External cost from meat consumption raises an issue of possible government mechanisms toward mitigation. Economic theory provides a framework for determining the optimal set of mechanisms considering the associated benefits and costs. Such a theoretical development rests on the responsiveness of consumers to alternative mechanisms. Considering two mechanisms, a Pigouvian tax and green-label education, yields tandem theoretical optimal government mechanisms. Populating this theoretical model with empirically derived elasticities and other parameters provides an application. Results indicate education alone will likely not yield a high social-optimal level of mitigation. Instead, a Pigouvian tax will be required to move consumption toward a socially desired state. <br>

With the growth of the distributed power generation market and the increasing integration of energy systems, more and more low carbon technologies are being installed at the domestic building level to optimise daily energy cost and reduce carbon emissions. The objective of this thesis is to optimise domestic building daily energy cost, and to identify ways of reducing the installation and maintenance costs of all domestic energy infrastructures. In this thesis, general energy conversion, storage and transmission in domestic buildings are considered. The first key part of this thesis is to size a combined heat and power (CHP) unit based on the Maximum Rectangle (MR) method and use the Genetic Algorithm (GA) method to optimize daily energy cost for a building without an energy storage system. The second key part of the thesis is to size a hybrid energy storage system (HESS) and develop a new rule-based energy control rule to optimise energy cost for a building with an energy storage system. The results show that after sizing the HESS, the daily benefit-cost ratio of the HESS is increased by approximately a factor of two over previous work. Additionally, the proposed rule based energy control model can yield up to 19.8% energy cost saving compared to a system dependent solely on electricity from the grid and using a boiler to generate heat. This ratio is almost equal to the previous work, but the present work increases customers’ comfort level by treating all load as critical. In addition, the optimization approach in this thesis is more real-world feasible, because it is not possible for exact loads to be known in advance. The results also show that daily energy cost saving for a building with HESSs and the appropriate control rule is approximately 47% higher than a building with a well-sized CHP but no HESS; and the capacity of CHP can also be reduced when the HESS is installed. Thus, the installation and maintenance costs of HESSs can be offset by reducing the capacity of CHP to some extent. In addition, the proposed control algorithm and HESSs have outstanding performance in improving the effective CHP output efficiency and average CHP input to output ratio. This proves the combination of HESS and the proposed rule-base control algorithm can reduce carbon emissions and make full use of CHP capacity. Page | iii However at present, the benefit to cost ratios of case studies of such domestic energy systems are always lower than 11% giving a negative return on investment. This figure is mainly limited by the high manufacturing price of HESSs and CHP. In the medium to long term future, the downward trend in battery and CHP manufacturing costs, coupled with changing energy tariffs are likely to lead to overwhelmingly positive cost benefit for this technology.

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 53). / In this thesis, we implement and test two algorithms for binary optimization and mixed integer optimization, respectively. We fine tune the parameters of these two algorithms and achieve satisfactory performance. We also compare our algorithms with CPLEX on large amount of fairly large-size instances. Based on the experimental results, our binary optimization algorithm delivers performance that is strictly better than CPLEX on instances with moderately dense constraint matrices, while for sparse instances, our algorithm delivers performance that is comparable to CPLEX. Our mixed integer optimization algorithm outperforms CPLEX most of the time when the constraint matrices are moderately dense, while for sparse instances, it yields results that are close to CPLEX, and the largest gap relative to the result given by CPLEX is around 5%. Our findings show that these two algorithms, especially the binary optimization algorithm, have practical promise in solving large, dense instances of both set covering and set packing problems. / by Jingting Zhou. / S.M.

