A new discrete double-population firefly algorithm for assembly sequence planning

Zhang, Zaifang; Yuan, Baoxun; Zhang, Zhinan

doi:10.1177/0954405416629099

Cited by 18 publications

(13 citation statements)

References 24 publications

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“…It is a combinational optimisation problem with strong constraints. 8 The ASP problem of a product is essentially a combinatorial optimisation problem. As the number of components in a product increases, the number of assembly sequences (including feasible and infeasible) will increase exponentially.…”

Section: Assembly Sequence Planningmentioning

confidence: 99%

An assembling algorithm for fixture in an assembly process planning system

Liu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Three-dimensional assembly process planning is a precondition for achieving full product lifecycle management based on three-dimensional modelling. Information expression and management operations of fixture models are key to three-dimensional assembly process planning systems. To reasonably and efficiently achieve the goal of planning assembly processes with a fixture model, the application of a fixture information model to a three-dimensional assembly process planning system is investigated. First, to manage tooling information, a fixture information model is defined that consists of management information, display information, geometric information, assembly constraints and degrees of freedom. Then, based on the analysis of the degrees of freedom of components and the assembly constraint relationship, a method of solving assembly constraints based on degrees of freedom reasoning is proposed. Finally, by using the method of solving assembly positioning based on geometric constraints, the pose transformation and assembly positioning of parts are achieved. Through the development of a tooling function module, the three-dimensional assembly process planning system is improved. The feasibility of the above method is verified using part of a spacecraft as an example.

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Section: Assembly Sequence Planningmentioning

confidence: 99%

An assembling algorithm for fixture in an assembly process planning system

Liu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Two separate immune models, namely, bone marrow model and negative selection models were developed as part of their work. Zhang et al (2016) developed a discrete double-population firefly algorithm. It guaranteed the population diversity reducing chances of premature convergence and enhanced local and global search capabilities by parallel evolution of solutions.…”

Section: Review Of Previous Researchmentioning

confidence: 99%

“…This gives the advantage of maintaining diversity in flowers during the course of the algorithm and helping to avoid the local optima. For cases like assembly sequence planning, the infeasible sequences nearer to the Figure 3 Assembly process constraints and optimization criteria related to assembly sequence planning optimal solution space can help identify the optimal solution (Zhang et al, 2016). To expand the field of initial solutions (i.e.…”

Section: Proposed Assembly Sequence Representation Scheme and Initial Population Generationmentioning

confidence: 99%

An improved hybrid flower pollination algorithm for assembly sequence optimization

Mishra

Deb

2018

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Purpose Assembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA. Design/methodology/approach In view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed. Findings The results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions. Practical implications It is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications. Originality/value Different representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

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“…Recently, FA has shown impressive performances in solving optimization problems. 18–20 As a member of the swarm intelligence family of algorithms, FA was proposed by Professor Yang, 21 University of Cambridge, in 2009. It is derived from the phenomenon of clustering activity of fireflies at night.…”

Section: Brief Reviewmentioning

confidence: 99%

An improved firefly algorithm for collaborative manufacturing chain optimization problem

Zhang

Jing

Zhu

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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In order to conduct resource sharing and deployment in cloud manufacturing environment, a concept of collaborative manufacturing chain was proposed. Based on machining tasks with the sequential characteristics, the proposed model considering the criteria of service cost, service time, service quality and service utilization was constructed. Fuzzy analytical hierarchy process was adopted to add the above multi-criteria model to a single objective problem. Then, an improved firefly algorithm was used to solve a reasonable collaborative manufacturing chain scheme. Based on the discrete characteristics of the collaborative manufacturing chain, iterative position function was improved to make the solution space to be a discrete domain. Furthermore, particle swarm optimization was used to optimize the step length factor α, attraction degree β0 and light absorption coefficient γ so as to prevent the firefly algorithm from local optimum. Compared with the genetic algorithm, numerical result suggests that the improved firefly algorithm has more advantages in convergence speed and solving efficiency. It is expected that this study can provide a useful reference for the service composition of collaborative manufacturing chain.

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A new discrete double-population firefly algorithm for assembly sequence planning

Cited by 18 publications

References 24 publications

An assembling algorithm for fixture in an assembly process planning system

An assembling algorithm for fixture in an assembly process planning system

An improved hybrid flower pollination algorithm for assembly sequence optimization

An improved firefly algorithm for collaborative manufacturing chain optimization problem

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