| Quantum swarm intelligence optimization algorithm is efficient integration of quantum computing and swarm intelligence algorithm.It has a good application in the field of engineering and a variety of optimization problems in real life,and is the research focus in the current academic and practical application.Most of the optimization problems are NP-hard problem with a great deal of complexity and uncertainty.Thus,it is of great theoretical significance and practical value to study the intelligent optimization algorithm of quantum swarm to solve the complex optimization problem.In this paper,quantum computing and swarm intelligence algorithm are integrated.Quantum-inspired swarm intelligent optimization algorithm and its improved algorithm are designed.Furthermore,the application research is carried out in related optimization problems.The main work and innovation points are as follows:(1)A quantum-inspired cuckoo search algorithm is proposed for no-wait flow-shop scheduling problem.The initial population is generated by a qubits which is encoded by double chains.Then,the cuckoo population is updated according to the levy flight mechanism,and the quantum rotation gate is introduced to improve the population in the iterative process.Test on the benchmarks,the results show that the proposed algorithm has a better optimization capability by compared with several other optimization algorithms.(2)Based on quantum-inspired cuckoo search algorithm,a co-search quantum-inspired cuckoo algorithm is proposed which combines the quantum encoding method of Bloch spherical coordinate and the thoughts of differential evolution.Quantum encoding method of Bloch spherical coordinate is developed to improve the solution and further increase the diversity.The thoughts of mutation and cross are introduced to enhance the strategy of cuckoo search,which can help the population to jump out of local optimum.Based on no-wait flow-shop scheduling problem,test the benchmark instances with experiment and the results show that the proposed algorithm can improve the solution quality to a large extent which is better than other swarm intelligent algorithm,and also can improve the performance of the quantum cuckoo search algorithm is improved.(3)The co-search quantum-inspired cuckoo algorithm is used to optimizing the path planning in the urban garbage recovery problem.Wireless sensor network model to collect and process information is designed with the experimental data which is collected from Yijiang district,Wuhu city.Then,the path planning is optimizing by the proposed algorithm.Compared with genetic algorithm and quantum-inspired cuckoo search algorithm,the experimental results show that the proposed algorithm has a better optimization effect. |
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