A Two-Individual Based Evolutionary Algorithm for the Flexible Job Shop Scheduling Problem

Ding, Junwen; Lu, Zhengding; Li, Chu-Min; Shen, Liji; Xu, Liping; Glover, Fred

doi:10.1609/aaai.v33i01.33012262

Cited by 19 publications

(4 citation statements)

References 29 publications

(34 reference statements)

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“…If the memory constraints are ignored, the problem can be viewed as the flexible job shop scheduling problem (FJSP). Therefore, the classic neighborhood structures of this problem (N7 and kinsertion) proposed by Ding et al (2019) can be used as the neighborhood action of the outer layer.…”

Section: B the Proposed Tabu Search Proceduresmentioning

confidence: 99%

“…First, as commonly used the classical FJSP problem Ding et al (2019), it is necessary to identify the critical path, critical operations, and critical blocks. Then we adopt the N7 neighborhood structure (called N π here González et al (2015) ) and k-insertion neighborhood structure (called N α here Mastrolilli and Gambardella (2000)), and construct the neighborhood N π and N α (line 6 and line 7), respectively.…”

Section: B the Proposed Tabu Search Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

A heuristic method for data allocation and task scheduling on heterogeneous multiprocessor systems under memory constraints

Ding¹,

Song²,

Liu³

et al. 2022

Preprint

Self Cite

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Computing workflows in heterogeneous multiprocessor systems are frequently modeled as directed acyclic graphs of tasks and data blocks, which represent computational modules and their dependencies in the form of data produced by a task and used by others. However, for some workflows, such as the task schedule in a digital signal processor may run out of memory by exposing too much parallelism. This paper focuses on the data allocation and task scheduling problem under memory constraints, and concentrates on shared memory platforms. We first propose an integer linear programming model to formulate the problem. Then we consider the problem as an extended flexible job shop scheduling problem, while trying to minimize the critical path of the graph. To solve this problem, we propose a tabu search algorithm (TS) which combines several distinguished features such as a greedy initial solution construction method and a mixed neighborhood evaluation strategy based on exact evaluation and approximate evaluation methods. Experimental results on randomly generated instances show that the the proposed TS algorithm can obtain relatively high-quality solutions in a reasonable computational time. In specific, the tabu search method averagely improves the makespan by 5-25% compared to the classical load balancing algorithm that are widely used in the literature. Besides, some key features of TS are also analyzed to identify its success factors.

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Section: B the Proposed Tabu Search Proceduresmentioning

confidence: 99%

Section: B the Proposed Tabu Search Proceduresmentioning

confidence: 99%

A heuristic method for data allocation and task scheduling on heterogeneous multiprocessor systems under memory constraints

Ding¹,

Song²,

Liu³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Taken together, there is already some work that uses MOEA to solve the workflow scheduling problem. Ding et al used taboo search to perform evolutionary process on only two individuals to search the optimum solution [12]. Lu et al adopted the auto aggressive model to predict the evolutionary trajectory of pareto non-dominated solutions to optimize the solutions [13].…”

Section: Multi-objective Evolutionary Algorithmmentioning

confidence: 99%

Enhanced multi-objective evolutionary algorithm for workflow scheduling problem

Lin

Tian

Wang³

et al. 2022

Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022)

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With the development and application of distributed cloud computing, the problem of assigning workflow tasks to computational resources has become more and more prominent. It involves multiple constraints and optimization objectives, and is a typical NP-hard problem. Existing evolutionary algorithms face local optimum and premature convergence problems. Considering these facts, we proposed a multi-objective evolutionary algorithm with elitism strategy (MOEAES) in this paper. To avoid local optimum, MOEAES uses a new crossover operator called Random Sub-Sequence Exchange Crossover (RSSEX), and it introduces a multi-population-based elitism strategy to accelerate the algorithm. Finally, experimental validation is carried out, which shows that MOEAES achieves performance improvement in terms of solution quality and convergence speed comparing to other methods.

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“…The evolutionary part of our algorithm uses the master-apprentice framework and manages only two individuals (apprentices) at each iteration, where the apprentices evolve to become masters after a given number of generations (a cycle). The idea of the two-individual based evolutionary algorithm was first proposed in [22] for solving the graph coloring problem and further formalized by Ding et al [5] for solving the flexible job shop scheduling problem. To absorb the essence of the history of the search process and evolution, one apprentice will be replaced by the master from the previous cycle.…”

Section: Master-apprentice Evolutionary Algorithm With Hybrid Tabmentioning

confidence: 99%

A Fast and Robust Heuristic Algorithm for the Minimum Weight Vertex Cover Problem

Wang

Punnen

2021

IEEE Access

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The minimum weight vertex cover problem (MWVCP) is a fundamental combinatorial optimization problem with various real-world applications. The MWVCP seeks a vertex cover of an undirected graph such that the sum of the weights of the selected vertices is as small as possible. In this paper, we present an effective algorithm to solve the MWVCP. First, a master-apprentice evolutionary algorithm based on two individuals is conducted to enhance the diversity of solutions. Second, a hybrid tabu search combined configuration checking and solution-based tabu search is introduced to intensify local search procedure. Harnessing the power of the evolutionary strategy and a novel variant of hybrid tabu search, Master-Apprentice Evolutionary Algorithm with Hybrid Tabu Search, MAE-HTS, is presented. Results of extensive computational experiments using standard benchmark instances and other large-scale instances demonstrate the efficacy of our algorithm in terms of solution quality, running time, and robustness compared to state-of-the-art heuristics from the literature and the commercial MIP solver Gurobi. We also systematically analyze the role of each individual component of the algorithm which when worked in unison produced superior outcomes. In particular, MAE-HTS produced improved solutions for 2 out of 126 public benchmark instances with better running time. In addition, our MAE-HTS outperforms other state-of-the-art algorithms DLSWCC and NuMWVC for 72 large scale MWVCP instances by obtaining the best results for 64 ones, while other two reference algorithms can only obtain 27 best results at most. INDEX TERMS Hybrid evolutionary algorithm, metaheuristics, minimum weight vertex cover problem, solution-based tabu search.

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A Two-Individual Based Evolutionary Algorithm for the Flexible Job Shop Scheduling Problem

Cited by 19 publications

References 29 publications

A heuristic method for data allocation and task scheduling on heterogeneous multiprocessor systems under memory constraints

A heuristic method for data allocation and task scheduling on heterogeneous multiprocessor systems under memory constraints

Enhanced multi-objective evolutionary algorithm for workflow scheduling problem

A Fast and Robust Heuristic Algorithm for the Minimum Weight Vertex Cover Problem

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