Research On The Cooperative Learning Strategy Assisted Fruit Fly Optimization Algorithm And Its Application In Distributed Shop Scheduling

The shop scheduling is a basic problem in manufacturing industry.In recent years,the development of manufacturing industry has been emphasized by the government,and shop scheduling problem has become particularly important.Especially with the development of economic globalization,the original single factory scheduling has been replaced by distributed shop scheduling.In the distributed shop scheduling,the scale of the problem is generally increased.The production environment has become dynamic and complex.The distributed shop scheduling is a recognized NP-hard problem.The traditional solution method is not satisfied the requirement of the current problem scales and the demand for solving speed.Therefore,the distributed shop scheduling problem is one of the key roles to assist the update of manufacturing system.Thus,the research of distributed shop scheduling is practically significant.The fruit fly optimization algorithm is a kind of population-based intelligent optimization algorithm which is inspired by fruit fly foraging behavior.It is characterized by simple principles,clear framework,few algorithm parameters,and easy to nest with various problems,having strong local search ability.As a typical swarm intelligence optimization algorithm,the efficient cooperative mechanism in FOA has received extensive attention from researchers.In this paper,the operation mechanism of FOA is studied in detail,and the reasons for its insufficient performance in solving complex problems are analyzed.After a series of experiments,the performance of the improved algorithms is enhanced significantly.The main research contents of this paper are as follows:(1)For solving the complex continuous optimization problems,an improved fruit fly algorithm based on vision scanning search and extensive learning mechanism is proposed in this paper.The vision scanning search strategy is used to scan the potential area by changing the search angle of swarm center.This strategy is utilized to guide the population to jump out of the local trap.The extensive learning uses the knowledge of neighboring structures to increase the diversity of the population for solving the non-separable issues.Furthermore,a new mutation strategy based on difference vector is proposed to improve the search efficiency of VLFOA.Testified in CEC 2017 benchmark problems,the results show that the VLFOA has a superior performance comparing with the original FOA and the state of art variants of the FOA.(2)A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism(HGCLFOA)is proposed in this study.The population is divided into elitist and inferior subpopulations with the fitness of objective functions.The population center is re-designed as an elitist subpopulation to maintain the diversity of the population.In the olfaction search stage,the hierarchical guidance strategy is introduced for local search according to the difference of solution qualities to assign inferior individuals to elitist individuals on different levels.Meanwhile,the inferior information is applied by the inferior solutions repairing strategy to deflect the prediction of the elitist subpopulation for preventing HGCLFOA from falling into the local optimum.In the vision search stage,a hybrid Gaussian distribution estimation strategy is adopted to extract the elitist information of previous generations to predict the distribution of potential elitist individuals in the next generation.The exploration and exploitation of the HGCLFOA are balanced by the cooperation between elitist subpopulation and inferior subpopulation.A random walk strategy is activated to assist the elitist solutions to jump out the local optimal.The parameters of the HGCLFOA are calibrated by DOE and ANOVA methods.The experimental results demonstrated that the HGCLFOA outperformed the classical FOA and state-of-arts variants of FOA.(3)A discrete knowledge-guided learning fruit fly optimization algorithm is proposed to solve the scheduling problem of distributed no-wait flowshop scheduling problem.Based on the problem knowledge,an efficient initialization method is proposed.The population center is extended from the single fruit fly individual to an elite population to guide the search of fruit fly swarm more scientifically.In the vision search stage,the position information of elite individuals in the population and the knowledge contained in the problems are used to guide the global search of elite populations.And a priority sequence is produced to update fruit fly individuals.In the olfactory search stage,three neighborhood structures are proposed.And the local search around elite individuals is applied.Through the experiment and analysis,in the three stages,the different operation is effective.And the proposed algorithm is verified on the TAILLARD problems.The results show that the proposed algorithm is efficient for solving complex discrete problems.