Optimised search heuristics: combining metaheuristics and exact methods to solve scheduling problems

Tese dout., Matemática, Investigação Operacional, Universidade do Algarve, 2009

Development of new approaches for the synthesis and decoding of one-bead one-compound cyclic peptide libraries

La plupart des processus cellulaires et biologiques reposent, à un certain niveau, sur des interactions protéine-protéine (IPP). Leur manipulation avec des composés chimiques démontre un grand potentiel pour la découverte de nouveaux médicaments. Malgré la demande toujours croissante en molécules capables d’interrompre sélectivement des IPP, le développement d’inhibiteurs d’IPP est fortement limité par la grande taille de la surface d’interaction. En considérant la nature de cette surface, la capacité à mimer des structures secondaires de protéines est très importante pour lier une protéine et inhiber une IPP. Avec leurs grandes capacités peptidomimétiques et leurs propriétés pharmacologiques intéressan-tes, les peptides cycliques sont des prototypes moléculaires de choix pour découvrir des ligands de protéines et développer de nouveaux inhibiteurs d’IPP. Afin d’exploiter pleinement la grande diversité accessible avec les peptides cycliques, l’approche combinatoire «one-bead-one-compound» (OBOC) est l’approche la plus accessible et puissante. Cependant, l’utilisation des peptides cycliques dans les chimiothèques OBOC est limitée par les difficultés à séquencer les composés actifs après le criblage. Sans amine libre en N-terminal, la dégradation d’Edman et la spectrométrie de masse en tandem (MS/MS) ne peuvent pas être utilisées. À cet égard, nous avons développé de nouvelles approches par ouverture de cycle pour préparer et décoder des chimiothèques OBOC de peptides cycliques. Notre stratégie était d’introduire un résidu sensible dans le macrocycle et comme ancrage pour permettre la linéarisation des peptides et leur largage des billes pour le séquençage par MS/MS. Tout d’abord, des résidus sensibles aux nucléophiles, aux ultraviolets ou au bromure de cyanogène ont été introduits dans un peptide cyclique et leurs rendements de clivage évalués. Ensuite, les résidus les plus prometteurs ont été utilisés dans la conception et le développement d’approches en tandem ouverture de cycle / clivage pour le décodage de chimiothèques OBOC de peptides cycliques. Dans la première approche, une méthionine a été introduite dans le macrocycle comme ancrage pour simultanément permettre l’ouverture du cycle et le clivage des billes par traitement au bromure de cyanogène. Dans la seconde approche, un résidu photosensible a été utilisé dans le macrocycle comme ancrage pour permettre l’ouverture du cycle et le clivage suite à une irradiation aux ultraviolets. Le peptide linéaire généré par ces approches peut alors être efficacement séquencé par MS/MS. Enfin, une chimiothèque OBOC a été préparée et criblée la protéine HIV-1 Nef pour identifier des ligands sélectifs. Le développement de ces méthodologies permttra l’utilisation de composés macrocycliques dans les chimiothèques OBOC et constitue une contribution importante en chimie médicinale pour la découverte de ligands de protéines et le développement d’inhibiteurs d’IPP.

Desenvolvimento de ferramentas de apoio multicritério à decisão em problemas de localização

Tese de doutoramento, Estatística e Investigação Operacional (Análise de Sistemas), Universidade de Lisboa, Faculdade de Ciências, 2014

Developing Low Fouling Peptides and Zwitterionic Materials for Biomedical Applications

Thesis (Ph.D.)--University of Washington, 2015

Simulated annealing metaheuristic to solve the optimal power flow

The optimal power flow problem has been widely studied in order to improve power systems operation and planning. For real power systems, the problem is formulated as a non-linear and as a large combinatorial problem. The first approaches used to solve this problem were based on mathematical methods which required huge computational efforts. Lately, artificial intelligence techniques, such as metaheuristics based on biological processes, were adopted. Metaheuristics require lower computational resources, which is a clear advantage for addressing the problem in large power systems. This paper proposes a methodology to solve optimal power flow on economic dispatch context using a Simulated Annealing algorithm inspired on the cooling temperature process seen in metallurgy. The main contribution of the proposed method is the specific neighborhood generation according to the optimal power flow problem characteristics. The proposed methodology has been tested with IEEE 6 bus and 30 bus networks. The obtained results are compared with other wellknown methodologies presented in the literature, showing the effectiveness of the proposed method.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

A new MILP based approach for unit commitment in power production planning

This paper presents a complete, quadratic programming formulation of the standard thermal unit commitment problem in power generation planning, together with a novel iterative optimisation algorithm for its solution. The algorithm, based on a mixed-integer formulation of the problem, considers piecewise linear approximations of the quadratic fuel cost function that are dynamically updated in an iterative way, converging to the optimum; this avoids the requirement of resorting to quadratic programming, making the solution process much quicker. From extensive computational tests on a broad set of benchmark instances of this problem, the algorithm was found to be flexible and capable of easily incorporating different problem constraints. Indeed, it is able to tackle ramp constraints, which although very important in practice were rarely considered in previous publications. Most importantly, optimal solutions were obtained for several well-known benchmark instances, including instances of practical relevance, that are not yet known to have been solved to optimality. Computational experiments and their results showed that the method proposed is both simple and extremely effective.

