Termite alate optimization algorithm: a swarm-based nature inspired algorithm for optimization problems

Swarm based algorithms play a very important role in solving optimization problems as these algorithms perform better than the traditional techniques. Various such swarm-based algorithms are developed and experimented with by previous researchers. However, the complexity and number of input parameters to be tuned are the significant disadvantages for most of these swarm-based algorithms. In this present study, a new swarm-based nature inspired algorithm, called Termite Alate Optimization Algorithm (TAOA), is proposed based on the phototactic activity of a termite alate group. The advantage of this algorithm is its faster convergence rate with effective exploration and exploitation capability. The algorithm also has a moderate number of process parameters and computational complexity. To evaluate the capability total 30 benchmark instances and 5 real-life problems are solved by this algorithm. Apart from the benchmark and real-life instances, the algorithm is also applied in the flow shop problem to evaluate its effectiveness. Finally, a comparison between the results of TAOA and other existing algorithms is carried out, which validates its ability.