Impact of Distributed Generation in the Transmission System Expansion Planning

In this work, the impact of distributed generation in the transmission expansion planning will be simulated through the performance of an optimization process for three different scenarios: the first without distributed generation, the second with distributed generation equivalent to 1% of the load, and the third with 5% of distributed generation. For modeling the expanding problem the load flow linearized method using genetic algorithms for optimization has been chosen. The test circuit used is a simplification of the south eastern Brazilian electricity system with 46 buses

Database Optimization Using Genetic Algorithms for Distributed Databases

Databases can store a vast amount of information and particular sets of data are accessed via queries which are written in specific interface language such as structured query language (SQL). Database optimization is a process of maximizing the speed and efficiency with which kind of data is retrieved or simply it’s a mechanism that reduces database systems response time. Query optimization is one of the major functionality in database management systems (DBMS). The purpose of the query optimization is to determine the most efficient and effective way to execute a particular query by considering several query plans such as graphical plans, textual plans and etc. Execution of any particular datasets depends on the capability of the query optimization mechanism to acquire competent query processing approaches. Distributed database system is a collection several interrelated databases which are spread physically across different environments that communicate through a computer network. Inability to obtain an effective query strategy with an efficient accuracy and minimum response time or cost to execute the given query is one of the major key issues of the query optimization in distributed database systems. Further inefficient database compression methods, inefficient query processing, missing indexes, inexact statistics, and deadlocks are furthermore defects. In this paper, it describes the methodologies such as genetic algorithm strategy for distributed database systems so as to execute the query plan. Genetic algorithms are extensively using to solve constrained and unconstrained optimization problems. The genetic algorithms are using three main types of rules such as selection rules, crossover rules, and mutation rules

Migrants Selection and Replacement in Distributed Evolutionary Algorithms for Dynamic Optimization

Many distributed systems (task scheduling, moving priorities, changing mobile environments, ...) can be linked as Dynamic Optimization Problems (DOPs), since they require to pursue an optimal value that changes over time. Consequently, we have focused on the utilization of Distributed Genetic Algorithms (dGAs), one of the domains still to be investigated for DOPs. A dGA essentially decentralizes the population in islands which cooperate through migrations of individuals. In this article, we analyze the effect of the migrants selection and replacement on the performance of the dGA for DOPs. Quality and distance based criteria are tested using a comprehensive set of benchmarks. Results show the benefits and drawbacks of each setting in dynamic optimization.Universidad de Málaga. Proyecto roadME (TIN2011-28194). Programa de movilidad de la AUIP

Cross-Layer Optimization and Dynamic Spectrum Access for Distributed Wireless Networks

We proposed a novel spectrum allocation approach for distributed cognitive radio networks. Cognitive radio systems are capable of sensing the prevailing environmental conditions and automatically adapting its operating parameters in order to enhance system and network performance. Using this technology, our proposed approach optimizes each individual wireless device and its single-hop communication links using the partial operating parameter and environmental information from adjacent devices within the wireless network. Assuming stationary wireless nodes, all wireless communication links employ non-contiguous orthogonal frequency division multiplexing (NC-OFDM) in order to enable dynamic spectrum access (DSA). The proposed approach will attempt to simultaneously minimize the bit error rate, minimize out-of-band (OOB) interference, and maximize overall throughput using a multi-objective fitness function. Without loss in generality, genetic algorithms are employed to perform the actual optimization. Two generic optimization approaches, subcarrier-wise approach and block-wise approach, were proposed to access spectrum. We also proposed and analyzed several approaches implemented via genetic algorithms (GA), such as quantizing variables, using adaptive variable ranges, and Multi-Objective Genetic Algorithms, for increasing the speed and improving the results of combined spectrum utilization/cross-layer optimization approaches proposed, together with several assisting processes and modifications devised to make the optimization to improve efficiency and execution time

