论文信息 - Efficient hybrid distributed genetic algorithms for wind turbine positioning in large wind farms

Efficient hybrid distributed genetic algorithms for wind turbine positioning in large wind farms

An efficient hybrid distributed genetic algorithm is proposed to determine the proper number and locations of wind turbines in large wind farms. The objective of this optimal process is to find a solution that maximizes the annual profit obtained from a wind farm. It is well-known that genetic algorithms are good for global searches, but are weak for local searches. To improve the performance of finding the optimal solution in a large search space, the hybrid methodology combines a distributed genetic algorithm and steepest ascent hill-climbing local search algorithms. The hill-climbing algorithm provides a powerful strategy for searching the local optimal solution by exploring the neighborhood of the current state. In this paper, the hill-climbing algorithm is further enhanced by a heuristic method to reduce the execution time for finding the optimal value. Test results show that this proposed hybrid distributed genetic algorithm adequately demonstrates its effectiveness in solution quality and execution time.

Hou-Sheng Huang

[1] Erick Cantú-Paz,et al. A Survey of Parallel Genetic Algorithms , 2000 .

[2] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3] Bryan A. Norman,et al. Heuristic methods for wind energy conversion system positioning , 2004 .

[4] M. Y. Hussaini,et al. Placement of wind turbines using genetic algorithms , 2005 .

[5] Jean-Michel Renders,et al. Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[6] Enrique Alba,et al. A survey of parallel distributed genetic algorithms , 1999 .

[7] Goldberg,et al. Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[8] Carlo Poloni,et al. Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm , 1994 .

[9] J. Højstrup,et al. A Simple Model for Cluster Efficiency , 1987 .

[10] D. E. Goldberg,et al. Genetic Algorithms in Search , 1989 .

[11] Erick Cantú-Paz,et al. Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms , 2001, J. Heuristics.

[12] Reiko Tanese,et al. Distributed Genetic Algorithms , 1989, ICGA.

[13] Andrew Hendriks,et al. A distributed genetic algorithm for RNA secondary structure prediction , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..