유전 알고리즘을 이용한 다중스케일/다목적 공간계획 최적화모델 구축

학위논문 (박사)-- 서울대학교 대학원 : 환경대학원 협동과정 조경학전공, 2019. 2. 이동근.공간계획 과정에서 다양한 이해관계자와 결부된 목표와 제약 요건을 만족시키는 것은 복잡한 비선형적 문제로서 해결하기 어려운 것으로 알려져 왔다. 그러나 최근 이러한 문제에 유전 알고리즘 (genetic algorithms), 담금질 기법 (simulated annealing), 개미 군집 최적화 (ant colony optimization) 등의 다목적 최적화 알고리즘이 응용되고 있으며, 관련 연구 역시 급증하고 있다. 이 중 유전 알고리즘은 공간 최적화 부문에 가장 빈도 높게 적용된 최적화 알고리즘으로 exploration과 exploitation의 균형으로 합리적인 시간 내에 충분히 좋은 계획안을 제시할 수 있다. 그러나 공간 최적화 연구가 보여준 좋은 성과에도 불구하고 대부분의 연구가 특정 용도 혹은 시설의 배치에 집중되어 있으며, 기후변화 적응, 재해 관리, 그린인프라 계획과 같은 최근의 환경 이슈를 다룬 사례는 매우 미흡하다. 따라서 본 연구에서는 유전 알고리즘과 비지배 정렬 유전 알고리즘 (non-dominated sorting genetic algorithm II)에 기초하여 기후변화 적응, 재해 관리, 도시의 녹지 계획 등과 같은 환경 이슈를 공간계획에 반영할 수 있는 일련의 공간 최적화 모델을 제시하였다. 개별 환경 이슈에 따라 공간 해상도, 목적, 제약요건이 다르게 구성하였으며, 공간적 범위가 좁아지고 공간해상도는 높아지는 순서대로 나열하였다. 논문의 첫번째 장에서는 행정구역 도 규모 (province scale, 해상도 1㎢)에서 미래의 기후변화에 적응하기 위한 토지이용 시나리오를 모의할 수 있는 공간 최적화 모델을 제안하였다. 기후변화가 먼 미래가 아닌, 현재 이미 진행되고 있으며 관련한 다수의 피해가 관찰되고 있기 때문에 공간적 관점에서 기후변화에 대한 적응의 필요성이 지적되어 왔다. 그러나 구체적으로 기후에 대한 회복 탄력성을 향상시키기 위하여 토지이용의 공간적 구성을 어떻게 변화시켜야 할지에 대한 방법론 제시는 미흡하다. 지역계획에서 기후변화 영향을 고려한 토지이용 배분은 매우 유용한, 기본적인 중장기 적응 전략에 해당한다. 본 연구에서는 다목적 유전 알고리즘 (MOGA, multi-objective genetic algorithm)에 기초하여 9,982㎢에 350만의 인구가 거주하는 한국의 충청남도 및 대전광역시 일대를 대상으로 기후변화 적응을 위한 토지이용 시나리오를 제시하였다. 지역적인 기후변화 영향과 경제적 여건을 고려하여 재해 피해 및 전환량의 최소화, 벼 생산량, 종 풍부도 보전, 경제적 가치의 최대화 등 다섯 가지의 목적을 선택하였다. 각 목적 별 가중치를 변화시키며 여섯 가지 가중치 조합에 대한 17개의 파레토 최적 토지이용 시나리오를 생성하였다. 대부분의 시나리오는 정도의 차이는 있으나 현재의 토지이용에 비해 기후변화 적응 부분에서 더 좋은 퍼포먼스를 보였으므로, 기후변화에 대한 회복탄력성이 개선할 수 있을 것으로 판단하였다. 또한 공간 최적화 모델의 유연한 구조를 고려하였을 때, 지역의 실무자 역시 가중치와 같은 모델의 파라미터, 기후변화 영향 평가와 같은 입력자료를 변경함으로써 효율적으로 새로운 시나리오를 생성 및 선택하는 것이 가능할 것으로 예상하였다. 논문의 두 번째 장에서는 행정구역 군 규모 (local scale, 해상도 100m)에서 기후변화에 따른 재해 피해를 관리하기 위한 토지이용 시나리오를 모의할 수 있는 공간 최적화 모델을 제안하였다. 산악지형에서 폭우로 인한 산사태는 인명과 재산에 심각한 피해를 초래할 수 있는 것으로 알려져 있다. 더욱이 기후변화에 따른 강우의 변동성 증가로 이러한 산사태 빈도 및 강도 역시 증대될 것으로 예상된다. 일반적으로 산사태 리스크가 높은 지역을 피해 개발지역을 배치하는 것이 피해를 저감 혹은 회피할 수 있는 가장 효과적인 전략으로 알려져 있으나, 실제공간에서의 계획은 매우 복잡한 비선형의 문제로서 이것을 실현하는 데 어려움이 있다. 따라서 본 연구에서는 비지배 정렬 유전 알고리즘 II에 기초하여 산사태 리스크 및 전환량, 파편화의 최소화 등의 다양한 목적을 만족시키는 종합적인 토지이용 배분 계획을 제안하였다. 대상지는 2018년 동계올림픽 개최지인 한국의 평창군으로서 2006년에 산사태로 인한 대규모의 피해를 경험하였으나, 올림픽 특수 등의 개발압력으로 인한 난개발이 우려되는 지역이다. 최종적으로 한번의 모의를 통해 현재의 토지이용 보다 적어도 한가지 이상의 목적에서 좋은 퍼포먼스를 보이는 100개의 파레토 최적 계획안을 생성하였다. 또한 5개의 대표적인 계획안을 선정하여 산사태리스크 최소화와 전환량 최소화 간에 발생하는 상쇄 효과를 설명하였다. 본 연구결과는 기후변화와 관련된 공간 적응 전략의 수립, 보다 향상된 개발계획을 위한 의사결정을 효과적으로 지원할 수 있을 것으로 예상하였다. 논문의 세 번째 장에서는 블록 규모(neighborhood scale, 2m)에서 도시 내 녹지계획안을 모의할 수 있는 공간 최적화 모델을 제안하였다. 녹지 공간은 도시민의 삶의 질에 결정적인 영향을 미치기 때문에 다양한 도시 재생 및 개발계획에는 녹지와 직 간접적으로 관련된 전략이 포함된다. 녹지 공간은 도시지역 내에서 열섬 현상 완화, 유출량 저감, 생태 네트워크 증진 등 다양한 긍정적 효과가 있음이 알려져 있으나, 공간 계획의 관점에서 이러한 다양한 효과를 종합적, 정량적으로 고려된 사례는 매우 미흡하다. 따라서 본 연구에서는 비지배 정렬 유전 알고리즘 II에 기초하여 녹지의 생태적 연결성 증진, 열섬 효과 완화와 같은 다양한 효과와 설치에 따르는 비용을 종합적으로 고려하여 적절한 녹지의 유형과 위치를 결정한 녹지계획안을 제시하였다. 블록 규모의 가상의 대상지에 본 최적화 모델을 적용함으로써 30개의 파레토 최적 녹지계획안을 생성하였으며, 각 목적 간 퍼포먼스를 비교하여 녹지의 열섬 완화 효과와 생태적 연결성 증진 효과 간의 상승 관계 (synergistic relationship), 이러한 긍정적 효과와 비용 절감 간의 상쇄 효과 (trade-off relationship)를 분석하였다. 또한 다양한 계획안 중 대표적인 특성을 지니는 계획안, 다수의 계획안에서 공통적으로 녹지 설치를 위해 선택된 주요 후보지역 역시 규명하였다. 본 연구에서 제시된 모델은 계획안의 수정에서부터 정량적 평가, 계획안 선택에 이르는 일련의 긍정적인 피드백 과정을 수없이 반복함으로써 기존의 녹지계획 과정을 개선하는 데 기여할 수 있을 뿐만 아니라 모델의 결과 역시 다자간 협력적 디자인 (co-design)을 위한 초안으로서 활용될 수 있을 것으로 예상하였다.The meeting of heterogeneous goals while staying within the constraints of spatial planning is a nonlinear problem that cannot be solved by linear methodologies. Instead, this problem can be solved using multi-objective optimization algorithms such as genetic algorithms (GA), simulated annealing (SA), ant colony optimization (ACO), etc., and research related to this field has been increasing rapidly. GA, in particular, are the most frequently applied spatial optimization algorithms and are known to search for a good solution within a reasonable time period by maintaining a balance between exploration and exploitation. However, despite its good performance and applicability, it has not adequately addressed recent urgent issues such as climate change adaptation, disaster management, and