Genetic Algorithms for Stochastic Flow Shop No Wait Scheduling

ln this paper we present Genetic Algorithms - evolutionary algorithms based on an analogy with natural selection and survival of the fittest - applied to an NP Complete combinatorial optimization problem: minimizing the makespan of a Stochastic Flow Shop No Wait (FSNW) schedule. This is an important optimization criteria in real-world situations and the problem itself is of practical significance. We restrict our applications to the three machine flow shop no wait problem which is known to be NP complete. The stochastic hypothesis is that the processing times of jobs are described by normally distributed random variables. We discuss how this problem may be translated into a TSP problem by using the start interval concept. Genetic algorithms, both sequential and parallel are then applied to search the solution space and we present the algorithms and empirical results

Application of DCS for Level Control in Nonlinear System using Optimization and Robust Algorithms

This proposed work deals with the real-time implementation of a PI level controller for a nonlinear interacting multi-input multi-output (MIMO) system using YOKOGAWA CENTUM CS 3000 DCS. Some intricate algorithms were chosen to tune the PI controller, presuming the effect of disturbances in a nonlinear interacting MIMO system. Three algorithms; a classical evolution algorithm, genetic algorithm (GA); a metaheuristic optimization algorithm, particle swarm optimization algorithm (PSO); and a robust algorithm, quantitative feedback theory (QFT) were chosen to tune thecontroller offline optimally. These controllers were then implemented in the process using distributed control systems (DCS), and the simulation results resulting from the three algorithms were compared with the experimental results. The impact of the tuning algorithms in the controller performance was studied in real-time

How good are distributed allocation algorithms for solving urban search and rescue problems? A comparative study with centralized algorithms

In this paper, a modified centralized algorithm based on particle swarm optimization (MCPSO) is presented to solve the task allocation problem in the search and rescue domain. The reason for this paper is to provide a benchmark against distributed algorithms in search and rescue application area. The hypothesis of this paper is that a centralized algorithm should perform better than distributed algorithms because it has all the available information at hand to solve the problem. Therefore, the centralized approach will provide a benchmark for evaluating how well the distributed algorithms are working and how much improvement can still be gained. Among the distributed algorithms, the consensus-based bundle algorithm (CBBA) is a relatively recent method based on the market auction mechanism, which is receiving considerable attention. Other distributed algorithms, such as PI and PI with softmax, have shown to perform better than CBBA. Therefore, in this paper, the three distributed algorithms mentioned earlier are compared against three centralized algorithms. They are particle swarm optimization, MCPSO, described in this paper, and genetic algorithms. Two experiments were conducted. The first involved comparing all the above-mentioned algorithms, both centralized and distributed, using the same set of application scenarios. It is found that MCPSO always outperforms the other five algorithms in time cost. Due to the high failure rate of CBBA and the other two centralized methods, the second experiment focused on carrying out more tests to compare MCPSO against PI and PI with softmax. All the results are shown and analyzed to determine the performance gaps between the distributed algorithms and the MCPSO

Параллельный генетический алгоритм с нечетким оператором миграции

Развитие вычислительной техники способствовало развитию методов параллельных вычислений при решении оптимизационных задач. Генетические алгоритмы – эффективное средство решения различных оптимизационных задач. В этой связи необходимы стратегии применения параллельных вычислений в генетических алгоритмах. По аналогии с эволюционными процессами за основу параллельных вычислений в генетических алгоритмах взят механизм миграции. Авторами предлагается реализация оператора миграции с использованием аппарата нечеткой логики. Проведенные испытания разработанной модели показали ее эффективность.Розвиток обчислювальної техніки сприяв розвитку методів паралельних обчислень при розв’язанні оптимізаційних задач. Генетичні алгоритми – ефективний засіб розв’язання різних оптимізаційних задач. У зв’язку з цим необхідні стратегії застосування паралельних обчислень в генетичних алгоритмах. За аналогією з еволюційними процесами за основу паралельних обчислень у генетичних алгоритмах взято механізм міграції. Авторами пропонується реалізація оператора міграції з використанням апарату нечіткої логіки. Проведені випробування розробленої моделі показали її ефективність.The computing engineering developing has assisted necessity of research applications of distributed computing methods for optimization tasks. The genetic algorithm is effective instruments for different optimization tasks solving. Thereupon methods of application distributed computing are required by genetic algorithms. The parallel computing in genetic algorithms based on migration operator by analogy with evolutionary process in nature. The mechanism of migration with fuzzy logic using is suggested. Test of migration operator has showed it effectiveness