An Improved Squirrel Search Algorithm with Reproduction and Competition Mechanisms

The performance of the recently-proposed Squirrel Search Algorithm (SSA) is improved in this paper. SSA is a swarm intelligence algorithm that simulates the dynamic foraging behavior of squirrels. The traditional SSA is prone to premature convergence when solving optimization problems. This work proposed a propagation and diffusion search mechanism to alleviate these drawbacks by expand the search space using the Invasive Weed Algorithm (IWO). The proposed algorithm, which called SSIWO, has high ability to improve the exploration and local optimal avoidance of SSA. In order to investigate the performance proposed SSIWO algorithm, several experiments are conducted on eight benchmark functions and using three algorithms. The experimental results show the superior performance of the proposed SSIWO algorithm to determine the optimal solutions of the benchmark function problems.