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Continuous functions minimization by dynamic random search technique

Hamzaçebi C. | Kutay F.

Article | 2007 | Applied Mathematical Modelling31 ( 10 ) , pp.2189 - 2198

Random search technique is the simplest one of the heuristic algorithms. It is stated in the literature that the probability of finding global minimum is equal to 1 by using the basic random search technique, but it takes too much time to reach the global minimum. Improving the basic random search technique may decrease the solution time. In this study, in order to obtain the global minimum fastly, a new random search algorithm is suggested. This algorithm is called as the Dynamic Random Search Technique (DRASET). DRASET consists of two phases, which are general search and local search based on general solution. Knowledge related to . . . the best solution found in the process of general search is kept and then that knowledge is used as initial value of local search. DRASET's performance was experimented with 15 test problems and satisfactory results were obtained. © 2006 Elsevier Inc. All rights reserved Daha fazlası Daha az

Improving genetic algorithms' performance by local search for continuous function optimization

Hamzaçebi C.

Article | 2008 | Applied Mathematics and Computation196 ( 1 ) , pp.309 - 317

The genetic algorithms (GAs) can be used as a global optimization tool for continuous and discrete functions problems. However, a simple GA may suffer from slow convergence, and instability of results. GAs' problem solution power can be increased by local searching. In this study a new local random search algorithm based on GAs is suggested in order to reach a quick and closer result to the optimum solution. © 2007 Elsevier Inc. All rights reserved.

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