Research And Application Of Flexible Job Shop Scheduling Problem Based On Salp Swarm Algorithm

With the deepening of global economic integration,"rigid" production scheduling has been unable to adapt to the diversified requirements of customers,therefore,the internal competition of small and medium-sized enterprises has become increasingly intense,which urgently needs the flexible production scheduling mode(FJSP)such a new,with strong flexibility and high efficiency,in line with user preferences of the production scheme.The emergence of multi-objective flexible job-shop scheduling problem(MOFJSP)enables enterprises to produce more flexibly while maintaining high efficiency,and provides more abundant management decision choices for enterprises.The emergence of this technology provides enterprises with a new and more sustainable development mode,which has important research value.Influenced by the salp clustering phenomenon,Seyedali Mirjalili proposed the salp clustering algorithm in 2017.In this paper,the operating mechanism and process of the algorithm were discussed in detail,and the exploration and variation strategies of SSA algorithm were compared by combining theory and experiment,and its time complexity was also analyzed.This paper aims to overcome the shortcomings of single objective and multi-objective optimization by improving the SSA algorithm,and to study it in order to achieve better results.The main research contents of this paper are as follows:1.Aiming at problems such as unbalanced global search and local development,low convergence accuracy and precocious convergence of salp swarm algorithm(SSA),Quantum Salp Swarm Algorithm Based On Improved Levy Flight(LQSSA)was proposed by introducing quantum computing theory.The algorithm uses quantum probability amplitude coding scheme to improve the diversity of the population,and adaptively adjusts the ratio of leaders and followers according to the number of iterations,so as to balance the exploration ability and development ability.An adaptive quantum turnstile strategy is proposed,which enhances the global search and local development capabilities of the algorithm and improves the computational efficiency.A quantum mutation gate based on Levy flight strategy was designed to dynamically adjust the mutation probability according to the number of iterations to prevent entering the local optimal.After the comparison of 8 benchmark test functions in CEC2017,the performance and stability of this method is significantly higher than that of standard SSA and some improved algorithms,and it is not affected by the local optimal value,and in flexible job shop scheduling,this method shows a good effect.2.In order to solve the problems of insufficient diversity,weak information exchange ability among populations,and easy to fall into local optimum in multi-objective optimization of salp swarm optimization algorithm,a multi-subgroup and multi-objective optimization algorithm is proposed.Pareto Envelop-based Selection Algorithm(PESA)is used.In this algorithm,a multi-link topology structure is proposed to enhance the information exchange between populations,and a dynamic multi-strategy subgroup is constructed to improve the diversity of the population.In addition,a population adaptive attenuation strategy is proposed to not only ensure the diversity of the population,but also accelerate the convergence of the algorithm.The variation strategy of Levy flight is used to jump out of the local optimal.By comparing with 8 different multi-objective reference detection functions,we find that the new algorithm can solve the problem better,and can better ensure the convergence and distribution of solutions.And in the field of multi-objective flexible job shop scheduling,the algorithm also has high practical application value,and the experimental results show that the algorithm is very effective.