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A Simple Butterfly Particle Swarm Optimization Algorithm with the Fitness-based Adaptive Inertia Weight and the Opposition-based Learning Average Elite Strategy

Tong L., Dong M., Ai B., Jing C.

Fundamenta Informaticae

2018

Vol. 163, nr 2

205--223

Particle swarm optimization (PSO) is a population-based stochastic optimization technique that can be applied to solve optimization problems. However, there are some defects for PSO, such as easily trapping into local optimum, slow velocity of convergence. This paper presents the simple butterfly particle swarm optimization algorithm with the fitness-based adaptive inertia weight and the opposition-based learning average elite strategy (SBPSO) to accelerate convergence speed and jump out of local optimum. SBPSO has the advantages of the simple butterfly particle swarm optimizer to increase the probability of finding the global optimum in the course of searching. Moreover, SBPSO benefits from the simple particle swarm (sPSO) to accelerate convergence speed. Furthermore, SBPSO adopts the opposition-based learning average elite to enhance the diversity of the particles in order to jump out of local optimum. Additionally, SBPSO generates the fitness-based adaptive inertia weight ω to adapt to the evolution process. Eventually, SBPSO presents a approach of random mutation location to enhance the diversity of the population in case of the position out of range. Experiments have been conducted with eleven benchmark optimization functions. The results have demonstrated that SBPSO outperforms than that of the other five recent proposed PSO in obtaining the global optimum and accelerating the velocity of convergence.

Radar target detection based on methods of image pattern matching

Tong L., Heymann F., Noack T.

Zeszyty Naukowe / Akademia Morska w Szczecinie

2013

nr 36 (108) z. 1

162--167

This paper studies the performance of pattern matching algorithms with the goal of the detection and tracking of vessel targets in maritime radar images. We compare different methods using a database which consists of radar images from two different manufactures. The data covers a timespan of roughly 4 hours with a one second time resolution. The performance of 3 template matching and 5 feature detector algorithms is tested and the most suitable algorithm is chosen for further optimizations. These optimizations are based on the properties of the radar images and the properties of the radar target.