extended input output models, and life cycle assessment principles. The problem of identifying key economic sectors that contribute significantly to global warming is posed in mathematical terms as a bi criteria linear program that seeks to optimize simultaneously the total economic output and the total life cycle CO2 emissions. We have applied this approach to the European Union economy, finding that significant reductions in global warming potential can be attained by regulating specific economic sectors. Our tool is intended to aid policymakers in the design of more effective public policies for achieving the environmental and economic targets sought.

Generalized Differential Evolution for Global Multi-Objective Optimization with Constraints

The objective of this thesis work is to develop and study the Differential Evolution Algorithm for multi-objective optimization with constraints. Differential Evolution is an evolutionary algorithm that has gained in popularity because of its simplicity and good observed performance. Multi-objective evolutionary algorithms have become popular since they are able to produce a set of compromise solutions during the search process to approximate the Pareto-optimal front. The starting point for this thesis was an idea how Differential Evolution, with simple changes, could be extended for optimization with multiple constraints and objectives. This approach is implemented, experimentally studied, and further developed in the work. Development and study concentrates on the multi-objective optimization aspect. The main outcomes of the work are versions of a method called Generalized Differential Evolution. The versions aim to improve the performance of the method in multi-objective optimization. A diversity preservation technique that is effective and efficient compared to previous diversity preservation techniques is developed. The thesis also studies the influence of control parameters of Differential Evolution in multi-objective optimization. Proposals for initial control parameter value selection are given. Overall, the work contributes to the diversity preservation of solutions in multi-objective optimization.

A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.

Multi-Objective Genetic Algorithms for the Single Allocation Hub Location Problem

Hub Location Problems play vital economic roles in transportation and telecommunication networks where goods or people must be efficiently transferred from an origin to a destination point whilst direct origin-destination links are impractical. This work investigates the single allocation hub location problem, and proposes a genetic algorithm (GA) approach for it. The effectiveness of using a single-objective criterion measure for the problem is first explored. Next, a multi-objective GA employing various fitness evaluation strategies such as Pareto ranking, sum of ranks, and weighted sum strategies is presented. The effectiveness of the multi-objective GA is shown by comparison with an Integer Programming strategy, the only other multi-objective approach found in the literature for this problem. Lastly, two new crossover operators are proposed and an empirical study is done using small to large problem instances of the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets.

Les dérivées de l’hémoglobine dans la structure rétinienne par la technique de réflectométrie multi-spectrale

L’examen de la rétine par des moyens non invasifs et in vivo a été un objectif de recherche pendant plusieurs années. Pour l’œil comme pour tous les organes du corps humain, un apport soutenu en oxygène est nécessaire pour le maintien de l’homéostasie. La concentration en oxygène du sang des vaisseaux rétiniens peut être déterminée principalement à partir des mesures du spectre de réflexion du fond de l’œil. En envoyant une lumière, à différentes longueurs d’onde, sur la rétine et en analysant la nature de la lumière réfléchie par la rétine, il est possible d’obtenir des informations quantitatives sur le niveau d'oxygène dans les vaisseaux sanguins de la rétine ou sur le flux sanguin. Cependant, la modélisation est compliquée due aux différentes interactions et aux chemins que la lumière prend à travers les tissus oculaires avant de quitter l’œil. L’objectif de cette thèse a été de développer et de valider un modèle mathématique afin de calculer les dérivées d’hémoglobine à partir de mesures spectrales de réflectométrie sur les vaisseaux sanguins de la rétine. L’instrument utilisé pour mesurer la fonction spectrale de réflectométrie a été un spectroréflectomètre multi-canal, une technologie capable de mesurer in vivo et en continu 800 spectres simultanément. L'équation mathématique qui décrit la fonction spectrale de réflectométrie dans la zone spectrale de 480 nm à 650 nm a été exprimée comme la combinaison linéaire de plusieurs termes représentant les signatures spectrales de l'hémoglobine SHb, de l'oxyhémoglobine SOHB, l’absorption et la diffusion des milieux oculaires et une famille de fonctions multigaussiennes utilisées pour compenser l’incompatibilité du modèle et les données expérimentales dans la zone rouge du spectre. Les résultats du modèle révèlent que le signal spectral obtenu à partir de mesures de réflectométrie dans l’œil est complexe, contenant la lumière absorbée, réfléchie et diffusée, mais chacun avec une certaine prédominance spécifique en fonction de la zone spectrale. La fonction spectrale d’absorption du sang est dominante dans la zone spectrale 520 à 580 nm, tandis que dans la zone spectrale de longueurs d’ondes plus grandes que 590 nm, la diffusion sur les cellules rouges du sang est dominante. Le modèle a été utilisé afin de mesurer la concentration d’oxygène dans les capillaires de la tête du nerf optique suite à un effort physique dynamique. L’effort physique a entraîné une réduction de la concentration d’oxygène dans les capillaires, ainsi qu’une réduction de la pression intraoculaire, tandis que la saturation sanguine en oxygène, mesurée au niveau du doigt, restait constante. Le modèle mathématique développé dans ce projet a ainsi permis, avec la technique novatrice de spectroréflectométrie multicanal, de déterminer in vivo et d’une manière non invasive l’oxygénation sanguine des vaisseaux rétiniens.

