Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems

Multi-objective swarm intelligence-based (MOSI-based) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) with conflicting objectives. Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. The reference point is determined by the decision maker to guide the search process to a particular region in the true Pareto front. However, HHMO algorithm produces a poor approximation to the Pareto front because lack of information sharing in its population update strategy, equal division of convergence parameter and randomly generated initial population. A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. The algorithm includes (i) a population update strategy which improves the movement of hawks in the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. The population update strategy calculates a new position of hawks based on the flush-and-ambush technique of Harris’s hawks, and selects the best hawks based on the non-dominated sorting approach. The adjustment strategy enables the parameter to adaptively changed based on the state of the search space. The initial population is produced by generating quasi-random numbers using Rsequence followed by adapting the partial opposition-based learning concept to improve the diversity of the worst half in the population of hawks. The performance of the 2S-ENDSHHMO has been evaluated using 12 MOPs and three engineering MOPs. The obtained results were compared with the results of eight state-of-the-art multi-objective optimization algorithms. The 2S-ENDSHHMO algorithm was able to generate non-dominated solutions with greater convergence and diversity in solving most MOPs and showed a great ability in jumping out of local optima. This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing

Performance assessment of Surrogate model integrated with sensitivity analysis in multi-objective optimization

This Thesis develops a new multi-objective heuristic algorithm. The optimum searching task is performed by a standard genetic algorithm. Furthermore, it is assisted by the Response Surface Methodology surrogate model and by two sensitivity analysis methods: the Variance-based, also known as Sobol’ analysis, and the Elementary Effects. Once built the entire method, it is compared on several multi-objective problems with some other algorithms

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects

Optimization of nonlinear function with planar regions using supernova

Nowadays, optimization is begun to be use in different fields, e.g. preference algorithms. These new challenges need a robustness meta heuristics to solve them. Supernova meta heuristic that emules the descent behavior of the gradients and share the same weakness of them. They get stuck planar regions and hardly find the needle minimum. The main objective of this works is to improve the performance of the original version of supernova for the problematic topologies mention above. First, a review of how to these problems are solved in the literature is presented. Second, A criterion to determine planar regions is described . Third, a strategy to choose the parameters agree with the topology of the function is implemented. Supernova 2.0 was tested using the set of benchmarks functions proposed in CEC2013. The new version is significantly better than the original version, no significantly better than SPSO2011 and significantly inferior with SADE. Although, the results are applied to Supernova, most of the strategies can be applied to other methods.Doctorad

Theoretical analysis and preference modelling for the valuation of ecosystem services from native pollinators in selected Thai rural communities