green infrastructure planning. It is criticized for concentrating on only the allocation of specific land use such as urban and protected areas, or on the site selection of a specific facility. Therefore, in this study, a series of spatial optimizations are proposed to address recent urgent issues such as climate change, disaster management, and urban greening by supplementing quantitative assessment methodologies to the spatial planning process based on GA and Non-dominated Sorting Genetic Algorithm II (NSGA II). This optimization model needs to be understood as a tool for providing a draft plan that quantitatively meets the essential requirements so that the stakeholders can collaborate smoothly in the planning process. Three types of spatial planning optimization models are classified according to urgent issues. Spatial resolution, planning objectives, and constraints were also configured differently according to relevant issues. Each spatial planning optimization model was arranged in the order of increasing spatial resolution. In the first chapter, the optimization model was proposed to simulate land use scenarios to adapt to climate change on a provincial scale. As climate change is an ongoing phenomenon, many recent studies have focused on adaptation to climate change from a spatial perspective. However, little is known about how changing the spatial composition of land use could improve resilience to climate change. Consideration of climate change impacts when spatially allocating land use could be a useful and fundamental long-term adaptation strategy, particularly for regional planning. Here climate adaptation scenarios were identified on the basis of existing extents of three land use classes using Multi-objective Genetic Algorithms (MOGA) for a 9,982 km2 region with 3.5 million inhabitants in South Korea. Five objectives were selected for adaptation based on predicted climate change impacts and regional economic conditions: minimization of disaster damageand existing land use conversionmaximization of rice yieldprotection of high-species-richness areasand economic value. The 17 Pareto land use scenarios were generated by six weighted combinations of the adaptation objectives. Most scenarios, although varying in magnitude, showed better performance than the current spatial land use composition for all adaptation objectives, suggesting that some alteration of current land use patterns could increase overall climate resilience. Given the flexible structure of the optimization model, it is expected that regional stakeholders would efficiently generate other scenarios by adjusting the model parameters (weighting combinations) or replacing the input data (impact maps) and selecting a scenario depending on their preference or a number of problem-related factors. In the second chapter, the optimization model was proposed to simulate land use scenarios for managing disaster damage due to climate change on local scale. Extreme landslides triggered by rainfall in hilly regions frequently lead to serious damage, including casualties and property loss. The frequency of landslides may increase under climate change, because of the increased variability of precipitation. Developing urban areas outside landslide risk zones is the most effective method of reducing or preventing damageplanning in real life is, however, a complex and nonlinear problem. For such multi-objective problems, GA may be the most appropriate optimization tool. Therefore, comprehensive land use allocation plans were suggested using the NSGA II to overcome multi-objective problems, including the minimization of landslide risk, minimization of change, and maximization of compactness. The study area is Pyeongchang-gun, the host city of the 2018 Winter Olympics in Korea, where high development pressure has resulted in an urban sprawl into the hazard zone that experienced a large-scale landslide in 2006. We obtained 100 Pareto plans that are better than the actual land use data for at least one objective, with five plans that explain the trade-offs between meeting the first and the second objectives