Numerical optimization is one of the most active research areas and is widely used in science, engineering, economics and industry. In numerical black box optimization, the underlying objective functions, which can be non-differentiable, non-convex, multi-modal and noisy, are unknown to optimization algorithms. At any points in the continuous search space, the first and second order information is not available. Only the objective function values are available by means of function evaluations. The optimization algorithms, which consider these optimization problems as a black box, are designed to find the best solutions in the continuous search space. / This thesis focuses on continuous black box optimization and presents a collection of the novel sampling methods improving the state-of-the-art optimization algorithms. The algorithms are Covariance Matrix Adaptation Evolution Strategies (CMA-ES) and Cooperative Coevolutionary Algorithms (CCEAs). We will also study these algorithms in the dynamic environments where the objective functions change during the course of optimization. It is necessary to understand algorithms’ performances because many real world problems are basically dynamic in nature. Examples of real world problems include but not limited to random arrival of new tasks, machine faults and degradation, climate change, market fluctuation and economic factors. / In the first part of this thesis, two novel sampling methods that improves Evolution Strategies (ES) for continuous black-box optimization will be introduced: halfspace sampling and eigenspace sampling. In Halfspace Sampling, the hyperplane which goes through the current solution separates the search space into two halfspaces: a positive halfspace and a negative halfspace. When a candidate solution is sampled, the sample always lies in the positive halfspace that is estimated by successful steps in the recent iterations. We theoretically derive the log-linear convergence rates of a scale-invariant step size ES when ES are used to optimize spherical functions in finite and infinite dimensions. Halfspace sampling is implemented in a (1+1) CMA-ES, and the resulting algorithm is benchmarked on the Black-Box Optimization Benchmarking (BBOB) testbed. In Eigenspace sampling, the optimization algorithms consider the eigenspace of the underlying objective functions. A candidate solution is always sampled in an eigenspace spanned by eigen-vectors with repeated or clustered eigenvalues. This demonstrates experimentally how eigenspace sampling can improve the CMA-ES for the current benchmark problems, In particular, the CMA-ES that uses eigenspace sampling often performs very well in ill-conditioned problems. / In the second part of this thesis, we will study the CMA-ES, ES and CCEA in dynamic environments. Two new types of individuals that address the dynamic environments will be introduced: 1) random immigrants (RIs) that increase the diversity for the changing environments, and 2) elitist individuals that improve the local convergence to the optima. The resulting algorithms are evaluated on a standard suite of benchmark problems. Superior results are observed when the two types of individuals are used. We also investigate the behavior of three CMA-ES variants, which include an elitist (1+1)-CMA-ES, a non-elitist(μ,λ)-CMA-ES and a sep-CMA-ES. Our experimental results show the simple elitist strategies that include the (1+1)-ES and the (1+1)-CMA-ES generally outperform non-elitist CMA-ES variants. The elitist strategies are robust to dynamic changes with different severites, but performance is worsened when the problem dimensions are increased. In higher dimensions, the performance of elitist and non-elitist variants of CMA-ES are marginally identical. / 「連續函數最優化」乃係研究領域中一項重要之議題。其廣泛被應用於科學、工程、經濟及工業等範籌。於黑箱最優化下，「優化算法」往往未能掌握目標函數之基本特性如可微分性，非凸性，多模態性及雜訊，甚至未能完全準確地使用連續函數之基本第一階及第二階導數。它們只能通過目標函數值來評估優化之進度。因此，設計最佳優化算法實為一個既困難且富挑戰性之研究議題。 / 本論文主要探討「連續函數黑箱最優化」，並提出新穎之抽樣方法，分別為「協方差矩陣適應進化策略」 (CMA-ES) 及「協同進化算法」(CCEAs) ，以改善現時研究領域中最佳之優化算法。本文亦深入了解此算法於動態環境中之優化表現。於應用科學中，如氣候變化，市場波動及經濟因素等等問題於本質上是動態，因此了解它們於動態環境中之優化表現是十分重要。 / 本論文第一部分提出兩種新抽樣方法，分別為「半空間抽樣法」及「特徵空間抽樣法」， 作為提高「進化策略」 (ES) 之最優化表現。一、於「半空間抽樣法」下，連續函數之搜崇空間會被一片超平面分割成兩個半空間，分別是「正半空間」及「負半空間」。當優化算法尋找一個元素時，它每一次會從正半空間里面抽樣。本文由此推算出進化策略在球形函數收斂速度，並將之應用於最先進之(1 十1)CMA-ES，從而測試它在最新之BBOB平台之表現。二、於「特徵空間抽樣法」下，優化算法首先在擁有重複或集群特徵值之特徵空間內抽出一個元素，然後將特徵空間抽樣法應用於CMA-ES。實驗結果發現使用特徵抽樣法能提升CMA-ES在俗稱「病態」函數中之優化表現。 / 本論文第二部分探討三個優化算法—「協方差矩陣適應進化策略」、「高進化策略」及「協同進化算法」於動態環境中之表現。本文提出以兩種新元素作處理動態環境—「隨機元素」(Random Immigrants)及「優生元素」(Elitist)。「 隨機元素」用以增加優化過程中元素之多樣性：「優生元素」則作增強局部優化之收斂速度。另外，本文更使用最新之測試平台評估三個CMA-ES優化算法之表現，包括(1+1) CMA-ES、(μ,λ)CMA-ES及sep CMA-ES。實驗結果證明，簡單精英進化策略，如(1+1)ES及(1+1)CMA-ES，普遍比非精英進化策略更能於動態環境作優化表現。精英進化策略較能應付不同程度轉變之動態環境。相反，當該連續函數之維度增加時，進化策略之優化表現則開始下降。於高維度下，精英CMA-ES及非精英CMA-ES之優化表現大致相同。 / Au, Chun Kit. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2015. / Includes bibliographical references (leaves 200-230). / Abstracts also in Chinese. / Title from PDF title page (viewed on 26, October, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

by Chau Chun Bun. / Bibliography: leaves 85-86 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1984

by Cheung Kam Ching Leo. / Thesis (M.Ph.)--Chinese University of Hong Kong, 1988. / Bibliography: leaves 98-99.

