هم افزایی الگوریتم تکاملی و فرایند اجتماعی و سیاسی برای بهینه سازی جهانی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|4143||2010||8 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 37, Issue 5, May 2010, Pages 3706–3713
This paper proposes a hybrid approach by combining the evolutionary optimization based genetic algorithm (GA) and socio-political process based colonial competitive algorithm (CCA). The performance of hybrid algorithm is illustrated using standard test functions in comparison to basic CCA method. Since the CCA method is newly developed, very little research work has been undertaken to deal with curse of dimensionality and to improve the convergence speed and accuracy of the basic CCA algorithm. The proposed CCA–GA algorithm is then used to tune a PID controller for a real time ball and beam system. Simulation results were reported and the hybrid algorithm indeed has established superiority over the basic algorithms with respect to set of functions considered and it can easily be extended for other global optimization problems.
The era of evolutionary computation started with genetic algorithms in the past three decades. Amounts of applications have benefited from the utilization of GA (Chaiyaratana & Zalzala, 1997). Potter and De Jong (1994) have demonstrated the use of co-operative co-evaluation GA in multivariable functional optimization. Breeder genetic algorithm (BGA) (Muhlenbein & Schlierkamp-Voosen, 1993) is first introduced by Muhlenbein et al. The major difference lies in the method of selection in comparison to simple GA. A typical task of GA is to find the best set of values in a predefined set of free parameters associated with either a process model or a control vector. The GA uses the basic reproduction operators such as crossover and mutation to produce the genetic composition of a population. Efforts are being made in the enhancement of conventional algorithm (Chen, 2008, Francisco and Manuel, 2000, Yang and Tinos, 2008 and Zhang et al., 2008). GA with neural network and fuzzy control (Siddique & Tokhi, 2002) are used extensively to optimize nonlinear and multivariable systems. In the past, researches were carried out in using hybrid genetic algorithm approaches for optimization problems. Buczak and Uhrig proposed a novel hierarchal fuzzy-genetic (Buczak & Uhrig, 1996) information fusion technique. Constraint handling is one of the major concern for solving the optimization problems using GA. Chootinan and Chen proposed a gradient information (Chootinan & Chen, 2006), derived from the constraint set, to systematically repair infeasible solutions. Though the GA methods were successful to solve complex optimization problems, recent search has identified some deficiencies in GA performance (Eberhart & Shi, 1998). This degradation in efficiency is apparent in applications with highly epistatic objective functions (i.e. where the parameters being optimized are highly correlated), the genetic operators alone cannot ensure better fitness of offspring because chromosomes in the population have similar structure and their average fitness are high toward the end of evolutionary process. Research is still on to increase the efficiency of GA by hybridization (Lee & Lee, 2005). Yang and Tinos (2007) proposes a hybrid immigrants scheme that combines the concept of elitism, dualism and random immigrants to address dynamic optimization problems. Esmaeil, Farzad, Ramin, and Caro (2008) first proposed the colonial competitive algorithm (CCA) in 2008. Unlike the current evolutionary algorithms, such as genetic algorithm (GA) and simulated annealing (SA) (Mantawy, Abdel-Magid, & Abido, 1999) that are computer simulation of natural processes such as natural evolution and annealing processes in materials, CCA uses imperialism and imperialistic competition, socio-political evolution process, as source of inspiration. The comparison between CCA and GA was illustrated by designing a PID controller for distillation column system (DCS). CCA proved to be superior to GA for a MIMO model. In this article we have come up with a hybrid optimization technique, which synergistically couples the GA and CCA. The proposed algorithm is validated using test functions and for PID controller tuning. The rest of the paper is organized as follows: Section 2 provides a brief literature overview of the genetic algorithm followed by the proposed hybrid approach based on CCA and GA. The algorithm is then validated using standard test functions and implemented on practical ball–beam system in Section 3 followed by conclusions and future work.
نتیجه گیری انگلیسی
This paper proposes a novel hybrid approach consisting GA (genetic algorithm) and CCA (colonial competitive algorithm) and its performance is evaluated using various test functions. The improvement is shown in terms of convergence rate of the objective function towards the optimality in comparison to CCA for higher dimension. It was analyzed that as the dimension increases, the solution converges to suboptimal region in case of CCA algorithm and requires more number of decades. Also, the proposed CCA–GA algorithm is implemented on a real time ball–beam system supplied by Googol Technology for tuning the PID controller. As evident from the graphical and empirical results, the suggested hybrid system performed well. Our future research would include the analysis and performance of fuzzy controller in the presence of output noise or input disturbances as in case of pulse input the overshoot is large in comparison to step input of same desired position.