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.

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.

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.

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.

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-level models and infrastructures for simulating biological system development

The hierarchical organisation of biological systems plays a crucial role in the pattern formation of gene expression resulting from the morphogenetic processes, where autonomous internal dynamics of cells, as well as cell-to-cell interactions through membranes, are responsible for the emergent peculiar structures of the individual phenotype. Being able to reproduce the systems dynamics at different levels of such a hierarchy might be very useful for studying such a complex phenomenon of self-organisation. The idea is to model the phenomenon in terms of a large and dynamic network of compartments, where the interplay between inter-compartment and intra-compartment events determines the emergent behaviour resulting in the formation of spatial patterns. According to these premises the thesis proposes a review of the different approaches already developed in modelling developmental biology problems, as well as the main models and infrastructures available in literature for modelling biological systems, analysing their capabilities in tackling multi-compartment / multi-level models. The thesis then introduces a practical framework, MS-BioNET, for modelling and simulating these scenarios exploiting the potential of multi-level dynamics. This is based on (i) a computational model featuring networks of compartments and an enhanced model of chemical reaction addressing molecule transfer, (ii) a logic-oriented language to flexibly specify complex simulation scenarios, and (iii) a simulation engine based on the many-species/many-channels optimised version of Gillespie’s direct method. The thesis finally proposes the adoption of the agent-based model as an approach capable of capture multi-level dynamics. To overcome the problem of parameter tuning in the model, the simulators are supplied with a module for parameter optimisation. The task is defined as an optimisation problem over the parameter space in which the objective function to be minimised is the distance between the output of the simulator and a target one. The problem is tackled with a metaheuristic algorithm. As an example of application of the MS-BioNET framework and of the agent-based model, a model of the first stages of Drosophila Melanogaster development is realised. The model goal is to generate the early spatial pattern of gap gene expression. The correctness of the models is shown comparing the simulation results with real data of gene expression with spatial and temporal resolution, acquired in free on-line sources.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Design and Multi-Objective Optimization of EMI Input Filters

An EMI filter design procedure for power converters is proposed. Based on a given noise spectrum, information about the converter noise source impedance and design constraints, the design space of the input filter is defined. The design is based on component databases and detailed models of the filter components, including high frequency parasitics, losses, weight, volume, etc.. The design space is mapped onto a performance space in which different filter implementations are evaluated and compared. A multi-objective optimization approach is used to obtain optimal designs w.r.t. a given performance function.

A 3D multi-objective optimization planning algorithm for wireless sensor networks

The complexity of planning a wireless sensor network is dependent on the aspects of optimization and on the application requirements. Even though Murphy's Law is applied everywhere in reality, a good planning algorithm will assist the designers to be aware of the short plates of their design and to improve them before the problems being exposed at the real deployment. A 3D multi-objective planning algorithm is proposed in this paper to provide solutions on the locations of nodes and their properties. It employs a developed ray-tracing scheme for sensing signal and radio propagation modelling. Therefore it is sensitive to the obstacles and makes the models of sensing coverage and link quality more practical compared with other heuristics that use ideal unit-disk models. The proposed algorithm aims at reaching an overall optimization on hardware cost, coverage, link quality and lifetime. Thus each of those metrics are modelled and normalized to compose a desirability function. Evolutionary algorithm is designed to efficiently tackle this NP-hard multi-objective optimization problem. The proposed algorithm is applicable for both indoor and outdoor 3D scenarios. Different parameters that affect the performance are analyzed through extensive experiments; two state-of-the-art algorithms are rebuilt and tested with the same configuration as that of the proposed algorithm. The results indicate that the proposed algorithm converges efficiently within 600 iterations and performs better than the compared heuristics.

