A Hybrid of Genetic Algorithm and Particle Swarm

An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the

best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation op- eration on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural net- work, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recur- rent fuzzy network design, a Takagi–Sugeno–Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority. Index Terms—Dynamic plant control, elite strategy, recurrent neural/fuzzy work, temporal sequence production. I. INTRODUCTION T HE ADVENT OF evolutionary computation has inspired new resources for optimization problem solving, such as the optimal design of neural networks and fuzzy systems. In contrast to traditional computation systems which may be good at accurate and exact computation, but have brittle operations, evolutionary computation provides a more robust and efficient approach for solving complex real-world problems [1]–[3]. Many evolutionary algorithms, such as genetic algorithm (GA) [4], genetic programming [5], evolutionary programming [6], and evolution strategies [7], have been proposed. Since they are heuristic and stochastic, they are less likely to get stuck in local minimum, and they are based on populations made up of individuals with a specified behavior similar to biological phenomenon. These common characteristics led to the development of evolutionary computation as an increasing important field. Manuscript received February 14, 2003; revised May 20, 2003. This work was supported by the National Science Council, Taiwan, R.O.C., under Grant NSC 91-2213-E-055-022. This paper was recommended by Associate Editor L. O. Hall. The author is with the Department of Electrical Engineering, National Chung Hsing University, Taichung, 402 Taiwan, R.O.C. Digital Object Identifier 10.1109/TSMCB.2003.818557 Among existing evolutionary algorithms, the most well- known branch is GA. GAs are stochastic search procedures based on the mechanics of natural selection, genetics, and evolution [4]. Since they simultaneously evaluate...

Website: bitbucket.org | Filesize: -
No of Page(s): 10
Download A Hybrid of Genetic Algorithm and Particle Swarm ... - Bitbucket.pdf