Nature-inspired optimization algorithms have attracted significant attention from researchers during the past decades due to their applicability to solving the challenging optimization problems, efficiently. Many intelligent systems require an excellent constrained optimization scheme to act as an artificially intelligent system. Artificial electric field algorithm (AEFA) is an intelligently designed artificial system that deals with the purpose of function optimization. AEFA works on the principle of Coulombs’ law of electrostatic force and Newtons’ law of motion. The present article extends the AEFA algorithm for constrained optimization problems by introducing the new velocity and position bound strategies. These bounds lead the particle to interact with each other within the domain of the problem, and they are allowed to learn from the problem space individually. They also help to make a better balance between exploration and exploitation by controlling the position update of the particles. The challenging IEEE CEC 2017 constrained benchmark set of 28 problems, and five multidimensional non-linear structural design optimization problems are solved using AEFA-C, which tests the effectiveness and the efficiency of the proposed scheme. The comparative study of AEFA-C is performed with nine state-of-art algorithms, including some IEEE CEC 2017 competitors. The comparative study, statistical analysis, and the findings suggest that the proposed AEFA-C is an efficient constrained optimizer.
Anupam Yadav (2020). AEFA-C for Constrained Optimization (https://www.mathworks.com/matlabcentral/fileexchange/74361-aefa-c-for-constrained-optimization), MATLAB Central File Exchange. Retrieved .
Anita, et al. “Artificial Electric Field Algorithm for Engineering Optimization Problems.” Expert Systems with Applications, vol. 149, Elsevier BV, July 2020, p. 113308, doi:10.1016/j.eswa.2020.113308.
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