Abstract
The optimality ability of any optimization algorithm for different problems is a very important aspect. In this article, we check the optimality ability of newly designed population-based artificial electric field algorithm (AEFA) over 30 unconstrained optimization problems of CEC 2017 benchmark set. The computational results of AEFA are compared with other existing algorithms along with their statistical validation. We also performed the numerical convergence of AEFA over the selected benchmark set which ensure the fast convergence of AEFA over other existing algorithms. This article demonstrates the superiority of AEFA over other existing algorithms.
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Anita, Yadav, A., Kumar, N. (2020). Artificial Electric Field Algorithm for Solving Real Parameter CEC 2017 Benchmark Problems. In: Nagar, A., Deep, K., Bansal, J., Das, K. (eds) Soft Computing for Problem Solving 2019 . Advances in Intelligent Systems and Computing, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-3290-0_13
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DOI: https://doi.org/10.1007/978-981-15-3290-0_13
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