Abstract
Differential Evolution (DE) has been regarded as one of the excellent optimization algorithm in the science, computing and engineering field since its introduction by Storm and Price in 1995. Robustness, simplicity and easiness to implement are the key factors for DE’s success in optimization of engineering problems. However, DE experiences convergence and stagnation problems. This paper focuses on DE convergence speed improvement based on introduction of newly developed mutation schemes strategies with reference to DE/rand/1 on account and tuning of control parameters. Simulations are conducted using benchmark functions such as Rastrigin, Ackley and Sphere, Griewank and Schwefel function. The results are tabled in order to compare the improved DE with the traditional DE.
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Acknowledgments
This research is supported partially by South African National Research Foundation Grants (No. 112108 and 112142), and South African National Research Foundation Incentive Grant (No. 95687), Eskom Tertiary Education Support Programme Grants (Z. Wang, Y. Sun), Research grant from URC of University of Johannesburg.
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Saveca, J., Wang, Z., Sun, Y. (2018). Improved Differential Evolution Based on Mutation Strategies. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_23
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DOI: https://doi.org/10.1007/978-3-319-93815-8_23
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