Until now, the existing microeconomic models concerned with pollination markets have not accommodated the global diversity of beekeeper-farmer interactions. The most prominent of such theoretical models is dedicated to describing the determinants of colony stocking densities and of equilibrium wages that farmers have paid to commercial beekeepers for decades in the highly bee-pollination reliant almond monocultures of California. This cumulative dissertation generalizes this basic model by taking into account the marginal productivity of a given agro-ecosystems wild bees and the opportunity costs that farmers incur when assigning labor time to beekeeping. In that regard, we assessed the economic potential of on-farm beekeeping, which can involve several bee species, by juxtaposing this activitys net benefits against those from hiring commercial pollination services. In addition to serving as a classification tool for a plurality of farmer-beekeeper-nature interactions and related optimization problems, the resulting analytical framework helps identifying the institutional settings that are most likely to lead to a specific bioeconomic equilibrium supply of pollination. What is more, it illustrates the interplay of the pertinent economic and agro-ecological factors, thus assisting the postulation of empirically testable hypotheses. We also conducted two separate discrete choice experiments (DCEs) with orchardists from the Thai provinces of Chiang Mai (N = 198 respondents) and Chanthaburi (N = 127), in order to elicit their preferences for changes in the population of local wild bees that would hypothetically result from a conservation policy consisting (along with a per-household implementation fee) of at least one of the following three measures: (i) offering farmers bee-friendly alternatives to conventional agro-chemicals, (ii) enabling the protection and/or rehabilitation of natural bee habitats near cropland, and (iii) fostering the husbandry of native bee species by transferring technical knowledge on the practice of on-farm beekeeping. In this context, we fitted random parameter logit models on the Chiang Mai dataset. They yielded a significant willingness to pay (WTP) for the presented conservation measures and suggested that the disutility the respondents perceived for a 50% decline in the local population of native bees was greater than the utility they would derive from experiencing a bee population increase of the same magnitude. Moreover, comparing our aggregated WTP estimates to the expected production losses, showed that orchard farmers underestimated the true use value of pollination. On the other hand, the average WTP for all conservation measures combined by far exceeded the costs that, according to our calculations, each household would incur for such a project to be implemented. Our models also indicated a significant preference heterogeneity in the sampled population, which we could partly explain with idiosyncratic variables such as the respondents attitudes towards native bees and beekeeping. Finally, we examined further sources of randomness in the observed choice behavior, by modelling the unknown choice decision-relevant influences that could not be captured during the DCEs. To that end, we fitted generalized mixed logit (GMXL) models on the pooled datasets, which allowed comparing, on a common utility scale, the part-worth (value) estimates from Chiang Mai and Chanthaburi, where different experimental designs were applied. Our results reveal that farmers in Chanthaburi, who reported having experienced crop declines that they attributed to insufficient pollination, introduced less subjective factors into their choices than their Chiang Mai counterparts, who may have been less familiar with the importance of conserving bees. Moreover, the GMXL results also suggest that Chanthaburi farmers placed a significantly higher value on the above-mentioned measures (i) and (ii), while caring comparatively less about a 50% decline in local wild bee colonies. One can thus hypothesize that an actual local pollinator decline may have made Chanthaburi farmers more aware of the importance of conserving native bees, while paradoxically making them more independent from the provision of wild pollination services, as they started managing crop pollination with stingless bees.Bisher existierende mikroökonomische Modelle, die sich mit Bestäubungsmärkten befassen, berücksichtigen mögliche Interaktionen zwischen Imkern und Landwirten nur in beschränktem Umfang. Das bekannteste dieser theoretischen Modelle bezieht sich auf die hochgradig von der Bienenbestäubung abhängigen kalifornischen Mandel-Monokulturen, in denen seit Jahrzenten kommerzielle Imker für Bestäubungsleistungen entlohnt werden. Das Modell analysiert die Bestimmungsgründe für die Besatzdichten an Honigbienen sowie für die Gleichgewichtslöhne der Bestäubungsimker. In dieser kumulativen Dissertation wird dieses Modell erweitert, indem zusätzlich (1) die Grenzproduktivität von Wildbienen in einem Agrarökosystem sowie (2) die Opportunitätskosten derjenigen Landwirte, die sich selbst der Bienenhaltung widmen, berücksichtigt werden. In diesem Zusammenhang wurde das ökonomische Potenzial, das sich für Landwirte aus der eigenen Haltung (u. U. von verschiedenen Arten) von Bienen ergibt, explizit berücksichtigt und dessen Nettonutzen dem Nettonutzen aus zugekauften Bestäubungsleistungen gegenübergestellt. Der von uns entwickelte analytische Rahmen dient einerseits zur Klassifizierung der vielfältigen Beziehungen zwischen Landwirtschaft, Imkerei und Natur sowie den entsprechenden Optimierungsproblemen, und hilft andererseits bei der Identifizierung institutioneller Lösungen, die jeweils geeignet erscheinen, ein bioökonomisches Gleichgewicht für ein optimiertes Angebot an Bestäubungsleistungen herbeizuführen. Darüber hinaus stellt er das Zusammenspiel der relevanten agrarökologischen und ökonomischen Einflussfaktoren dar und ermöglicht damit, empirisch überprüfbare Hypothesen abzuleiten. Zusätzlich haben wir zwei voneinander unabhängige sogenannte Discrete Choice-Eperimente (DCE) mit Obstbauern in den thailändischen Provinzen Chiang Mai (N = 198 Teilnehmer) und Chanthaburi (N = 127) durchgeführt, um deren Präferenzen hinsichtlich der Veränderungen der örtlichen Wildbienenpopulation zu ermitteln. Diese Veränderungen können sich jeweils aus hypothetischen Kombinationen von Teilnahmegebühren und den folgenden Naturschutzmaßnahmen ergeben: (i) Angebot von bienenschonenden Alternativen zum konventionellen chemisch-synthetischen Pflanzenschutz, (ii) Ermöglichung von Schutz und/oder Wiederherstellung natürlicher Bienenhabitate auf den Plantagen sowie den angrenzenden Flächen, und (iii) Förderung der Haltung einheimischer Bienenarten auf den eigenen landwirtschaftlichen Flächen durch Wissenstransfer zur Bienenhaltung. In diesem Zusammenhang haben wir Random Parameter Logit-Modelle für das in Chiang Mai durchgeführte DCE geschätzt. Diese ergaben signifikante Zahlungsbereitschaften (WTP) der Befragten für die oben genannten Maßnahmen sowie einen dem 50%-igen Rückgang der Bienenpopulation beigemessenen negativen Nutzen, der dem Betrag nach größer war als der mit einer entsprechenden Populationszunahme einhergehende Nutzen. Ein Vergleich der aggregierten WTP-Schätzung mit den realistischerweise zu erwartenden Ertragseinbußen zeigte zudem, dass der wahre Wert des Bestäubungsnutzens von den befragten Obstbauern unterschätzt wird. Außerdem zeigten auf den Modellergebnissen beruhende Berechnungen, dass die durchschnittliche WTP für das Bündel aller drei Schutzmaßnahmen die für den einzelnen Haushalt kalkulierten Teilnahmekosten im Fall einer Projektumsetzung bei weitem übertreffen. Unsere Modelle haben darüber hinaus eine signifikante Präferenzheterogenität bei den Befragungsteilnehmern ergeben, die wir teilweise mit idiosynkratischen Variablen, wie z. B. der Einstellung der Befragten zu einheimischen Bienen und zur Imkerei, erklären konnten. Wir sind schließlich den möglichen Ursachen der Zufallskomponente des beobachteten Wahlverhaltens nachgegangen, indem wir die unbekannten entscheidungsrelevanten Einflüsse, die wir in unseren beiden DCE nicht abbilden konnten, modelliert haben. Zu diesem Zweck wurden sogenannte Generalized Mixed Logit (GMXL)-Modelle für den aus beiden Regionen zusammengefügten Datensatz geschätzt. Dies ermöglichte den Vergleich der Teilnutzenschätzer für Chiang Mai mit denen für Chanthaburi auf einer gemeinsamen Nutzenskala, obwohl in Chiang Mai ein anderes experimentelles Design verwendet wurde. Unsere Ergebnisse zeigen, dass die Landwirte in Chanthaburi, die bereits von Ertragseinbußen wegen (nach ihrer Ansicht) unzureichender Bestäubung berichteten, weniger subjektive Faktoren in ihre Wahlentscheidungen einfließen ließen als ihre Kollegen in Chiang Mai. Letztere scheinen insgesamt weniger mit der Bedeutung des Bienenschutzes vertraut gewesen zu sein. Außerdem legen die GMXL-Ergebnisse nahe, dass die Landwirte in Chanthaburi einen signifikant höheren Wert auf die obigen Maßnahmen (i) und (ii) legen, während sie dem 50%-igen Rückgang der örtlichen Wildbienenpopulation einen relativ geringeren Wert beimessen. Daher kann man die Hypothese vertreten, dass es dort in der Vergangenheit bereits zu einem Rückgang lokaler Bestäuber gekommen ist und die Landwirte in Chanthaburi sich deshalb der Bedeutung des Schutzes einheimischer Bienen stärker bewusst sind. Die in diesem Zusammenhang paradox anmutende geringere Gewichtung eines Populationsrückgangs mag sich dadurch erklären, dass die Landwirte bereits damit begonnen haben, die Bestäubung mit Hilfe stachelloser Bienen zu bewerkstelligen, was sie von den Bestäubungsleistungen wilder Bienen unabhängiger macht

