Parallel Problem Solving from Nature – PPSN XIV:14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings

This book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016. The total of 93 revised full papers were carefully reviewed and selected from 224 submissions. The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization. PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis.

Optimization of Cooling Protocols for Hearts Destined for Transplantation

Design and analysis of conceptually different cooling systems for the human heart preservation are numerically investigated. A heart cooling container with required connections was designed for a normal size human heart. A three-dimensional, high resolution human heart geometric model obtained from CT-angio data was used for simulations. Nine different cooling designs are introduced in this research. The first cooling design (Case 1) used a cooling gelatin only outside of the heart. In the second cooling design (Case 2), the internal parts of the heart were cooled via pumping a cooling liquid inside both the heart’s pulmonary and systemic circulation systems. An unsteady conjugate heat transfer analysis is performed to simulate the temperature field variations within the heart during the cooling process. Case 3 simulated the currently used cooling method in which the coolant is stagnant. Case 4 was a combination of Case 1 and Case 2. A linear thermoelasticity analysis was performed to assess the stresses applied on the heart during the cooling process. In Cases 5 through 9, the coolant solution was used for both internal and external cooling. For external circulation in Case 5 and Case 6, two inlets and two outlets were designed on the walls of the cooling container. Case 5 used laminar flows for coolant circulations inside and outside of the heart. Effects of turbulent flow on cooling of the heart were studied in Case 6. In Case 7, an additional inlet was designed on the cooling container wall to create a jet impinging the hot region of the heart’s wall. Unsteady periodic inlet velocities were applied in Case 8 and Case 9. The average temperature of the heart in Case 5 was +5.0oC after 1500 s of cooling. Multi-objective constrained optimization was performed for Case 5. Inlet velocities for two internal and one external coolant circulations were the three design variables for optimization. Minimizing the average temperature of the heart, wall shear stress and total volumetric flow rates were the three objectives. The only constraint was to keep von Mises stress below the ultimate tensile stress of the heart’s tissue.

Effective end wall profiling rules for a highly loaded compressor cascade

This paper presents a numerical study of a linear compressor cascade to investigate the effective end wall profiling rules for highly-loaded axial compressors. The first step in the research applies a correlation analysis for the different flow field parameters by a data mining over 600 profiling samples to quantify how variations of loss, secondary flow and passage vortex interact with each other under the influence of a profiled end wall. The result identifies the dominant role of corner separation for control of total pressure loss, providing a principle that only in the flow field with serious corner separation does the does the profiled end wall change total pressure loss, secondary flow and passage vortex in the same direction. Then in the second step, a multi-objective optimization of a profiled end wall is performed to reduce loss at design point and near stall point. The development of effective end wall profiling rules is based on the manner of secondary flow control rather than the geometry features of the end wall. Using the optimum end wall cases from the Pareto front, a quantitative tool for analyzing secondary flow control is employed. The driving force induced by a profiled end wall on different regions of end wall flow are subjected to a detailed analysis and identified for their positive/negative influences in relieving corner separation, from which the effective profiling rules are further confirmed. It is found that the profiling rules on a cascade show distinct differences at design point and near stall point, thus loss control of different operating points is generally independent.

Contribution à la planification tactique et opérationnelle du transport forestier

Cette thèse est une contribution à la modélisation, la planification et l’optimisation du transport pour l’approvisionnement en bois de forêt des industries de première transformation. Dans ce domaine, les aléas climatiques (mise au sol des bois par les tempêtes), sanitaires (attaques bactériologiques et fongiques des bois) et commerciaux (variabilité et exigence croissante des marchés) poussent les divers acteurs du secteur (entrepreneurs et exploitants forestiers, transporteurs) à revoir l’organisation de la filière logistique d’approvisionnement, afin d’améliorer la qualité de service (adéquation offre-demande) et de diminuer les coûts. L’objectif principal de cette thèse était de proposer un modèle de pilotage améliorant la performance du transport forestier, en respectant les contraintes et les pratiques du secteur. Les résultats établissent une démarche de planification hiérarchique des activités de transport à deux niveaux de décision, tactique et opérationnel. Au niveau tactique, une optimisation multi-périodes permet de répondre aux commandes en minimisant l’activité globale de transport, sous contrainte de capacité agrégée des moyens de transport accessibles. Ce niveau permet de mettre en œuvre des politiques de lissage de charge et d’organisation de sous-traitance ou de partenariats entre acteurs de transport. Au niveau opérationnel, les plans tactiques alloués à chaque transporteur sont désagrégés, pour permettre une optimisation des tournées des flottes, sous contrainte des capacités physiques de ces flottes. Les modèles d’optimisation de chaque niveau sont formalisés en programmation linéaire mixte avec variables binaires. L’applicabilité des modèles a été testée en utilisant un jeu de données industrielles en région Aquitaine et a montré des améliorations significatives d’exploitation des capacités de transport par rapport aux pratiques actuelles. Les modèles de décision ont été conçus pour s’adapter à tout contexte organisationnel, partenarial ou non : la production du plan tactique possède un caractère générique sans présomption de l’organisation, celle-ci étant prise en compte, dans un deuxième temps, au niveau de l’optimisation opérationnelle du plan de transport de chaque acteur.