Hybrid multi-objective optimization and hybridized self-organizing response surface method

Numerical optimization is a technique where a computer is used to explore design parameter combinations to find extremes in performance factors. In multi-objective optimization several performance factors can be optimized simultaneously. The solution to multi-objective optimization problems is not a single design, but a family of optimized designs referred to as the Pareto frontier. The Pareto frontier is a trade-off curve in the objective function space composed of solutions where performance in one objective function is traded for performance in others. A Multi-Objective Hybridized Optimizer (MOHO) was created for the purpose of solving multi-objective optimization problems by utilizing a set of constituent optimization algorithms. MOHO tracks the progress of the Pareto frontier approximation development and automatically switches amongst those constituent evolutionary optimization algorithms to speed the formation of an accurate Pareto frontier approximation. Aerodynamic shape optimization is one of the oldest applications of numerical optimization. MOHO was used to perform shape optimization on a 0.5-inch ballistic penetrator traveling at Mach number 2.5. Two objectives were simultaneously optimized: minimize aerodynamic drag and maximize penetrator volume. This problem was solved twice. The first time the problem was solved by using Modified Newton Impact Theory (MNIT) to determine the pressure drag on the penetrator. In the second solution, a Parabolized Navier-Stokes (PNS) solver that includes viscosity was used to evaluate the drag on the penetrator. The studies show the difference in the optimized penetrator shapes when viscosity is absent and present in the optimization. In modern optimization problems, objective function evaluations may require many hours on a computer cluster to perform these types of analysis. One solution is to create a response surface that models the behavior of the objective function. Once enough data about the behavior of the objective function has been collected, a response surface can be used to represent the actual objective function in the optimization process. The Hybrid Self-Organizing Response Surface Method (HYBSORSM) algorithm was developed and used to make response surfaces of objective functions. HYBSORSM was evaluated using a suite of 295 non-linear functions. These functions involve from 2 to 100 variables demonstrating robustness and accuracy of HYBSORSM.

Multi-objective optimization of the molecular structure of refrigerants

The aim of this work was to develop a new methodology, which can be used to design new refrigerants that are better than the currently used refrigerants. The methodology draws some parallels with the general approach of computer aided molecular design. However, the mathematical way of representing the molecular structure of an organic compound and the use of meta models during the optimization process make it different. In essence, this approach aimed to generate molecules that conform to various property requirements that are known and specified a priori. A modified way of mathematically representing the molecular structure of an organic compound having up to four carbon atoms, along with atoms of other elements such as hydrogen, oxygen, fluorine, chlorine and bromine, was developed. The normal boiling temperature, enthalpy of vaporization, vapor pressure, tropospheric lifetime and biodegradability of 295 different organic compounds, were collected from open literature and data bases or estimated. Surrogate models linking the previously mentioned quantities with the molecular structure were developed. Constraints ensuring the generation of structurally feasible molecules were formulated and used in commercially available optimization algorithms to generate molecular structures of promising new refrigerants. This study was intended to serve as a proof-of-concept of designing refrigerants using the newly developed methodology.

Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning

Recent advances in energy technology generation and new directions in electricity regulation have made distributed generation (DG) more widespread, with consequent significant impacts on the operational characteristics of distribution networks. For this reason, new methods for identifying such impacts are needed, together with research and development of new tools and resources to maintain and facilitate continued expansion towards DG. This paper presents a study aimed at determining appropriate DG sites for distribution systems. The main considerations which determine DG sites are also presented, together with an account of the advantages gained from correct DG placement. The paper intends to define some quantitative and qualitative parameters evaluated by Digsilent (R), GARP3 (R) and DSA-GD software. A multi-objective approach based on the Bellman-Zadeh algorithm and fuzzy logic is used to determine appropriate DG sites. The study also aims to find acceptable DG locations both for distribution system feeders, as well as for nodes inside a given feeder. (C) 2010 Elsevier Ltd. All rights reserved.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies. [DOI:10.1115/1.4002740]

Measuring fund performance using multi-factor models: evidence for the Portuguese market

This paper examines the performance of Portuguese equity funds investing in the domestic and in the European Union market, using several unconditional and conditional multi-factor models. In terms of overall performance, we find that National funds are neutral performers, while European Union funds under-perform the market significantly. These results do not seem to be a consequence of management fees. Overall, our findings are supportive of the robustness of conditional multi-factor models. In fact, Portuguese equity funds seem to be relatively more exposed to smallcaps and more value-oriented. Also, they present strong evidence of time-varying betas and, in the case of the European Union funds, of time-varying alphas too. Finally, in terms of market timing, our tests suggest that mutual fund managers in our sample do not exhibit any market timing abilities. Nevertheless, we find some evidence of timevarying conditional market timing abilities but only at the individual fund level.

Entropy diversity in multi-objective particle swarm optimization

Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyzethe MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.