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An improved ant colony optimization approach for optimization of process planning.

Wang J, Fan X, Ding H - ScientificWorldJournal (2014)

Bottom Line: In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively.A case has been carried out to study the influence of various parameters of ACO on the system performance.Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach.

View Article: PubMed Central - PubMed

Affiliation: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China.

ABSTRACT
Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPCs). A pheromone updating strategy proposed in this paper is incorporated in the standard ACO, which includes Global Update Rule and Local Update Rule. A simple method by controlling the repeated number of the same process plans is designed to avoid the local convergence. A case has been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach.

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Determination of numbers of ants K.
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fig5: Determination of numbers of ants K.

Mentions: Thirdly, initial parameters of ACO algorithm affect the performance of process planning using ACO. The effect is described and illustrated by an analysis of the application of the proposed ACO algorithm in the process planning problem for Part 1. Number of ants K has important effect on the convergence speed. If K is too small, searching randomness of ACO will increase and the computation time will be long. If K is too large, the optimization rate will become very slow. Generally, value of K is considered according to the problem size. In the case of problems with ρ = 0.75, α = 1, β = 1, τ0 = 1, E = 50, Q = 2000, Mite = 300, and Mrpt = 5, 10 trials were separately conducted by varying the values of K ∈ {10,25,40}. The average results of the experiment are summarized in Figure 5.


An improved ant colony optimization approach for optimization of process planning.

Wang J, Fan X, Ding H - ScientificWorldJournal (2014)

Determination of numbers of ants K.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4109124&req=5

fig5: Determination of numbers of ants K.
Mentions: Thirdly, initial parameters of ACO algorithm affect the performance of process planning using ACO. The effect is described and illustrated by an analysis of the application of the proposed ACO algorithm in the process planning problem for Part 1. Number of ants K has important effect on the convergence speed. If K is too small, searching randomness of ACO will increase and the computation time will be long. If K is too large, the optimization rate will become very slow. Generally, value of K is considered according to the problem size. In the case of problems with ρ = 0.75, α = 1, β = 1, τ0 = 1, E = 50, Q = 2000, Mite = 300, and Mrpt = 5, 10 trials were separately conducted by varying the values of K ∈ {10,25,40}. The average results of the experiment are summarized in Figure 5.

Bottom Line: In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively.A case has been carried out to study the influence of various parameters of ACO on the system performance.Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach.

View Article: PubMed Central - PubMed

Affiliation: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China.

ABSTRACT
Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPCs). A pheromone updating strategy proposed in this paper is incorporated in the standard ACO, which includes Global Update Rule and Local Update Rule. A simple method by controlling the repeated number of the same process plans is designed to avoid the local convergence. A case has been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach.

Show MeSH
Related in: MedlinePlus