The Application Of Fruit Fly Optimization Algorithm In Combinatorial Optimization

The fruit fly algorithm(Fruit Fly Optimization Algorithm,FOA)is a new type of group intelligent optimization algorithm which has just been proposed in recent years.The algorithm has the advantages of easy understanding,easy implementation,and less adjustment of parameters.It has attracted the attention of scholars at home and abroad and has become one of the popular research algorithms.Through in-depth study of the fruit fly optimization algorithm,it is known that the algorithm also has some shortcomings,such as easy to fall into local optimum and low precision.Aiming at the deficiencies of this algorithm,the corresponding improvement methods are proposed through analysis.At the same time,the improved algorithm is applied to the solution of combinatorial optimization problem,and the related research on the fruit fly optimization algorithm is also improved.The main research contents of this paper are as follows:Firstly,it introduces several common swarm intelligent algorithms in briefly,summarizes the solution process of these algorithms,and describes the basic content,mathematical model and solution method of combinatorial optimization problems.Then it analyzes the fruit fly optimization algorithm in detail,including its biological origin and basic principles,parametric and algorithmic flow,summarizes its research status and application fields,and discusses its shortcomings.Secondly,aiming at the deficiency of the fruit fly optimization algorithm,the principle of simulated annealing algorithm and roulette selection mechanism are applied to the algorithm,that is,before the fruit flies enter the iteration,the position of the previous generation of fruit flies is disturbed by simulated annealing;The reverse roulette selection strategy determines the search distance so that the optimal position after selection is used as the search position of the latter generation of fruit flies,so that the latter generation of fruit flies can jump out of the local optimum through the selected search distance,thereby the improved algorithm can improve the diversity of the fruit fly population and then lead to improved algorithm performance.Experiments on the test function show that the improved fruit fly optimization algorithm improves the global optimization ability and has higher precision.Then,the improved fruit fly optimization algorithm is used to solve the combinatorial optimization problem,and the traveling salesman problem and the batch flow shop scheduling problem are briefly introduced.In the traveling salesman problem,the path coding which the reverse roulette strategy selects is cross-operated to obtain a new path coding,then the improved algorithm calculates the corresponding path length,and uses a simulated annealing algorithm to accept poor path coding with a certain probability,thereby solves this problem.In the batch flow shop problem,the improved fruit fly algorithm is applied to the batch flow shop scheduling problem by coding the fruit fly individual,and the new fruit fly individual is selected by the reverse roulette strategy,and combined with the greedy iterative process.This process optimizes the selected individuals,generates the new individual sequence.Then the improved algorithm calculates the corresponding completion time,and uses the simulated annealing algorithm to accept poor individual sequences with a certain probability.At the same time,through comparison with other algorithms,it can be seen that the improved fruit fly algorithm is effective and feasible.Finally,the research contents of this paper are summarized,and the related research topics of the fruit fly optimization algorithm are further studied.