A step-size follow-the-leader optimization algorithm with an improved step parameters

Follow the leader (FTL) algorithm is a newly developed optimization algorithm inspired by a sheep’s movement within a flock. FTL has been successfully implemented to solve power prediction problems. However, the probability of falling in local optima is high due to randomness in the step parameter. This paper proposes a step-size follow-the-leader (SFTL) algorithm with decreasing and increasing step-size combinations. The improved step parameter tunes the search space by generating a new solution to improve the accuracy and convergence rate of the FTL algorithm. Four different FTL variants have been presented in this paper to show the impact of the dynamic step-size parameter. FTL improvement is verified by testing SFTL over thirty-two fixed unimodal, unimodal, fixed multimodal, and multimodal benchmark functions. The computational results indicate that SFTL significantly improves the basic FTL algorithm and converges early compared to other algorithms. SFTL has also been tested on five real engineering design problems and obtained results show that SFTL outperformed FTL and other popular optimization algorithms.