Research On Application Of Hybrid Optimization Algorithms In Image Processing

It needs a great amount of calculation for handling with complex science and engineering problems,meanwhile,commonly used deterministic optimization algorithms often fails in the face of these problems with the limited time.As a result,evolutionary computation inspired by natural evolution process has been widely studied and attracted much attention,which has been successfully used in many practical problems.However,single evolutionary algorithm or swarm intelligence algorithm has some deficiencies itself,which needs further improvement.Multivariate hybird algorithm is a kind of algorithm based on fusion of single evolutionary or swarm algorithms,which achieves the target of optimization through cooperation each other together.It is with the advantages of good balance,flexible combination and strong robustness and suitable for complex optimization problems.Though scholars have done a lot of research on the hybrid algorithms and acquired good achievements.There is many a evolutionary computation algorithm,the relevant theory and practice on the topic is not perfect,which deserves further study.On the basis of the characteristics of evolutionary algorithms and swarm intelligence algorithms a novel scheme for blending optimization algorithms is proposed in the thesis,that is,parallel mode,serial mode,and serial-parallel hybrid mode.Six commonly used hybrid optimization algorithms are selected to combine,further,the hybrid algorithms is applied in image processing.The main work is as follows:1.Genetic algorithm,differential evolution algorithm,particle swarm algorithm,artificial bee colony algorithm,cuckoo search algorithm and firefly algorithm are tested on 15 benchmark functions in CEC2015.Based on the process of algorithms the convergence ability,search ability of the each algorithm and the characteristics of escaping from the local optimum is analyzed.2.A specific hybrid strategy is proposed: parallel mode,serial mode,and serial-parallel hybrid mode.Six serial hybrid algorithms and six parallel hybrid algorithms are designed and simulation is carried out.The results show that the performance of the hybrid algorithm is more balanced and it has good effect on the optimization of complex problems.3.The multivariate hybrid algorithm are studied and applied in image segmentation,image enhancement and image match.Four hybrid algorithms with better performance and the corresponding single algorithm are tested for comparison.These hybrid algorithms include serial particle swarm search algorithm and cuckoo algorithm,serial differential evolution algorithm and cuckoo search algorithm,parallel genetic algorithm with differential evolution algorithm,parallel particle swarm algorithm with differential evolution algorithm.Four single algorithms are particle swarm algorithm,cuckoo search algorithm,differential evolution algorithm,genetic algorithm.The experimental results show that the hybrid optimization algorithm is able to obtain the better results in image segmentation,image enhancement and image match,which is stable on the different images.In general,this thesis proposes a kind of hybrid mode for optimization algorithms.Several hybrid algorithms are implemented based on the model and are applied in image segmentation,image enhancement and image match.The experimental results show that the hybrid algorithm inherits the characteristics the corresponding single algorithm,and has good stability for different optimization problems.