mentioned above. The results can be used by decision makers for better urban planning and for climate change-related spatial adaptation. In the third chapter, the optimization model was proposed to simulate urban greening plans on a neighborhood scale. Green space is fundamental to the good quality of life of residents, and therefore urban planning or improvement projects often include strategies directly or indirectly related to greening. Although green spaces generate positive effects such as cooling and reduction of rainwater runoff, and are an ecological corridor, few studies have examined the comprehensive multiple effects of greening in the urban planning context. To fill this gap in this fields literature, this study seeks to identify a planning model that determines the location and type of green cover based on its multiple effects (e.g., cooling and enhancement of ecological connectivity) and the implementation cost using NSGA II. The 30 Pareto-optimal plans were obtained by applying our model to a hypothetical landscape on a neighborhood scale. The results showed a synergistic relationship between cooling and enhancement of connectivity, as well as a trade-off relationship between greenery effects and implementation cost. It also defined critical lots for urban greening that are commonly selected in various plans. This model is expected to contribute to the improvement of existing planning processes by repeating the positive feedback loop: from plan modification to quantitative evaluation and selection of better plans. These optimal plans can also be considered as options for co-design by related stakeholders.1. INTRODUCTION 2. CHAPTER 1: Modelling Spatial Climate Change Land use Adaptation with Multi-Objective Genetic Algorithms to Improve Resilience for Rice Yield and Species Richness and to Mitigate Disaster Risk 2.1. Introduction 2.2. Study area 2.3. Methods 2.4. Results 2.5. Discussion 2.6. References 2.7. Supplemental material 3. CHAPTER 2: Multi-Objective Land-Use Allocation Considering Landslide Risk under Climate Change: Case Study in Pyeongchang-gun, Korea 3.1. Introduction 3.2. Material and Methods 3.3. Results 3.4. Discussion 3.5. Conclusion 3.6. References 4. CHAPTER 3: Multi-Objective Planning Model for Urban Greening based on Optimization Algorithms 3.1. Introduction 3.2. Methods 3.3. Results 3.4. Discussion 3.5. Conclusion 3.6. References 3.7. Appendix 5. CONCLUSION REFERENCESDocto

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Evolutionary approaches to optimisation in rough machining

This thesis concerns the use of Evolutionary Computation to optimise the sequence and selection of tools and machining parameters in rough milling applications. These processes are not automated in current Computer-Aided Manufacturing (CAM) software and this work, undertaken in collaboration with an industrial partner, aims to address this. Related research has mainly approached tool sequence optimisation using only a single tool type, and machining parameter optimisation of a single-tool sequence. In a real world industrial setting, tools with different geometrical profiles are commonly used in combination on rough machining tasks in order to produce components with complex sculptured surfaces. This work introduces a new representation scheme and search operators to support the use of the three most commonly used tool types: end mill, ball nose and toroidal. Using these operators, single-objective metaheuristic algorithms are shown to find near-optimal solutions, while surveying only a small number of tool sequences. For the first time, a multi-objective approach is taken to tool sequence optimisation. The process of ‘multi objectivisation’ is shown to offer two benefits: escaping local optima on deceptive multimodal search spaces and providing a selection of tool sequence alternatives to a machinist. The multi-objective approach is also used to produce a varied set of near-Pareto optimal solutions, offering different trade-offs between total machining time and total tooling costs, simultaneously optimising tool sequences and the cutting speeds of individual tools. A challenge for using computationally expensive CAM software, important for real world machining, is the time cost of evaluations. An asynchronous parallel evolutionary optimisation system is presented that can provide a significant speed up, even in the presence of heterogeneous evaluation times produced by variable length tool sequences. This system uses a distributed network of processors that could be easily and inexpensively implemented on existing commercial hardware, and accessible to even small workshops

