Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking

Abstract

A new optimization algorithm called Hybrid Sine-Cosine Algorithm with Differential Evolution algorithm (Hybrid SCA-DE) is proposed in this paper for solving optimization problems and object tracking. The proposed hybrid algorithm has better capability to escape from local optima with faster convergence than the standard SCA and DE. The effectiveness of this algorithm is evaluated using 23 benchmark functions, which are divided into three groups: unimodal, multimodal, and fixed dimension multimodal functions. Statistical parameters have been employed to observe the efficiency of the Hybrid SCA-DE qualitatively and results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms. The proposed algorithm is applied for object tracking as a real thought-provoking case study. To demonstrate the tracking ability of a Hybrid SCA-DE-based tracker, a comparative study of tracking accuracy and speed of the Hybrid SCA-DE-based tracker with four other trackers, namely, Particle Filter, Scale-invariant feature transform, Particle swarm optimization and Bat algorithm are presented. Comparative results show that the Hybrid SCA-DE-based tracker can robustly track an arbitrary target in various challenging conditions than the other trackers.