A Mono- and Multi-objective Approach for Recommending Software Refactoring

Les systèmes logiciels sont devenus de plus en plus répondus et importants dans notre société. Ainsi, il y a un besoin constant de logiciels de haute qualité. Pour améliorer la qualité de logiciels, l’une des techniques les plus utilisées est le refactoring qui sert à améliorer la structure d'un programme tout en préservant son comportement externe. Le refactoring promet, s'il est appliqué convenablement, à améliorer la compréhensibilité, la maintenabilité et l'extensibilité du logiciel tout en améliorant la productivité des programmeurs. En général, le refactoring pourra s’appliquer au niveau de spécification, conception ou code. Cette thèse porte sur l'automatisation de processus de recommandation de refactoring, au niveau code, s’appliquant en deux étapes principales: 1) la détection des fragments de code qui devraient être améliorés (e.g., les défauts de conception), et 2) l'identification des solutions de refactoring à appliquer. Pour la première étape, nous traduisons des régularités qui peuvent être trouvés dans des exemples de défauts de conception. Nous utilisons un algorithme génétique pour générer automatiquement des règles de détection à partir des exemples de défauts. Pour la deuxième étape, nous introduisons une approche se basant sur une recherche heuristique. Le processus consiste à trouver la séquence optimale d'opérations de refactoring permettant d'améliorer la qualité du logiciel en minimisant le nombre de défauts tout en priorisant les instances les plus critiques. De plus, nous explorons d'autres objectifs à optimiser: le nombre de changements requis pour appliquer la solution de refactoring, la préservation de la sémantique, et la consistance avec l’historique de changements. Ainsi, réduire le nombre de changements permets de garder autant que possible avec la conception initiale. La préservation de la sémantique assure que le programme restructuré est sémantiquement cohérent. De plus, nous utilisons l'historique de changement pour suggérer de nouveaux refactorings dans des contextes similaires. En outre, nous introduisons une approche multi-objective pour améliorer les attributs de qualité du logiciel (la flexibilité, la maintenabilité, etc.), fixer les « mauvaises » pratiques de conception (défauts de conception), tout en introduisant les « bonnes » pratiques de conception (patrons de conception).

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

A Multi-Objective Antenna Placement Genetic Algorithm for Matched Array Synthesis on Complex Platforms

A Multi-Objective Antenna Placement Genetic Algorithm (MO-APGA) has been proposed for the synthesis of matched antenna arrays on complex platforms. The total number of antennas required, their position on the platform, location of loads, loading circuit parameters, decoupling and matching network topology, matching network parameters and feed network parameters are optimized simultaneously. The optimization goal was to provide a given minimum gain, specific gain discrimination between the main and back lobes and broadband performance. This algorithm is developed based on the non-dominated sorting genetic algorithm (NSGA-II) and Minimum Spanning Tree (MST) technique for producing diverse solutions when the number of objectives is increased beyond two. The proposed method is validated through the design of a wideband airborne SAR