Modified Artificial Bee Colony Algorithm And Its Applications On Cutting Parameter Optimization Problem

The macro intelligent behavior characteristics exhibited among gregarious creatures suchas insects and animals through mutual cooperation in individuals is called Swarm Intelligence.And Artificial Bee Colony Algorithm was inspired by bee colony’ intelligent behaviorexhibited when collecting honey. The algorithm is widely applied in many engineeringproblems. Research of cutting parameter optimization has started in a very long period of time,recently, swarm intelligence optimization algorithm has become a vital tool of cuttingparameter optimization research. In this paper, we try to solve cutting parameter optimizationproblem using a Modified Artificial Bee Colony Algorithm.Firstly, we analysis the model of bee colony foraging and introduce the mechanism of thebasic Artificial Bee Colony algorithm. Based on a detailed flow chart of basic Artificial BeeColony algorithm, we test its effect and performance for solving high-dimensional continuousfunction problems by programming. According to the test results, its shortage on searchstratrgy is further analyzed, thus a local search strategy is introduced to modify and improveArtificial Bee Colony algorithm. Finally, through another test, a Modified Artificial BeeColony algorithm is put forward, which can gain good results when solving high-dimensionalcontinuous function problems.Then, aming at cutting parameter optimization, a cutting parameter optimization modewith unit production cost goal is referenced, based on minimizing production cost during theactual production process. And we test the unit production cost model by the previousModified Artificial Bee Colony algorithm. The feasibility and superiority of ModifiedArtificial Bee Colony algorithm in studying cutting parameter optimization can be verifiedthrough comparing test results. After achieving perfect results in single objective problems,another two models are suggested, taking unit production time and carbon emissions for target,and multi-objective cutting parameter optimization model combing unit production cost, unitproduction time and carbon emissions is established. The multi-objective optimization modelis solved applying Modified Artificial Bee Colony algorithm. Through single objective cuttingparameter optimization and multi-objective cutting parameter optimization research, on theone hand, we verify that the algorithm posed in this paper can solve related issues perfectly,on the other hand, we shows that production cost, production time and carbon emissionsduring process are mutual restraint. When selecting cutting parameters, we have to balancethe relationship among them.Finally, a summary of our work both on Modified Artificial Bee Colony algorithm andcutting parameter optimization research is given. At the same time, the further research directions of both are analyzed and forecasted, which can be regarded as a reference forsubsequent researchers.