Approximation limits of linear programs (beyond hierarchies)

We develop a framework for proving approximation limits of polynomial size linear programs (LPs) from lower bounds on the nonnegative ranks of suitably defined matrices. This framework yields unconditional impossibility results that are applicable to any LP as opposed to only programs generated by hierarchies. Using our framework, we prove that O(n^1/2-ε)-approximations for CLIQUE require LPs of size 2^nΩ(ε). This lower bound applies to LPs using a certain encoding of CLIQUE as a linear optimization problem. Moreover, we establish a similar result for approximations of semidefinite programs by LPs. Our main technical ingredient is a quantitative improvement of Razborov's [38] rectangle corruption lemma for the high error regime, which gives strong lower bounds on the nonnegative rank of shifts of the unique disjointness matrix.

Energy Management of a Battery-Ultracapacitor Hybrid Energy Storage System in Electric Vehicles

Electric vehicle (EV) batteries tend to have accelerated degradation due to high peak power and harsh charging/discharging cycles during acceleration and deceleration periods, particularly in urban driving conditions. An oversized energy storage system (ESS) can meet the high power demands; however, it suffers from increased size, volume and cost. In order to reduce the overall ESS size and extend battery cycle life, a battery-ultracapacitor (UC) hybrid energy storage system (HESS) has been considered as an alternative solution. In this work, we investigate the optimized configuration, design, and energy management of a battery-UC HESS. One of the major challenges in a HESS is to design an energy management controller for real-time implementation that can yield good power split performance. We present the methodologies and solutions to this problem in a battery-UC HESS with a DC-DC converter interfacing with the UC and the battery. In particular, a multi-objective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. This optimization problem is numerically solved for standard drive cycle datasets using Dynamic Programming (DP). Trained using the DP optimal results, an effective real-time implementation of the optimal power split is realized based on Neural Network (NN). This proposed online energy management controller is applied to a midsize EV model with a 360V/34kWh battery pack and a 270V/203Wh UC pack. The proposed online energy management controller effectively splits the load demand with high power efficiency and also effectively reduces the battery peak current. More importantly, a 38V-385Wh battery and a 16V-2.06Wh UC HESS hardware prototype and a real-time experiment platform has been developed. The real-time experiment results have successfully validated the real-time implementation feasibility and effectiveness of the real-time controller design for the battery-UC HESS. A battery State-of-Health (SoH) estimation model is developed as a performance metric to evaluate the battery cycle life extension effect. It is estimated that the proposed online energy management controller can extend the battery cycle life by over 60%.

Designing Efficient Subsidy Policies in a Regional Economy: A Multicriteria Decision-Making (MCDM)-Computable General Equilibrium (CGE) Approach

Since policy-makers usually pursue several conflicting objectives, policy-making can be understood as a multicriteria decision problem. Following the methodological proposal by André and Cardenete (2005) André, F. J. and Cardenete, M. A. 2005. Multicriteria Policy Making. Defining Efficient Policies in a General Equilibrium Model, Seville: Centro de Estudios Andaluces. Working Paper No. E2005/04, multi-objective programming is used in connection with a computable general equilibrium model to represent optimal policy-making and to obtain so-called efficient policies in an application to a regional economy (Andalusia, Spain). This approach is applied to the design of subsidy policies under two different scenarios. In the first scenario, it is assumed that the government is concerned just about two objectives: ensuring the profitability of a key strategic sector and increasing overall output. Finally, the scope of the exercise is enlarged by solving a problem with seven policy objectives, including both general and sectorial objectives. It is concluded that the observed policy could have been Pareto-improved in several directions.