Parallel optimization algorithms for high performance computing : application to thermal systems

The need of optimization is present in every field of engineering. Moreover, applications requiring a multidisciplinary approach in order to make a step forward are increasing. This leads to the need of solving complex optimization problems that exceed the capacity of human brain or intuition. A standard way of proceeding is to use evolutionary algorithms, among which genetic algorithms hold a prominent place. These are characterized by their robustness and versatility, as well as their high computational cost and low convergence speed. Many optimization packages are available under free software licenses and are representative of the current state of the art in optimization technology. However, the ability of optimization algorithms to adapt to massively parallel computers reaching satisfactory efficiency levels is still an open issue. Even packages suited for multilevel parallelism encounter difficulties when dealing with objective functions involving long and variable simulation times. This variability is common in Computational Fluid Dynamics and Heat Transfer (CFD & HT), nonlinear mechanics, etc. and is nowadays a dominant concern for large scale applications. Current research in improving the performance of evolutionary algorithms is mainly focused on developing new search algorithms. Nevertheless, there is a vast knowledge of sequential well-performing algorithmic suitable for being implemented in parallel computers. The gap to be covered is efficient parallelization. Moreover, advances in the research of both new search algorithms and efficient parallelization are additive, so that the enhancement of current state of the art optimization software can be accelerated if both fronts are tackled simultaneously. The motivation of this Doctoral Thesis is to make a step forward towards the successful integration of Optimization and High Performance Computing capabilities, which has the potential to boost technological development by providing better designs, shortening product development times and minimizing the required resources. After conducting a thorough state of the art study of the mathematical optimization techniques available to date, a generic mathematical optimization tool has been developed putting a special focus on the application of the library to the field of Computational Fluid Dynamics and Heat Transfer (CFD & HT). Then the main shortcomings of the standard parallelization strategies available for genetic algorithms and similar population-based optimization methods have been analyzed. Computational load imbalance has been identified to be the key point causing the degradation of the optimization algorithm¿s scalability (i.e. parallel efficiency) in case the average makespan of the batch of individuals is greater than the average time required by the optimizer for performing inter-processor communications. It occurs because processors are often unable to finish the evaluation of their queue of individuals simultaneously and need to be synchronized before the next batch of individuals is created. Consequently, the computational load imbalance is translated into idle time in some processors. Several load balancing algorithms have been proposed and exhaustively tested, being extendable to any other population-based optimization method that needs to synchronize all processors after the evaluation of each batch of individuals. Finally, a real-world engineering application that consists on optimizing the refrigeration system of a power