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Mohamed, A. W., A. A. Hadi, A. K. Mohamed, and N. H. Awad, "Evaluating the Performance of Adaptive GainingSharing Knowledge Based Algorithm on CEC 2020 Benchmark Problems", 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1 - 8, 19-24 July 2020, Submitted. Abstract
Song, Y., D. Wu, A. W. Mohamed, X. Zhou, B. Zhang, and W. Deng, "Enhanced Success History Adaptive DE for Parameter Optimization of Photovoltaic Models", Complexity, vol. 2021: Hindawi, pp. 6660115, 2021. AbstractWebsite

In the past few decades, a lot of optimization methods have been applied in estimating the parameter of photovoltaic (PV) models and obtained better results, but these methods still have some deficiencies, such as higher time complexity and poor stability. To tackle these problems, an enhanced success history adaptive DE with greedy mutation strategy (EBLSHADE) is employed to optimize parameters of PV models to propose a parameter optimization method in this paper. In the EBLSHADE, the linear population size reduction strategy is used to gradually reduce population to improve the search capabilities and balance the exploitation and exploration capabilities. The less and more greedy mutation strategy is used to enhance the exploitation capability and the exploration capability. Finally, a parameter optimization method based on EBLSHADE is proposed to optimize parameters of PV models. The different PV models are selected to prove the effectiveness of the proposed method. Comparison results demonstrate that the EBLSHADE is an effective and efficient method and the parameter optimization method is beneficial to design, control, and optimize the PV systems.

Wu, W., H. Ouyang, A. W. Mohamed, C. Zhang, and S. Li, Enhanced harmony search algorithm with circular region perturbation for global optimization problems, , vol. 50, issue 3, pp. 951 - 975, 2020. AbstractWebsite

To improve the searching effectiveness of the harmony search (HS) algorithm, an enhanced harmony search algorithm with circular region perturbation (EHS_CRP) is proposed in this paper. In the EHS_CRP algorithm, a global and local dimension selection strategy is designed to accelerate the search speed of the algorithm. A selection learning operator based on the global and local mean level is proposed to improve the balance between exploration and exploitation. Circular region perturbation is employed to avoid the algorithm stagnation and get a better exploration region. To assess performance, the proposed algorithm is compared with 10 state-of-the-art swarm intelligent approaches in a large set of global optimization problems. The simulation results confirm that EHS_CRP has a significant advantage in terms of accuracy, convergence speed, stability and robustness. Moreover, EHS_CRP performs better than other tested methods in engineering design optimization problems. Thus, the EHS_CRP algorithm is a viable and reliable alternative for some difficult and multidimensional real-world problems.

Mohamed, A. W., A. K. Mohamed, E. Z. Elfeky, and M. Saleh, "Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems", International Journal of Applied Metaheuristic Computing (IJAMC), vol. 10, issue 1, Hershey, PA, USA, IGI Global, pp. 1 - 28, 2019. AbstractWebsite

The performance of Differential Evolution is significantly affected by the mutation scheme, which attracts many researchers to develop and enhance the mutation scheme in DE. In this article, the authors introduce an enhanced DE algorithm (EDDE) that utilizes the information given by good individuals and bad individuals in the population. The new mutation scheme maintains effectively the exploration/exploitation balance. Numerical experiments are conducted on 24 test problems presented in CEC'2006, and five constrained engineering problems from the literature for verifying and analyzing the performance of EDDE. The presented algorithm showed competitiveness in some cases and superiority in other cases in terms of robustness, efficiency and quality the of the results.

Mohamed, A. K., A. W. Mohamed, E. Z. Elfeky, and M. Saleh, "Enhancing AGDE Algorithm Using Population Size Reduction for Global Numerical Optimization", The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), Cham, Springer International Publishing, pp. 62 - 72, 2018. Abstract

Adaptive guided differential evolution algorithm (AGDE) is a DE algorithm that utilizes the information of good and bad vectors in the population, it introduced a novel mutation rule in order to balance effectively the exploration and exploitation tradeoffs. It divided the population into three clusters (best, better and worst) with sizes 100p%, NP-2 * 100p% and 100p% respectively. Where p is the proportion of the partition with respect to the total number of individuals in the population (NP). AGDE selects three random individuals, one of each partition to implement the mutation process. Besides, a novel adaptation scheme was proposed in order to update the value of crossover rate without previous knowledge about the characteristics of the problems. This paper introduces enhanced AGDE (EAGDE) with non-linear population size reduction, which gradually decreases the population size according to a non-linear function. Moreover, a newly developed rule developed to determine the initial population size, that is related to the dimensionality of the problems.

Mohamed, A. W., An efficient modified differential evolution algorithm for solving constrained non-linear integer and mixed-integer global optimization problems, , vol. 8, issue 3, pp. 989 - 1007, 2017. AbstractWebsite

In this paper, an efficient modified Differential Evolution algorithm, named EMDE, is proposed for solving constrained non-linear integer and mixed-integer global optimization problems. In the proposed algorithm, new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best,better and the worst individuals among the three randomly selected vectors is introduced. The proposed novel approach to mutation operator is shown to enhance the global and local search capabilities and to increase the convergence speed of the new algorithm compared with basic DE. EMDE uses Deb’s constraint handling technique based on feasibility and the sum of constraints violations without any additional parameters. In order to evaluate and analyze the performance of EMDE, Numerical experiments on a set of 18 test problems with different features, including a comparison with basic DE and four state-of-the-art evolutionary algorithms are executed. Experimental results indicate that in terms of robustness, stability and efficiency, EMDE is significantly better than other five algorithms in solving these test problems. Furthermore, EMDE exhibits good performance in solving two high-dimensional problems, and it finds better solutions than the known ones. Hence, EMDE is superior to the compared algorithms.