Research Article Parameter Optimization of Single-Diode Model of Photovoltaic Cell Using Memetic Algorithm Yourim Yoon 1 and Zong Woo Geem 2 1 Department of Computer Engineering, Gachon University, 1342 Seongnam Daero, Seongnam 461-701, Republic of Korea 2 Department of Energy IT, Gachon University, 1342 Seongnam Daero, Seongnam 461-701, Republic of Korea Correspondence should be addressed to Zong Woo Geem; zwgeem@gmail.com Received 26 September 2015; Revised 5 November 2015; Accepted 11 November 2015 Academic Editor: Mahmoud M. El-Nahass Copyright © 2015 Y. Yoon and Z. W. Geem. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis study proposes a memetic approach for optimally determining the parameter values of single-diode-equivalent solar cell model. Te memetic algorithm, which combines metaheuristic and gradient-based techniques, has the merit of good performance in both global and local searches. First, 10 single algorithms were considered including genetic algorithm, simulated annealing, particle swarm optimization, harmony search, diferential evolution, cuckoo search, least squares method, and pattern search; then their fnal solutions were used as initial vectors for generalized reduced gradient technique. From this memetic approach, we could further improve the accuracy of the estimated solar cell parameters when compared with single algorithm approaches. 1. Introduction Determining the parameter values of photovoltaic (PV) cell models is very important when designing solar cells and esti- mating their performance. Te key parameters that represent the behavior of solar cells include generated photocurrent, saturation current, series resistance, shunt resistance, and ideality factor [1]. Estimating these parameters accurately is essential for precise modeling and accurate performance evaluation of solar cells. Several models have been proposed to describe the behavior of solar cells using current-voltage (-) relation- ship [2–4]. Te - curve of a solar cell has nonlinear characteristics determined by the solar cell parameters. Tese models generally consist of analytical equations based on a physical description that formulate PV-generated current with the technical characteristics and the environmental variables including the operating voltage, the ambient tem- perature, and the irradiance [5]. Among numerous modeling approaches, the single-diode model (SDM) is the most widely utilized solar cell model in the literature. A general SDM includes fve parameters: photocurrent, saturation current, diode ideality constant, series resistance, and shunt resis- tance. So far various computational intelligence methods, such as genetic algorithm, particle swarm optimization, simulated annealing, and harmony search, have been proposed for opti- mal estimation of solar cell parameters. Many studies have aimed to overcome the shortcomings of the conventional deterministic algorithms and to investigate the efciency and applicability of the algorithms. Hybrid methods combining two or more metaheuristic algorithms also have been applied to explore the capability of stochastic artifcial intelligence algorithms in estimating solar cell parameters. Tese algo- rithms could fnd relevant parameter values by minimizing the root mean square error (RMSE) as the objective function in the optimization process. Up to now, metaheuristic algorithms have shown a higher level of applicability in estimating solar cell parameters with fne performance. Nonetheless, we also presume that a memetic approach, which combines the well-developed evolutionary frameworks with gradient-based local search algorithm, can provide an opportunity for better solu- tions. Trough this memetic combination, it can be seen that the accuracy represented by RMSE can be further improved because metaheuristic algorithm can be reinforced by calculus-based method in terms of local search perfor- mance and calculus-based method can be reinforced by Hindawi Publishing Corporation International Journal of Photoenergy Volume 2015, Article ID 963562, 7 pages http://dx.doi.org/10.1155/2015/963562