Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm

Optimal multi-reservoir operation is a multi-objective problem in nature and some of its objectives are nonlinear, non-convex and multi-modal functions. There are a few areas of application of mathematical optimization models with a richer or more diverse history than in reservoir systems optimization. However, actual implementations remain limited or have not been sustained.Genetic Algorithms (GAs) are probabilistic search algorithms that are capable of solving a variety of complex multi-objective optimization problems, which may include non-linear, non-convex and multi-modal functions. GA is a population based global search method that can escape from local optima traps and find the global optima. However GAs have some drawbacks such as inaccuracy of the intensification process near the optimal set.In this paper, a new model called Self-Learning Genetic Algorithm (SLGA) is presented, which is an improved version of the SOM-Based Multi-Objective GA (SBMOGA) presented by Hakimi-Asiabar et al. (2009) [45]. The proposed model is used to derive optimal operating policies for a three-objective multi-reservoir system. SLGA is a new hybrid algorithm which uses Self-Organizing Map (SOM) and Variable Neighborhood Search (VNS) algorithms to add a memory to the GA and improve its local search accuracy. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm can enhance the local search efficiency in the Evolutionary Algorithms (EAs).To evaluate the applicability and efficiency of the proposed methodology, it is used for developing optimal operating policies for the Karoon-Dez multi-reservoir system, which includes one-fifth of Iran's surface water resources. The objective functions of the problem are supplying water demands, generating hydropower energy and controlling water quality in downstream river.     

Unsupervised adaptive neural-fuzzy inference system for solving differential equations

There has been a growing interest in combining both neural network and fuzzy system, and as a result, neuro-fuzzy computing techniques have been evolved. ANFIS (adaptive network-based fuzzy inference system) model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. In this paper, a novel structure of unsupervised ANFIS is presented to solve differential equations. The presented solution of differential equation consists of two parts; the first part satisfies the initial/boundary condition and has no adjustable parameter whereas the second part is an ANFIS which has no effect on initial/boundary conditions and its adjustable parameters are the weights of ANFIS. The algorithm is applied to solve differential equations and the results demonstrate its accuracy and convince us to use ANFIS in solving various differential equations.     

ARO: A new model free optimization algorithm for real time applications inspired by the asexual reproduction

This paper presents a new individual based optimization algorithm, which is inspired from asexual reproduction known as a remarkable biological phenomenon, called as asexual reproduction optimization (ARO). ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem; this leads to the fitter individual. ARO adaptive search ability along with its strength and weakness points are fully described in the paper. Furthermore, the ARO convergence to the global optimum is mathematically analyzed. To approve the effectiveness of the ARO performance, it is tested with several benchmark functions frequently used in the area of optimization. Finally, the ARO performance is statistically compared with that of an improved genetic algorithm (GA). Results of simulation illustrate that ARO remarkably outperforms GA.     

An enumeration algorithm for integer‐valued data envelopment analysis

Data envelopment analysis (DEA) models assume real‐valued inputs and outputs, but on many occasions, some inputs and/or outputs can only take integer values. In these cases, using DEA models can result in misleading efficiency assessments and inaccurate performance targets. In this paper, we propose an enumeration algorithm for computing efficiency scores and performance targets of decision‐making units with integer value inputs/outputs. In the presented algorithm, we do not use any of the mixed integer linear programming (MILP) models that are used in previous studies. We show that the result of our algorithm and that of the MILP model presented in this context is the same. We also generalize our algorithm for different types of returns to scale as well as for the hybrid setting with real‐valued data.     

Best position algorithms for efficient top-k query processing

The general problem of answering top-k queries can be modeled using lists of data items sorted by their local scores. The main algorithm proposed so far for answering top-k queries over sorted lists is the Threshold Algorithm (TA). However, TA may still incur a lot of useless accesses to the lists. In this paper, we propose two algorithms that are much more efficient than TA. First, we propose the best position algorithm (BPA). For any database instance (i.e. set of sorted lists), we prove that BPA stops as early as TA, and that its execution cost is never higher than TA. We show that there are databases over which BPA executes top-k queries O(m) times faster than that of TA, where m is the number of lists. We also show that the execution cost of our algorithm can be (m−1) times lower than that of TA. Second, we propose the BPA2 algorithm, which is much more efficient than BPA. We show that the number of accesses to the lists done by BPA2 can be about (m−1) times lower than that of BPA. We evaluated the performance of our algorithms through extensive experimental tests. The results show that over our test databases, BPA and BPA2 achieve significant performance gains in comparison with TA.     

Building a peer-to-peer content distribution network with high performance,scalability and robustness

Content distribution networks (CDN) are fundamental, yet expensive technologies for distributing the content of web-servers to large audiences. The P2P model is a perfect match to build a low-cost and scalable CDN infrastructure for popular websites by exploiting the underutilized resources of their user communities. However, building a P2P-based CDN is not a straightforward endeavor. In contrast to traditional CDNs, peers are autonomous and volunteer participants with their own heterogeneous interests that should be taken into account in the design of the P2P system. Moreover, churn rate is much higher than in dedicated CDN infrastructures, which can easily destabilize the system and severely degrade the performance. Finally and foremostly, while many P2P systems abstract any topological information about the underlying network, a top priority of a CDN is to incorporate locality-awareness in query routing in order to locate close-by content. This paper aims at building a P2P CDN with high performance, scalability and robustness. Our proposed protocols combine DHT efficiency with gossip robustness and take into account the interests and localities of peers. In short, Flower-CDN provides a hybrid and locality-aware routing infrastructure for user queries. PetalUp-CDN is a highly scalable version of Flower-CDN that dynamically adapts to variable rates of participation and prevent overload situations. In addition, we ensure the robustness of our P2P CDN via low-cost maintenance protocols that can detect and recover from churn and dynamicity. Our extensive performance evaluation shows that our protocols yield high performance gains under both static and highly dynamic environments. Furthermore, they incur acceptable and tunable overhead. Finally we provide main guidelines to deploy Flower-CDN for the public use.     

The bipartite edge frustration of graphs under subdivided edges and their related sums

The bipartite edge frustration of a graph G, denoted by φ(G), is the smallest number of edges that have to be deleted from G to obtain a bipartite spanning subgraph of G. This topological index is related to the well-known Max-cut problem, and has important applications in computing stability of fullerenes. In this paper, the bipartite edge frustration of an infinite family of fullerenes is computed. Moreover, this quantity for four classes of graphs arising from a given graph under different types of edge subdivisions is investigated.     

3D SSD tracking with estimated 3D planes

We present a tracking method where full camera position and orientation is tracked from intensity differences in a video sequence. The camera pose is calculated based on 3D planes, and hence does not depend on point correspondences. The plane based formulation also allows additional constraints to be naturally added, e.g., perpendicularity between walls, floor and ceiling surfaces, co-planarity of wall surfaces etc. A particular feature of our method is that the full 3D pose change is directly computed from temporal image differences without making a commitment to a particular intermediate (e.g., 2D feature) representation. We experimentally compared our method with regular 2D SSD tracking and found it more robust and stable. This is due to 3D consistency being enforced even in the low level registration of image regions. This yields better results than first computing (and hence committing to) 2D image features and then from these compute 3D pose.