An opposition-based chaotic GA/PSO hybrid algorithm and its application in circle detection

An evolutionary circle detection method based on a novel Chaotic Hybrid Algorithm (CHA) is proposed. The method combines the strengths of particle swarm optimization, genetic algorithms and chaotic dynamics, and involves the standard velocity and position update rules of PSOs, with the ideas of selection, crossover and mutation from GA. The opposition-based learning (OBL) is employed in CHA for population initialization. In addition, the notion of species is introduced into the proposed CHA to enhance its performance in solving multimodal problems. The effectiveness of the Species-based Chaotic Hybrid Algorithm (SCHA) is proven through simulations and benchmarking; finally it is successfully applied to solve 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 SCHA-based method. Simulation tests were undertaken on several hand drawn sketches and natural photos, and the effectiveness of the proposed method was clearly shown in the test results.