Hybrid Genetic: Particle Swarm Optimization Algorithm

Hybrid Genetic: Particle Swarm Optimization Algorithm D.H. Kim, A. Abraham, and K. Hirota Summary. This chapter proposes a hybrid approach by combining a Euclidian distance (EU) based genetic algorithm (GA) and particle swarm optimization (PSO) method. The perfor- mance of the hybrid algorithm is illustrated using four test functions. Proportional integral derivative (PID) controllers have been widely used in industrial systems such as chemical process, biomedical process, and in the main steam temperature control system of the thermal

power plant. Very often, it is difficult to achieve an optimal PID gain without prior expert knowledge, since the gain of the PID controller has to be manually tuned by a trial and er- ror approach. Using the hybrid EU–GA–PSO approach, global and local solutions could be simultaneously found for optimal tuning of the controller parameters. 7.1 Introduction During the last decade, genetic algorithm-based approaches have received increased attention from the engineers dealing with problems, which could not be solved using conventional problem solving techniques. A typical task of a GA in this context is to find the best values of a predefined set of free parameters associated with either a process model or a control vector. A possible solution to a specific problem can be encoded as an individual (or a chromosome), which consists of group of genes. Each individual represents a point in the search space and a possible solution to the problem can be formulated. A population consists of a finite number of individu- als and each individual is decided by an evaluating mechanism to obtain its fitness value. Using this fitness value and genetic operators, a new population is generated iteratively which is referred to as a generation. The GA uses the basic reproduction operators such as crossover and mutation to produce the genetic composition of a population. Many efforts for the enhancement of conventional genetic algorithms have been proposed. Among them, one category focuses on modifying the structure of the population or on the individual’s role while another category is focused on modification/efficient control of the basic operations, such as crossover or mutation, of conventional genetic algorithms [9]. The proportional integral derivative (PID) controller has been widely used ow- ing to its simplicity and robustness in chemical process, power plant, and electrical D.H. Kim et al.: Hybrid Genetic: Particle Swarm Optimization Algorithm, Studies in Computational Intelligence (SCI) 75, 147–170 (2007) www.springerlink.com c©Springer-Verlag Berlin Heidelberg 2007 148 D.H. Kim et al. systems [1]. Its popularity is also due to its easy implementation in hardware and software. However, using only the P,I,D parameters, it is often very difficult to con- trol a plant with complex dynamics, such as large dead time, inverse response, and for power plants having a high nonlinear characteristics [5]. Recently, there has been a growing interest in the usage of intelligent approaches such as fuzzy inference sys- tems, neural network, evolutionary algorithms, and...

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