New insights on integer-programming models for the kidney exchange problem

In recent years several countries have set up policies that allow exchange of kidneys between two or more incompatible patient–donor pairs. These policies lead to what is commonly known as kidney exchange programs. The underlying optimization problems can be formulated as integer programming models. Previously proposed models for kidney exchange programs have exponential numbers of constraints or variables, which makes them fairly difficult to solve when the problem size is large. In this work we propose two compact formulations for the problem, explain how these formulations can be adapted to address some problem variants, and provide results on the dominance of some models over others. Finally we present a systematic comparison between our models and two previously proposed ones via thorough computational analysis. Results show that compact formulations have advantages over non-compact ones when the problem size is large.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

A physical packing sequence algorithm for the container loading problem with static mechanical equilibrium conditions

The container loading problem (CLP) is a combinatorial optimization problem for the spatial arrangement of cargo inside containers so as to maximize the usage of space. The algorithms for this problem are of limited practical applicability if real-world constraints are not considered, one of the most important of which is deemed to be stability. This paper addresses static stability, as opposed to dynamic stability, looking at the stability of the cargo during container loading. This paper proposes two algorithms. The first is a static stability algorithm based on static mechanical equilibrium conditions that can be used as a stability evaluation function embedded in CLP algorithms (e.g. constructive heuristics, metaheuristics). The second proposed algorithm is a physical packing sequence algorithm that, given a container loading arrangement, generates the actual sequence by which each box is placed inside the container, considering static stability and loading operation efficiency constraints.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Variant ionotropic glutamate receptors as chemosensory receptors in Drosophila.

Ionotropic glutamate receptors (iGluRs) mediate neuronal communication at synapses throughout vertebrate and invertebrate nervous systems. We have characterized a family of iGluR-related genes in Drosophila, which we name ionotropic receptors (IRs). These receptors do not belong to the well-described kainate, AMPA, or NMDA classes of iGluRs, and they have divergent ligand-binding domains that lack their characteristic glutamate-interacting residues. IRs are expressed in a combinatorial fashion in sensory neurons that respond to many distinct odors but do not express either insect odorant receptors (ORs) or gustatory receptors (GRs). IR proteins accumulate in sensory dendrites and not at synapses. Misexpression of IRs in different olfactory neurons is sufficient to confer ectopic odor responsiveness. Together, these results lead us to propose that the IRs comprise a novel family of chemosensory receptors. Conservation of IR/iGluR-related proteins in bacteria, plants, and animals suggests that this receptor family represents an evolutionarily ancient mechanism for sensing both internal and external chemical cues.

Potential approaches for more successful dendritic cell-based immunotherapy.

INTRODUCTION: Dendritic cells (DCs) are the most important antigen-presenting cell population for activating antitumor T-cell responses; therefore, they offer a unique opportunity for specific targeting of tumors. AREAS COVERED: We will discuss the critical factors for the enhancement of DC vaccine efficacy: different DC subsets, types of in vitro DC manufacturing protocol, types of tumor antigen to be loaded and finally different adjuvants for activating them. We will cover potential combinatorial strategies with immunomodulatory therapies: depleting T-regulatory (Treg) cells, blocking VEGF and blocking inhibitory signals. Furthermore, recommendations to incorporate these criteria into DC-based tumor immunotherapy will be suggested. EXPERT OPINION: Monocyte-derived DCs are the most widely used DC subset in the clinic, whereas Langerhans cells and plasmacytoid DCs are two emerging DC subsets that are highly effective in eliciting cytotoxic T lymphocyte responses. Depending on the type of tumor antigens selected for loading DCs, it is important to optimize a protocol that will generate highly potent DCs. The future aim of DC-based immunotherapy is to combine it with one or more immunomodulatory therapies, for example, Treg cell depletion, VEGF blockage and T-cell checkpoint blockage, to elicit the most optimal antitumor immunity to induce long-term remission or even cure cancer patients.

Particle swarm optimization for two-connected networks with bounded rings

The Two-Connected Network with Bounded Ring (2CNBR) problem is a network design problem addressing the connection of servers to create a survivable network with limited redirections in the event of failures. Particle Swarm Optimization (PSO) is a stochastic population-based optimization technique modeled on the social behaviour of flocking birds or schooling fish. This thesis applies PSO to the 2CNBR problem. As PSO is originally designed to handle a continuous solution space, modification of the algorithm was necessary in order to adapt it for such a highly constrained discrete combinatorial optimization problem. Presented are an indirect transcription scheme for applying PSO to such discrete optimization problems and an oscillating mechanism for averting stagnation.