EST-TSA: An effective search tendency based to tree seed algorithm

As a population-based stochastic search algorithm for continuous optimization problems, tree−seed algorithm (TSA) fails to balance capability of the exploitation and exploration of the algorithm effectively. Therefore, TSA has certain defects in local search capability. This paper proposes improvements of TSA in local search capability. Although TSA can achieve good performance in general, it does not fully consider the role of the current optimal position of the population in solving optimization problems. This slows down the convergence speed of the algorithm. This paper redesigns the balance rule between exploitation and exploration and improves the original position updating formula of TSA. EST-TSA can converge to the global optimal solution more effectively in the search process, which greatly strengthens the local search capability. Experimental results show that EST-TSA has better local search capability compared with the basic TSA. The improvements proposed in this paper are significant for TSA performance.