Species-Based Chaotic Hybrid Optimizing Algorithm and its Application in Image Detection
Na Dong, C.H. Wu, W.H. Ip, Zengqiang Chen, Ai‐Guo Wu · Applied Artificial Intelligence · 2014
An evolutionary image-detection method based on a novel chaotic hybrid optimizing algorithm (CHOA) is proposed. The method combines the strengths of particle swarm optimization (PSO), genetic algorithms (GAs), and chaotic dynamics (CD), and involves the standard velocity and position update rules of PSOs with the ideas of selection, crossover, and mutation from GA. In addition, the notion of species is introduced into the proposed CHOA to enhance its performance in solving multimodal problems. The effectiveness of the species-based chaotic hybrid optimizing algorithm (SCHOA) is proven through simulations and benchmarking, and finally, it is successfully applied to solve a multitemplate matching (MTM) problem in the printed circuit board (PCB) industry, as well as circle-detection problems. To make it more powerful in solving circle-detection problems in complicated circumstances, the notion of “tolerant radius” is proposed and incorporated into the SCHOA method. Simulation tests were undertaken on several hand-drawn sketches and natural photos, and the effectiveness of the proposed method was clearly shown through the test results.