electronic device has been presented as an illustrative example in which the use of the proposed load balancing algorithms is able to reduce the simulation time required by the optimization tool.El aumento de las aplicaciones que requieren de una aproximación multidisciplinar para poder avanzar se constata en todos los campos de la ingeniería, lo cual conlleva la necesidad de resolver problemas de optimización complejos que exceden la capacidad del cerebro humano o de la intuición. En estos casos es habitual el uso de algoritmos evolutivos, principalmente de los algoritmos genéticos, caracterizados por su robustez y versatilidad, así como por su gran coste computacional y baja velocidad de convergencia. La multitud de paquetes de optimización disponibles con licencias de software libre representan el estado del arte actual en tecnología de optimización. Sin embargo, la capacidad de adaptación de los algoritmos de optimización a ordenadores masivamente paralelos alcanzando niveles de eficiencia satisfactorios es todavía una tarea pendiente. Incluso los paquetes adaptados al paralelismo multinivel tienen dificultades para gestionar funciones objetivo que requieren de tiempos de simulación largos y variables. Esta variabilidad es común en la Dinámica de Fluidos Computacional y la Transferencia de Calor (CFD & HT), mecánica no lineal, etc. y es una de las principales preocupaciones en aplicaciones a gran escala a día de hoy. La investigación actual que tiene por objetivo la mejora del rendimiento de los algoritmos evolutivos está enfocada principalmente al desarrollo de nuevos algoritmos de búsqueda. Sin embargo, ya se conoce una gran variedad de algoritmos secuenciales apropiados para su implementación en ordenadores paralelos. La tarea pendiente es conseguir una paralelización eficiente. Además, los avances en la investigación de nuevos algoritmos de búsqueda y la paralelización son aditivos, por lo que el proceso de mejora del software de optimización actual se verá incrementada si se atacan ambos frentes simultáneamente. La motivación de esta Tesis Doctoral es avanzar hacia una integración completa de las capacidades de Optimización y Computación de Alto Rendimiento para así impulsar el desarrollo tecnológico proporcionando mejores diseños, acortando los tiempos de desarrollo del producto y minimizando los recursos necesarios. Tras un exhaustivo estudio del estado del arte de las técnicas de optimización matemática disponibles a día de hoy, se ha diseñado una librería de optimización orientada al campo de la Dinámica de Fluidos Computacional y la Transferencia de Calor (CFD & HT). A continuación se han analizado las principales limitaciones de las estrategias de paralelización disponibles para algoritmos genéticos y otros métodos de optimización basados en poblaciones. En el caso en que el tiempo de evaluación medio de la tanda de individuos sea mayor que el tiempo medio que necesita el optimizador para llevar a cabo comunicaciones entre procesadores, se ha detectado que la causa principal de la degradación de la escalabilidad o eficiencia paralela del algoritmo de optimización es el desequilibrio de la carga computacional. El motivo es que a menudo los procesadores no terminan de evaluar su cola de individuos simultáneamente y deben sincronizarse antes de que se cree la siguiente tanda de individuos. Por consiguiente, el desequilibrio de la carga computacional se convierte en tiempo de inactividad en algunos procesadores. Se han propuesto y testado exhaustivamente varios algoritmos de equilibrado de carga aplicables a cualquier método de optimización basado en una población que necesite sincronizar los procesadores tras cada tanda de evaluaciones. Finalmente, se ha presentado como ejemplo ilustrativo un caso real de ingeniería que consiste en optimizar el sistema de refrigeración de un dispositivo de electrónica de potencia. En él queda demostrado que el uso de los algoritmos de equilibrado de carga computacional propuestos es capaz de reducir el tiempo de simulación que necesita la herramienta de optimización

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa