Dynamic opposite learning enhanced dragonfly algorithm for solving large-scale flexible job shop scheduling problem

Yang, Dongsheng; Wu, Manman; Li, Di; Xu, Yunlang; Zhou, Xianyu; Yang, Zhile

doi:10.1016/j.knosys.2021.107815

Cited by 32 publications

(13 citation statements)

References 42 publications

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“…where X − is the natural enemy location (the current worst solution). In summary of the five dragonfly group behaviors above, the step vector update strategy for individual dragonflies is shown in equation (6).…”

Section: Dragonfly Algorithmmentioning

confidence: 99%

“…(2) We add a local reasonableness mechanism [5]. e reasonableness review operator is added to the dragonfly algorithm, which iterates through the computational dimensions of individual dragonflies, retaining the reasonable localities after the flight and eliminating the localities that are not reasonable after the flight, to speed up the algorithm's computation rate [6].…”

Section: Dragonfly Algorithmmentioning

confidence: 99%

“…Step 4: We calculate the population evolution according to equations (1-5): separation Sr, alignment Ar, aggregation Cr, food attraction Fr, and natural enemy dispersal Er. We calculate the step size of individual dragonfly position update according to equation (6).…”

Section: Dragonfly Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Political Classroom Teaching Quality in Universities Based on DA-BP Algorithm

Guan

Zhu

2022

Security and Communication Networks

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In order to improve the evaluation accuracy of political classroom teaching quality in universities, a DA-BP-based evaluation method of political classroom teaching quality in universities is proposed. First of all, in constructing the teaching quality evaluation system of university political courses using the framework of behavior theory, the system consists of 4 primary indicators and 15 secondary indicators. Second, the university’s 2008–2013 annual teaching quality assessment scores for the teaching quality of political courses were used as the input data of the DA-BP network, and the 2013–2016 teaching evaluation scores were used as the algorithm’s test data. In order to test the prediction accuracy of the DA-BP algorithm, it is compared with the GA-BP algorithm, PSO-BP algorithm, and BP algorithm. The research results show that DA-BP has higher prediction accuracy than other algorithms and can better evaluate the teaching quality of political courses.

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Section: Dragonfly Algorithmmentioning

confidence: 99%

Section: Dragonfly Algorithmmentioning

confidence: 99%

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Evaluation of Political Classroom Teaching Quality in Universities Based on DA-BP Algorithm

Guan

Zhu

2022

Security and Communication Networks

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“…Section 3.1.2 enriches the PIO algorithm by integrating the DOL strategy. Further, the DOL strategy consists of two pivotal stages: the initial population generation and parameters and the creation of asymmetric opposite points, followed by population diversification through updating operations [36]. The DOL strategy introduces two critical elements for optimization enhancement: a Jump rate (Jr) that enables random switching during the evolution of a mutated population and a weighting factor (w) meticulously designed to strike an equilibrium between exploration and exploitation capabilities [37].…”

Section: Dol Phasementioning

confidence: 99%

Enhancing sparse regression modeling of hysteresis with optimized PIO algorithm in piezo actuator

Jin,

Yu,

et al. 2024

Smart Mater. Struct.

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The extensive application of piezo actuators is attributed to their high responsiveness and ability to achieve nanoscale steps. However, the accuracy and stability of motion are seriously affected by hysteresis caused by nonlinear characteristics. In this paper, a pigeon-inspired optimization (PIO) algorithm based on dynamic opposite learning (DOL) is proposed to address the issue of nonlinear modeling accuracy in piezo actuators by integrating the sparse identification of nonlinear dynamics (SINDy) method. Firstly, the DOL strategy is employed to introduce reverse pigeon flock into the PIO algorithm, thereby enhancing population diversity and optimization performance. Secondly, through combining the DOLPIO algorithm with the SINDy algorithm, sparse optimization for the penalty process in SINDy algorithm is conducted and the sparse coefficient is optimized based on modeling accuracy. Thirdly, the DOLPIO algorithm is utilized again to optimize the framework of optimized sparse penalty model in order to improve overall modeling accuracy. Finally, experiments are conducted on an established platform to validate the effectiveness of this algorithm.

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“…Based on the notation listed in Table 1, a mathematic formulation for problem can be described as follows. 28,46 Objective Function:…”

Section: Problem Statementmentioning

confidence: 99%

Digital twin and deep reinforcement learning enabled real-time scheduling for complex product flexible shop-floor

Chang

Jia

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Real-time scheduling methods are essential and critical to complex product flexible shop-floor due to the dynamic events in the production process, such as new job insertions, machine breakdowns and frequent rework. Recently, digital twin (DT) technology can help identify disturbances by continuously comparing physical space with virtual space, which enables real-time scheduling and greatly reduces the deviation between pre-schedule and actual schedule. However, the conventional scheduling models and algorithms cannot satisfy the adaptiveness and timeliness requirements of optimization in DT enabled shop-floor (DTS). To address above challenges, an overall framework of DT enabled real-time scheduling (DTE-RS) for complex product shop-floor is proposed to effectively reduce adverse impacts of the dynamic disturbances and minimize the makespan. Firstly, complex product flexible job shop scheduling problem (CPFJSP) is formulated as Markov Decision Process (MDP), taking into account machine breakdown and new job insertions. Then, deep Q-network (DQN) based solution is developed to achieve optimal task dispatching according to real-time production state. Finally, the case study for aircraft overhaul shop-floor is conducted to demonstrate effectiveness and feasibility of the proposed real-time scheduling method. Through experimental comparison, it is indicated that the proposed method could effectively respond to dynamic disturbances and outperform the dynamic scheduling method in terms of makespan.

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Dynamic opposite learning enhanced dragonfly algorithm for solving large-scale flexible job shop scheduling problem

Cited by 32 publications

References 42 publications

Evaluation of Political Classroom Teaching Quality in Universities Based on DA-BP Algorithm

Evaluation of Political Classroom Teaching Quality in Universities Based on DA-BP Algorithm

Enhancing sparse regression modeling of hysteresis with optimized PIO algorithm in piezo actuator

Digital twin and deep reinforcement learning enabled real-time scheduling for complex product flexible shop-floor

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