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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.

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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.

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Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Economic Dispatch Using Particle swarm optimizer Techniques, Titelzusatz: Economic dispatch using PSO,NPSO,CPSO and APSO techniques, Autor: Jambulingam, Vikramarajan, Verlag: LAP Lambert Academic Publishing, Sprache: Englisch, Rubrik: Wärme- // Energie-, Kraftwerktechnik, Seiten: 56, Informationen: Paperback, Gewicht: 100 gr, Verkäufer: averdo

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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.

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Erscheinungsdatum: 27.05.2014, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques, Autor: Jambulingam, Vikramarajan, Verlag: Anchor Academic Publishing, Sprache: Englisch, Rubrik: Biologie // Sonstiges, Seiten: 68, Informationen: Paperback, Gewicht: 122 gr, Verkäufer: averdo

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Civil engineering structures such as buildings, bridges, stadiums, and offshore structures play an import role in our daily life. However, constructing these structures requires lots of budget. Thus, how to cost-efficiently design structures satisfying all required design constraints is an important factor to structural engineers. Traditionally, mathematical gradient-based optimal techniques have been applied to the design of optimal structures. While, many practical engineering optimal problems are very complex and hard to solve by traditional method. In the past few decades, swarm intelligence algorithms, which were inspired by the social behaviour of natural animals such as fish schooling and bird flocking, were developed because they do not require conventional mathematical assumptions and thus possess better global search abilities than the traditional optimization algorithms and have attracted more and more attention. These intelligent based algorithms are very suitable for continuous and discrete design variable problems such as ready-made structural members and have been vigorously applied to various structural design problems and obtained good results. This book gathers the authors' latest research work related with particle swarm optimizer algorithm and group search optimizer algorithm as well as their application to structural optimal design. The readers can understand the full spectrum of the algorithms and apply the algorithms to their own research problems.

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