Improved Shuffled Jaya algorithm for sizing optimization of skeletal structures with discrete variables

Abstract

Jaya algorithm is a simple and efficient population-based metaheuristic algorithm. Besides its simplicity, it has free from any algorithm-specific parameters. Although it has these advantages, the Jaya algorithm suffers from some shortcomings including unwanted premature convergence and the possibility of being trapped in local minima due to insufficient population diversity. To alleviate these handicaps, this paper proposes an Improved Shuffled based Jaya (IS-Jaya) algorithm. The proposed optimization method uses the concept of shuffling process to gain superior exploration capability in the search mechanism. A mechanism that causes to escape from local minima is also incorporated into the original Jaya algorithm. The efficiency of the IS-Jaya algorithm is tested on discrete optimization problems and compared to those of Jaya algorithm, self-adaptive multi-population-based Jaya (SAMP-Jaya), and some other state-of-art optimization methods. Optimization results show that the proposed optimization method can be an effective tool for solving discrete size optimization of skeletal structures.