Hydrodynamic study of a bow of a combatant hull

This thesis regards methods to study and improve the hydrodynamic performances of a ship. More specifically, the attention is focused on the ship's resistance and on modern methods used in the design process to reduce it and achieve the best design configuration. These are CFD analyses and optimization techniques. Each aspect related to this modern design process is described in detail. The original part of this thesis is the study and the optimization of the DTMB 5415 hydrodynamics

Towards a more efficient use of computational budget in large-scale black-box optimization

Evolutionary algorithms are general purpose optimizers that have been shown effective in solving a variety of challenging optimization problems. In contrast to mathematical programming models, evolutionary algorithms do not require derivative information and are still effective when the algebraic formula of the given problem is unavailable. Nevertheless, the rapid advances in science and technology have witnessed the emergence of more complex optimization problems than ever, which pose significant challenges to traditional optimization methods. The dimensionality of the search space of an optimization problem when the available computational budget is limited is one of the main contributors to its difficulty and complexity. This so-called curse of dimensionality can significantly affect the efficiency and effectiveness of optimization methods including evolutionary algorithms. This research aims to study two topics related to a more efficient use of computational budget in evolutionary algorithms when solving large-scale black-box optimization problems. More specifically, we study the role of population initializers in saving the computational resource, and computational budget allocation in cooperative coevolutionary algorithms. Consequently, this dissertation consists of two major parts, each of which relates to one of these research directions. In the first part, we review several population initialization techniques that have been used in evolutionary algorithms. Then, we categorize them from different perspectives. The contribution of each category to improving evolutionary algorithms in solving large-scale problems is measured. We also study the mutual effect of population size and initialization technique on the performance of evolutionary techniques when dealing with large-scale problems. Finally, assuming uniformity of initial population as a key contributor in saving a significant part of the computational budget, we investigate whether achieving a high-level of uniformity in high-dimensional spaces is feasible given the practical restriction in computational resources. In the second part of the thesis, we study the large-scale imbalanced problems. In many real world applications, a large problem may consist of subproblems with different degrees of difficulty and importance. In addition, the solution to each subproblem may contribute differently to the overall objective value of the final solution. When the computational budget is restricted, which is the case in many practical problems, investing the same portion of resources in optimizing each of these imbalanced subproblems is not the most efficient strategy. Therefore, we examine several ways to learn the contribution of each subproblem, and then, dynamically allocate the limited computational resources in solving each of them according to its contribution to the overall objective value of the final solution. To demonstrate the effectiveness of the proposed framework, we design a new set of 40 large-scale imbalanced problems and study the performance of some possible instances of the framework

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications