# OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm
###### Abstract
So far, various optimization algorithms have been introduced to deal with nonlinear and practical applications, such as genetic algorithm (GA) [13], particle swarm optimization (PSO) [14], whale optimization algorithm (WOA) [15], butterfly optimization algorithm (BOA) [9], sine cosine optimization algorithm (SCA) [16], crow optimization algorithm (CSA) [17], gray wolf optimization algorithm (GWO) [18], and bee colony optimization algorithm (ABC) [19]. In recent years, [PERSON] et al. proposed the Whale Optimization Algorithm (WOA) to optimize TCSC and SVC reactive power planning for the problems of transmission loss and high operating costs. The results show that this method has fewer iterations, will not fall into local minimum, and has good convergence characteristics [15]. [PERSON] and [PERSON] proposed an opposite crow search algorithm for transmission loss, which has better performance in reducing active power and system operation cost [16]. In order to solve the problems of insufficient reactive power and unstable voltage of transmission lines, [PERSON] and [PERSON] et al. proposed an improved sine cosine optimization algorithm, which uses the techniques of the sine cosine algorithm (SCA) and quasi inverse sine cosine algorithm (QOSCA) to minimize transmission losses and operating costs [17]. [PERSON] et al. proposed an improved hunger game search algorithm (IHGS) for solar photovoltaic system parameter identification, which solved the problem of stability of the algorithm when solving the global optimal solution. It shows the feasibility and effectiveness of the improved HGS algorithm [20]. In addition, [PERSON] et al. proposed a new population-based optimization algorithm, the Pelican Algorithm (POA). This algorithm has high development and strong search ability [21]. [PERSON] and [PERSON] put forward the search and rescue optimization algorithm (SAR) for the problem of single objective continuous optimization and demonstrated the feasibility of the algorithm through experiments [22]. [PERSON] and [PERSON] put forward a new solution to the problem that the traditional algorithm population is small and easy to fall into the local optimum. They used chaos mapping and opposite learning to initialize the solution of the given problem and improve the diversity of the population by constantly following the new initial population position through the interference operator. The experimental results show that the proposed method has high efficiency in dealing with the optimal solution problem [23]. In order to improve the performance of PSO on image threshold segmentation, a new OTSU multi-threshold segmentation method based on Improved PSO was proposed. The selection process of thresholds were optimized and compared with many classical PSO methods. The results show that our algorithm reduces the running time and improves the accuracy in threshold segmentation. The main contributions in this paper can be summarized as follows:  
1. An Improved PSO algorithm is proposed, in which (a) chaos optimization was added to reduce premature convergence; (b) elite particle search strategy to particle swarm optimization algorithm was used to reduce optimization time and improve efficiency; (c) learning factors were improved to balance local search and global search. 2. Combining PSO with OTSU algorithm, a gray image segmentation algorithm based on improved particle swarm optimization is proposed. The proposed improved particle swarm optimization segmentation algorithm can search for a more accurate threshold, thus promoting better component division of gray-scale images. 3. Some classical test functions are selected to verify the robustness and development of the algorithm in solving single peak, multi-peak, and multi-peak fixed dimension functions. 4.