Improved Social Spider Algorithm for Large Scale Optimization

Abstract

Heuristic algorithms can give optimal solutions for low, middle, and large scale optimization problems in an acceptable time. The social spider algorithm (SSA) is one of the recent meta-heuristic algorithms that imitate the behaviors of the spider to perform global optimization. The original study of this algorithm was proposed to solve low scale continuous problems, and it is not be solved to middle and large scale continuous problems. In this paper, we have improved the SSA and have solved middle and large scale continuous problems, too. By adding two new techniques to the original SSA, the performance of the original SSA has been improved and it is named as an improved SSA (ISSA). In this paper, various unimodal and multimodal standard benchmark functions for low, middle, and large-scale optimization are studied for displaying the performance of ISSA. ISSA's performance is also compared with the well-known and new evolutionary methods in the literature. Test results show that ISSA displays good performance and can be used as an alternative method for large scale optimization.