Self-adaptive Multi-population Rao Algorithms for Engineering Design Optimization

The performance of various population-based advanced optimization algorithms has been significantly improved by using the multi-population search scheme. The multi-population search process improves the diversity of solutions by dividing the total population into a number of sub-populations groups to search for the best solution in different areas of a search space. This paper proposes improved optimization algorithms based on self-adaptive multi-population for solving engineering design optimization problems. These proposed algorithms are based on Rao algorithms which are recently proposed simple and algorithm-specific parameter-less advanced optimization algorithms. In this work, Rao algorithms are upgraded with the multi-population search process to enhance the diversity of search. The number of sub-populations is changed adaptively considering the strength of solutions to control the exploration and exploitation of the search process. The performance of proposed algorithms is investigated on 25 unconstrained benchmark functions and 14 complex constrained engineering design optimization problems. The results obtained using proposed algorithms are compared with the various advanced optimization algorithms. The comparison of results shows the effectiveness of proposed algorithms for solving engineering design optimization problems. The significance of the proposed methods has proved using a well-known statistical test known as “Friedman test.” Furthermore, the convergence plots are illustrated to show the convergence speed of the proposed algorithms.