The purpose of this thesis is to develop methods to generate minimum-energy consumption trajectories for a point-to-point motion under pre-defined kinematic and dynamic constraints for robotic manipulators. With respect to other trajectory optimization methods, the work presented in this thesis provides two new methods to the scientific literature. The proposed methods improve the handling of the constraints in trajectory optimization methods as well as reducing the computational complexity of redundant/hyper-redundant manipulator systems.

"Complex event processing (CEP) has become increasingly important in modern applications, ranging from supply chain management for RFID tracking to real-time intrusion detection. These monitoring applications must detect complex event pattern sequences in event streams. However, the state-of-art in the CEP literature such as SASE, ZStream or Cayuga either do not support the specification of nesting for pattern queries altogether or they limit the nesting of non-occurrence expressions over composite event types. A recent work by Liu et al proposed a nested complex event pattern expression language, called NEEL (Nested Complex Event Language), that supports the specification of the non-occurrence over complex expressions. However, their work did not carefully consider predicate handling in these nested queries, especially in the context of complex negation. Yet it is well-known that predicate specification is a critical component of any query language. To overcome this gap, we now design a nested complex event pattern expression language called NEEL+, as an extension of the NEEL language, specifying nested CEP queries with predicates. We rigorously define the syntax and semantics of the NEEL+ language, with particular focus on predicate scoping and predicate placement. Accordingly, we introduce a top-down execution paradigm which recursively computes a nested NEEL+ query from the outermost query to the innermost one. We integrate predicate evaluation as part of the overall query evaluation process. Moreover, we design two optimization techniques that reduce the computation costs for processing NEEL+ queries. One, the intra-query method, called predicate push-in, optimizes each individual query component of a nested query by pushing the predicate evaluation into the process of computing the query rather than evaluating predicates at the end of the computation of that particular query. Two, the inter-query method, called predicate shortcutting, optimizes inter-query predicate evaluation. That is, it evaluates the predicates that correlate different query components within a nested query by exploiting a light weight predicate short cut. The NEEL+ system caches values of the equivalence attributes from the incoming data stream. When the computation starts, the system checks the existence of the attribute value of the outer query component in the cache and the predicate acts as a shortcut to early terminate the computation. Lastly, we conduct experimental studies to evaluate the CPU processing resources of the NEEL+ System with and without optimization techniques using real-world stock trading data streams. Our results confirm that our optimization techniques when applied to NEEL+ in a rich variety of cases result in a 10 fold faster query processing performance than the NEEL+ system without optimization."

"The fast pace of global urbanization is drastically changing the population distributions over the world, which leads to significant changes in geographical population densities. Such changes in turn alter the underlying geographical power demand over time, and drive power substations to become over-supplied (demand ≪ capacity) or under-supplied (demand ≈ capacity). In this work, we make the first attempt to investigate the problem of power substation/user assignment by analyzing large scale power grid data. We develop a Scalable Power User Assignment (SPUA) framework, that takes large-scale spatial power user/substation distribution data and temporal user power consumption data as input, and assigns users to substations, in a manner that minimizes the maximum substation utilization among all substations. To evaluate the performance of SPUA framework, we conduct evaluations on real power consumption data and user/substation location data collected from Xinjiang Province in China for 35 days in 2015. The evaluation results demonstrate that our SPUA framework can achieve a 20%–65% reduction on the maximum substation utilization, and 2 to 3.7 times reduction on total transmission loss over other baseline methods."

Casting as a manufacturing process to make complex shapes of metal materials in mass production may experience many different defects such as porosity and incomplete filling. How to improve the casting quality becomes important. Gating/riser system design is critical to improving casting quality. The objective of the research presented in this thesis is to optimize gating/riser systems based on CAD and simulation technology with the goal of improving casting quality such as reducing incomplete filling area, decreasing large porosity and increasing yield. Therefore in the thesis, an optimization framework is presented based on CAD and simulation technology. Given a CAD model of part design and after converted to a casting model, it is the first step to evaluate castability of the casting design. Then the runner and risers are represented parametrically, and CAD models generated by varying parameters can be used in the simulation. After analyzing simulation results, the gating/riser system design is optimized to improve casting quality. In the thesis, one engine block is used to verify the effectiveness of the optimization method. Compared with the initial design, it is found that the optimized casting design can decrease porosity around 18% while the yield increases 16%.