Exact solutions for the agricultural and the two-dimensional packing problems

Objectives and study method: The objective of this study is to develop exact algorithms that can be used as management tools for the agricultural production planning and to obtain exact solutions for two of the most well known twodimensional packing problems: the strip packing problem and the bin packing problem. For the agricultural production planning problem we propose a new hierarchical scheme of three stages to improve the current agricultural practices. The objective of the first stage is to delineate rectangular and homogeneous management zones into the farmer’s plots considering the physical and chemical soil properties. This is an important task because the soil properties directly affect the agricultural production planning. The methodology for this stage is based on a new method called “Positions and Covering” that first generates all the possible positions in which the plot can be delineated. Then, we use a mathematical model of linear programming to obtain the optimal physical and chemical management zone delineation of the plot. In the second stage the objective is to determine the optimal crop pattern that maximizes the farmer’s profit taken into account the previous management zones delineation. In this case, the crop pattern is affected by both management zones delineation, physical and chemical. A mixed integer linear programming is used to solve this stage. The objective of the last stage is to determine in real-time the amount of water to irrigate in each crop. This stage takes as input the solution of the crop planning stage, the atmospheric conditions (temperature, radiation, etc.), the humidity level in plots, and the physical management zones of plots, just to name a few. This procedure is made in real-time during each irrigation period. A linear programming is used to solve this problem. A breakthrough happen when we realize that we could propose some adaptations of the P&C methodology to obtain optimal solutions for the two-dimensional packing problem and the strip packing. We empirically show that our methodologies are efficient on instances based on real data for both problems: agricultural and two-dimensional packing problems. Contributions and conclusions: The exact algorithms showed in this study can be used in the making-decision support for agricultural planning and twodimensional packing problems. For the agricultural planning problem, we show that the implementation of the new hierarchical approach can improve the farmer profit between 5.27% until 8.21% through the optimization of the natural resources. An important characteristic of this problem is that the soil properties (physical and chemical) and the real-time factors (climate, humidity level, evapotranspiration, etc.) are incorporated. With respect to the two-dimensional packing problems, one of the main contributions of this study is the fact that we have demonstrate that many of the best solutions founded in literature by others approaches (heuristics approaches) are the optimal solutions. This is very important because some of these solutions were up to now not guarantee to be the optimal solutions.

Efficient Hill Climber for Constrained Pseudo-Boolean Optimization Problems

Efficient hill climbers have been recently proposed for single- and multi-objective pseudo-Boolean optimization problems. For $k$-bounded pseudo-Boolean functions where each variable appears in at most a constant number of subfunctions, it has been theoretically proven that the neighborhood of a solution can be explored in constant time. These hill climbers, combined with a high-level exploration strategy, have shown to improve state of the art methods in experimental studies and open the door to the so-called Gray Box Optimization, where part, but not all, of the details of the objective functions are used to better explore the search space. One important limitation of all the previous proposals is that they can only be applied to unconstrained pseudo-Boolean optimization problems. In this work, we address the constrained case for multi-objective $k$-bounded pseudo-Boolean optimization problems. We find that adding constraints to the pseudo-Boolean problem has a linear computational cost in the hill climber.

Analisi multi-obiettivo in ottica lean-green della reverse logistics dei veicoli a fine vita (ELV)

