A Hybrid Evolutionary Algorithm and its Application to Parameter Identification of Rolling Elements Bearings

Abstract

Genetic algorithms (GAs) are powerful stochastic search techniques and are the most widely known types of evolutionary algorithms (EAs). This method performs a search by evolving a population of candidate solutions through the use of non-deterministic operators and by improving incrementally the individuals forming the population by mechanisms inspired from those of genetics (e.g. crossover and mutation). They are known to offer significant advantages over traditional methods by using simultaneously several search principles and heuristics, of which the most important ones are: population-wide search, continuous balance between exploitation (convergence) and exploration (maintained diversity) and the principle of building-block combination. However, GA can suffer from excessively slow convergence before providing an accurate solution. This is because of its fundamental requirement of using minimal prior knowledge without exploiting local information. Since the introduction of global search algorithms in engineering applications, many modified versions of GA have been reported to reduce the searching time and to raise the global search capability. Many researchers have proposed improved versions of GA which GA operator works adaptively (Wu et al., 1999; He et al., 2001; Fung et al., 2002). A local search or meta-heuristic algorithm has been incorporated into GA to improve the algorithm (Renders & Flasse, 1996; Berger et al., 1999; Lee et al., 2001; Hsiao et al., 2001; Hagenman et al., 2003; Jiang et al., 2003). The combined GA-SA algorithm has been introduced to improve the efficiency of the global search (Roach & Nagi, 1996; Yu et al., 2000; Ong et al., 2002; Liu et al., 2002; Ponnambalam et al., 2003). In the first half of this chapter, a new hybrid evolutionary algorithm known as clusteringbased hybrid evolutionary algorithm (CHEA) is introduced (Kim et al., 2006). This algorithm utilizes the GA’s grouping property which involves gathering a number of individuals around the global candidate according to the generation. Clustering of individuals using artificial neural network (ANN) is incorporated into the GA to evaluate the stage of maturity of genetic evolution and to deal with statistical data of each cluster. After clustering, a local search is carried out for each cluster to accelerate the convergence process and to judge the convexity of each cluster. Finally, an efficient random search is adapted for searching the potential global candidate which may be missed in GA and local search. The efficiency of the proposed algorithm is then verified by applying it to three wellO pe n A cc es s D at ab as e w w w .ite ch on lin e. co m