A cloud model based symbiotic organism search algorithm for DG allocation in radial distribution network

Recently metaheuristic algorithms have become popular in solving DG placement problems due to its advantages of simple implementation and ability to find the near optimal solution for complex and large-scale optimization problems. Symbiotic organism search (SOS) is one of the latest metaheuristic algorithms introduced to solve DG placement problems. Unlike many other metaheuristic algorithms, SOS is simple to implement and does not use any control parameters which lead to enhancing performance stability. However, like other optimization algorithms SOS suffers with local optimal and stagnations which affects its accuracy and convergence especially in solving real world problems like DG placement problem. This work attempts to enhance performance of SOS by combining with cloud-based model. The proposed algorithm is named cloud based model symbiotic organism search (CMSOS). In CMSOS, Cloud-based theories have been used to generate random number operator in mutualism phase of the original SOS. To assess the performance of CMSOS in solving optimization problems 26 benchmark functions with different dimensions have been used. The performance of the proposed algorithm has been tested for real world DG placement problems. The performed analysis such as statistical, convergence and complexity measures show superiority of the proposed algorithm compare to the studied algorithms.