The selection of machining parameters directly affects the production time,quality,cost,and other process performance measures for multi-pass milling.Optimization of machining parameters is of great significance.Howev...The selection of machining parameters directly affects the production time,quality,cost,and other process performance measures for multi-pass milling.Optimization of machining parameters is of great significance.However,it is a nonlinear constrained optimization problem,which is very difficult to obtain satisfactory solutions by traditional optimization methods.A new optimization technique combined chaotic operator and imperialist competitive algorithm(ICA)is proposed to solve this problem.The ICA simulates the competition between the empires.It is a population-based meta-heuristic algorithm for unconstrained optimization problems.Imperialist development operator based on chaotic sequence is introduced to improve the local search of ICA,while constraints handling mechanism is introduced and an imperialist-colony transformation policy is established.The improved ICA is called chaotic imperialist competitive algorithm(CICA).A case study of optimizing machining parameters for multi-pass face milling operations is presented to verify the effectiveness of the proposed method.The case is to optimize parameters such as speed,feed,and depth of cut in each pass have yielded a minimum total product ion cost.The depth of cut of optimal strategy obtained by CICA are 4 mm,3 mm,1 mm for rough cutting pass 1,rough cutting pass 1 and finish cutting pass,respectively.The cost for each pass are$0.5366 US,$0.4473 US and$0.3738 US.The optimal solution of CICA for various strategies with at=8 mm is$1.3576 US.The results obtained with the proposed schemes are better than those of previous work.This shows the superior performance of CICA in solving such problems.Finally,optimization of cutting strategy when the width of workpiece no smaller than the diameter of cutter is discussed.Conclusion can be drawn that larger tool diameter and row spacing should be chosen to increase cutting efficiency.展开更多
Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many r...Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.展开更多
基金supported by the National Natural Science Foundation of China under grant no.51705182.
文摘The selection of machining parameters directly affects the production time,quality,cost,and other process performance measures for multi-pass milling.Optimization of machining parameters is of great significance.However,it is a nonlinear constrained optimization problem,which is very difficult to obtain satisfactory solutions by traditional optimization methods.A new optimization technique combined chaotic operator and imperialist competitive algorithm(ICA)is proposed to solve this problem.The ICA simulates the competition between the empires.It is a population-based meta-heuristic algorithm for unconstrained optimization problems.Imperialist development operator based on chaotic sequence is introduced to improve the local search of ICA,while constraints handling mechanism is introduced and an imperialist-colony transformation policy is established.The improved ICA is called chaotic imperialist competitive algorithm(CICA).A case study of optimizing machining parameters for multi-pass face milling operations is presented to verify the effectiveness of the proposed method.The case is to optimize parameters such as speed,feed,and depth of cut in each pass have yielded a minimum total product ion cost.The depth of cut of optimal strategy obtained by CICA are 4 mm,3 mm,1 mm for rough cutting pass 1,rough cutting pass 1 and finish cutting pass,respectively.The cost for each pass are$0.5366 US,$0.4473 US and$0.3738 US.The optimal solution of CICA for various strategies with at=8 mm is$1.3576 US.The results obtained with the proposed schemes are better than those of previous work.This shows the superior performance of CICA in solving such problems.Finally,optimization of cutting strategy when the width of workpiece no smaller than the diameter of cutter is discussed.Conclusion can be drawn that larger tool diameter and row spacing should be chosen to increase cutting efficiency.
文摘Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.
