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Adaptive guided differential evolution algorithm with novel mutation for numerical optimization

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Abstract

This paper presents adaptive guided differential evolution algorithm (AGDE) for solving global numerical optimization problems over continuous space. In order to utilize the information of good and bad vectors in the DE population, the proposed algorithm introduces a new mutation rule. It uses two random chosen vectors of the top and the bottom 100p% individuals in the current population of size NP while the third vector is selected randomly from the middle [NP-2(100p %)] individuals. This new mutation scheme helps maintain effectively the balance between the global exploration and local exploitation abilities for searching process of the DE. Besides, a novel and effective adaptation scheme is used to update the values of the crossover rate to appropriate values without either extra parameters or prior knowledge of the characteristics of the optimization problem. In order to verify and analyze the performance of AGDE, Numerical experiments on a set of 28 test problems from the CEC2013 benchmark for 10, 30, and 50 dimensions, including a comparison with classical DE schemes and some recent evolutionary algorithms are executed. Experimental results indicate that in terms of robustness, stability and quality of the solution obtained, AGDE is significantly better than, or at least comparable to state-of-the-art approaches.

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Acknowledgements

The author would like to thank Deanship of Scientific Research at Majmaah University for supporting this work.

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Authors and Affiliations

  1. Operations Research Department, Institute of Statistical Studies and Research, Cairo University, Giza, 12613, Egypt

    Ali Wagdy Mohamed

  2. College of Science and Humanities, Majmaah University, P.O Box 66, Majmaah, 11952, Kingdom of Saudi Arabia

    Ali Khater Mohamed

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  1. Ali Wagdy Mohamed
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Correspondence to Ali Wagdy Mohamed.

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Ali Wagdy Mohamed and Ali Khater Mohamed declare that he has no conflict of interest.

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Mohamed, A.W., Mohamed, A.K. Adaptive guided differential evolution algorithm with novel mutation for numerical optimization. Int. J. Mach. Learn. & Cyber. 10, 253–277 (2019). https://doi.org/10.1007/s13042-017-0711-7

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