La gestione del fine vita dei prodotti è un argomento di interesse attuale per le aziende; sempre più spesso l’imprese non possono più esimersi dall’implementare un efficiente sistema di Reverse Logistics. Per rispondere efficacemente a queste nuove esigenze diventa fondamentale ampliare i tradizionali sistemi logistici verso tutte quelle attività svolte all’interno della Reverse Logitics. Una gestione efficace ed efficiente dell’intera supply chain è un aspetto di primaria importanza per un’azienda ed incide notevolmente sulla sua competitività; proprio per perseguire questo obiettivo, sempre più aziende promuovono politiche di gestione delle supply chain sia Lean che Green. L’obiettivo di questo lavoro, nato dalle esigenze descritte sopra, è quello di applicare un modello innovativo che consideri sia politiche di gestione Lean, che dualmente politiche Green, alla gestione di una supply chain del settore automotive, comprendente anche le attività di gestione dei veicoli fuori uso (ELV). Si è analizzato per prima cosa i principi base e gli strumenti utilizzati per l’applicazione della Lean Production e del Green supply chain management e in seguito si è analizzato le caratteristiche distintive della Reverse Logistics e in particolare delle reti che trattano i veicoli a fine vita. L’obiettivo finale dello studio è quello di elaborare e implementare, tramite l’utilizzo del software AMPL, un modello di ottimizzazione multi-obiettivo (MOP- Multi Objective Optimization) Lean e Green a una Reverse Supply Chain dei veicoli a fine vita. I risultati ottenuti evidenziano che è possibile raggiungere un ottimo compromesso tra le due logiche. E' stata effettuata anche una valutazione economica dei risultati ottenuti, che ha evidenziato come il trade-off scelto rappresenti anche uno degli scenari con minor costi.

A generative design system based on evolutionary and mathematical functions

Previous work by Professor John Frazer on Evolutionary Architecture provides a basis for the development of a system evolving architectural envelopes in a generic and abstract manner. Recent research by the authors has focused on the implementation of a virtual environment for the automatic generation and exploration of complex forms and architectural envelopes based on solid modelling techniques and the integration of evolutionary algorithms, enhanced computational and mathematical models. Abstract data types are introduced for genotypes in a genetic algorithm order to develop complex models using generative and evolutionary computing techniques. Multi-objective optimisation techniques are employed for defining the fitness function in the evaluation process.

Heavy truck suspension optimization based on modified skyhook damping control

This paper presents a multi-objective optimization strategy for heavy truck suspension systems based on modified skyhook damping (MSD) control, which improves ride comfort and road-friendliness simultaneously. A four-axle heavy truck-road coupling system model was established using functional virtual prototype technology; the model was then validated through a ride comfort test. As the mechanical properties and time lag of dampers were taken into account, MSD control of active and semi-active dampers was implemented using Matlab/Simulink. Through co-simulations with Adams and Matlab, the effects of passive, semi-active MSD control, and active MSD control were analyzed and compared; thus, control parameters which afforded the best integrated performance were chosen. Simulation results indicated that MSD control improves a truck’s ride comfort and roadfriendliness, while the semi-active MSD control damper obtains road-friendliness comparable to the active MSD control damper.

Metaheuristic approaches to the hybrid flow shop scheduling problem with a cost-related criterion

In the paper, the flow-shop scheduling problem with parallel machines at each stage (machine center) is studied. For each job its release and due date as well as a processing time for its each operation are given. The scheduling criterion consists of three parts: the total weighted earliness, the total weighted tardiness and the total weighted waiting time. The criterion takes into account the costs of storing semi-manufactured products in the course of production and ready-made products as well as penalties for not meeting the deadlines stated in the conditions of the contract with customer. To solve the problem, three constructive algorithms and three metaheuristics (based one Tabu Search and Simulated Annealing techniques) are developed and experimentally analyzed. All the proposed algorithms operate on the notion of so-called operation processing order, i.e. the order of operations on each machine. We show that the problem of schedule construction on the base of a given operation processing order can be reduced to the linear programming task. We also propose some approximation algorithm for schedule construction and show the conditions of its optimality.

Optimal mission path planning (MPP) for an air sampling unmanned aerial system

This paper presents advanced optimization techniques for Mission Path Planning (MPP) of a UAS fitted with a spore trap to detect and monitor spores and plant pathogens. The UAV MPP aims to optimise the mission path planning search and monitoring of spores and plant pathogens that may allow the agricultural sector to be more competitive and more reliable. The UAV will be fitted with an air sampling or spore trap to detect and monitor spores and plant pathogens in remote areas not accessible to current stationary monitor methods. The optimal paths are computed using a Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimisers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and Hybrid Game are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The trajectories on a three-dimension terrain, which are generated off-line, are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of coupling a Hybrid-Game strategy to a MOEA for MPP tasks. The reduction of numerical cost is an important point as the faster the algorithm converges the better the algorithms is for an off-line design and for future on-line decisions of the UAV.