Abhishek Kumar

TL;DR: A set of 57 real-world Constrained Optimization Problems are described and presented as a benchmark suite to validate the COPs and reveal that the selected problems are indeed challenging to these algorithms, which have been shown to solve many synthetic benchmark problems easily.

TL;DR: A new retreat phase called Covariance Matrix Adapted Retreat Phase (CMAR), which uses covariance matrix to generate a new solution and thus improves the local search capability of EBO and is competitive with the compared algorithms.

TL;DR: The obtained results of the proposed Spherical Search algorithm are compared with the results of other well-known optimization algorithms and their advanced variants: Particle Swarm Optimization, Differential Evolution, and Covariance Matrix Adapted Evolution Strategy and the comparative analysis reveals that the performance of SS is quite competitive with respect to the other peer algorithms.

TL;DR: A new variant of the recently developed Spherical Search algorithm is introduced, which contains a powerful and effective self-adaptation structure to enhance the performance.

TL;DR: The experimental results show that the performance of the proposed CMA-ES variant with linear timing complexity is better than several other state-of-the-art algorithms in terms of constraint handling and robustness.