Group Search Optimizer for Economic Load Dispatch ab 54.99 € als Taschenbuch: An Animal searching technique for power system optimization. Aus dem Bereich: Bücher, Wissenschaft, Physik,
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As the amount of stored data grows, the relational schemas needed to organize all these data get more complex, increasing the number of relations in the database. As a consequence, it becomes necessary to write SQL queries that involve a large number of relations. Once a SQL query is introduced into the DBMS, the query optimizer must find the most efficient query execution plan to solve it. State-of-the-art query optimizers, which typically employ dynamic programming techniques, are limited in the number of joins they can handle. In these situations, optimizers either resort to heuristics or fall back to greedy algorithms. However, greedy algorithms do not consider the entire search space and thus may overlook the optimal plan, resulting in bad query performance. In this book, we present a query optimizer based on genetic programming algorithms. We compare the results yielded by our optimizer with those yielded by the UDB DB2 optimizer, as well as some of the most efficient randomized algorithms proposed in the literature. Our studies show that the larger the number of relations involved in the query, the larger the benefit obtained by this type of optimizers.
For a DBMS that provides support for a declarative query language like SQL, the query optimizer is a crucial piece of software. The declarative nature of a query allows it to be translated into many equivalent evaluation plans. The process of choosing a suitable plan from all alternatives is known as query optimization. The basis of this choice are a cost model and statistics over the data. Essential for the costs of a plan is the execution order of join operations in its operator tree, since the runtime of plans with different join orders can vary by several orders of magnitude. An exhaustive search for an optimal solution over all possible operator trees is computationally infeasible. To decrease complexity, the search space must be restricted. Therefore, a well-accepted heuristic is applied: All possible bushy join trees are considered, while cross products are excluded from the search.There are two efficient approaches to identify the best plan: bottom-up and top- down join enumeration. But only the top-down approach allows for branch-and-bound pruning, which can improve compile time by several orders of magnitude, while still preserving optimality.Hence, this book focuses on the top-down join enumeration. In the first part, we present two efficient graph-partitioning algorithms suitable for top-down join enumer- ation. However, as we will see, there are two severe limitations: The proposed algo- rithms can handle only (1) simple (binary) join predicates and (2) inner joins. There- fore, the second part adopts one of the proposed partitioning strategies to overcome those limitations. Furthermore, we propose a more generic partitioning framework that enables every graph-partitioning algorithm to handle join predicates involving more than two relations, and outer joins as well as other non-inner joins. As we will see, our framework is more efficient than the adopted graph-partitioning algorithm. The third part of this book discusses the two branch-and-bound pruning strategies that can be found in the literature. We present seven advancements to the combined strategy that improve pruning (1) in terms of effectiveness, (2) in terms of robustness and (3), most importantly, avoid the worst-case behavior otherwise observed.Different experiments evaluate the performance improvements of our proposed methods. We use the TPC-H, TPC-DS and SQLite test suite benchmarks to evalu- ate our joined contributions. As we show, the average compile time improvement in those settings is 100% when compared with the state of the art in bottom-up join enu- meration. Our synthetic workloads show even higher improvement factors.
The economic load dispatch (ELD) is the process of allocating the forecasted load demand among the committed generating units in electric power system and its primary objective is to minimize the total cost of generation while honoring the operational constraints of the available generation's resource. This thesis concerns with the implementation of group search optimizer (GSO) to find the global solution for nonlinear optimization problems while satisfying equality and inequality constraints in the context of time expansive evaluation of functions. GSO technique implements the animal scanning mechanism metamorphically to design optimum searching strategies for solving optimization problems. Group search algorithm employed in this work is population based algorithm, and resource searching process of animals in nature is analogous to the process of seeking optima in a search space. The promising results on the benchmark function show the applicability of the group search optimizer for solving economic load dispatch problem. The validity of the proposed method has been demonstrated for three, four and six generator electrical power system.
This book presents an efficient meta-heuristic method for distribution systems reconfiguration for lower losses and better voltage profile. A modified Tabu Search (MTS) algorithm is used to reconfigure distribution systems so that active power losses are minimized with turning on/off sectionalizing switches. A new method to check the radial topology of the system is presented. Also, the reconfiguration problem is solved using the particle swarm optimizer (PSO), a member of the recently growing swarm intelligent-based algorithms. In most of the PSO publications, the algorithm is used for solving problems of unconstrained optimization. Consequently, to address the feeder reconfiguration problem, some modifications to the standard PSO are proposed to allow dealing with such highly constrained optimization problem. To verify the effectiveness of the proposed methods, comparative studies are conducted on four test systems with encouraging results. The proposed methods are applied to 16-node, 32-node, 69-node, and 119-node distribution systems. The obtained results are compared with results obtained using other approaches in the previous literature work to examine the performance.
Relational query optimizers are not always robust. They depend on statistics and cost models which are often inaccurate, and sometimes absent. They fail to detect correlations, and cannot efficiently handle the large search space of big queries. Those challenges and their impact on the quality of the chosen plan are aggravated in the context of XML. In fact, in XML, it is harder to collect and maintain representative statistics. Moreover, the search space of plans is usually larger than that of relational queries, due to the higher number of joins in a typical XQuery. ROX, our Run-time Optimizer for XQueries, is autonomous, not depending on statistics and cost models, is robust in always finding a good execution plan benefiting from the detected correlations, and is efficient in exploring the space of plans. ROX moves the optimization to run-time, and interleaves it with query execution, defining the plan incrementally. Sampling techniques are used to accurately estimate the cardinality and cost of operators. We introduce chain sampling, the first generic and robust method to deal with any type of correlated data. ROX can be used in both pipelined and materialized database systems.
Four modified versions of particle swarm optimizer (PSO) have been applied to the economic power dispatch with valve-point effects. In order to obtain the optimal solution, traditional PSO search a new position around the current position. The proposed strategies which explore the vicinity of particle s best position found so as far leads to a better result. In addition, to deal with the equality constraint of the economic dispatch problems, a simple mechanism is also devised that the difference of the demanded load and total generating power is evenly shared among units except the one reaching its generating limit. To show their capability, the proposed algorithms are applied to thirteen.Comparision among particle swarm optimization is given. The results show that the proposed algorithms indeed produce more optimal solutions in both cases.The different PSO techniques are New PSO, Self-Adaptive PSO and Chaotic PSO Among the different PSO techniques, it is found that Self-Adaptive PSO is better than other PSO techniques in terms of better solutions, speed of convergence, time of execution and robustness but it has more premature convergence.