A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

Pérez-Rodríguez, Ricardo

doi:10.3390/mca26030064

Cited by 5 publications

(5 citation statements)

References 55 publications

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“…Several heuristic algorithms have been proposed to obtain approximate solutions for PFSP [26][27][28][29]. Additionally, metaheuristic optimization techniques have revolutionized combinatorial optimization, leading to a diverse range of strategies for solving PFSP, including genetic algorithms [30,31], simulated annealing [32], estimation of distribution algorithms [7,10,33], and particle swarm optimization [34,35]. In [36], a parallel metaheuristic approach is designed to tackle PFSP.…”

Section: Introductionmentioning

confidence: 99%

An Estimation of Distribution Algorithm for Permutation Flow-Shop Scheduling Problem

Lemtenneche,

Bensayah,

Cheriet

2023

Systems

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Estimation of distribution algorithms (EDAs) is a subset of evolutionary algorithms widely used in various optimization problems, known for their favorable results. Each generation of EDAs builds a probabilistic model to represent the most promising individuals, and the next generation is created by sampling from this model. The primary challenge in designing such algorithms lies in effectively constructing the probabilistic model. The mutual exclusivity constraint imposes an additional challenge for EDAs to approach permutation-based problems. In this study, we propose a new EDA called Position-Guided Sampling Estimation of Distribution Algorithm (PGS-EDA) specifically designed for permutation-based problems. Unlike conventional approaches, our algorithm focuses on the positions rather than the elements during the sampling phase. We evaluate the performance of our algorithm on the Permutation Flow-shop Scheduling Problem (PFSP). The experiments conducted on various sizes of Taillard instances provide evidence of the effectiveness of our algorithm in addressing the PFSP, particularly for small and medium-sized problems. The comparison results with other EDAs designed to handle permutation problems demonstrate that our PSG-EDA algorithm consistently achieves the lowest Average Relative Percentage Deviation (ARPD) values in 19 out of the 30 instances of sizes 20 and 50 used in the study. These findings validate the superior performance of our algorithm in terms of minimizing the makespan criterion of the PFSP.

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Section: Introductionmentioning

confidence: 99%

An Estimation of Distribution Algorithm for Permutation Flow-Shop Scheduling Problem

Lemtenneche,

Bensayah,

Cheriet

2023

Systems

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“…EDA combines the characteristics of both genetic algorithms and statistical learning, possessing stronger evolution guidance, chaining learning ability, and global search capability. It was applied to optimization problems such as supermarket scheduling [13], path planning [14], and crane scheduling [15].…”

Section: Introductionmentioning

confidence: 99%

“…Among a few related studies, [14] introduced a probability model with Mallows distribution. In [15], a probability model was introduced using a distance-based ranking model and the moth-flame algorithm. In [20], utility functions were introduced to characterize preferences and apply them to multi-objective optimization.…”

Section: Introductionmentioning

confidence: 99%

An Interactive Estimation of the Distribution Algorithm Integrated with Surrogate-Assisted Fitness

Qiao,

Guo,

Zhang

2023

Symmetry

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To accurately model user preference information and ensure the symmetry or similarity between real user preference and the estimated value in product optimization design, an interactive estimation of a distribution algorithm integrated with surrogate-assisted fitness evaluation (SAF-IEDA) is proposed in this paper. Firstly, taking the evaluation information of a few individuals as training data, a similarity evaluation method between decision variables is proposed. Following that, a preference probability model is built to estimate the distribution probability of decision variables. Then, the preference utility function of individuals is defined based on the similarity of decision variables. Finally, the surrogate-assisted fitness evaluation is realized by optimizing the weight of the decision variables’ similarities. The above strategies are incorporated into the interactive estimation of the distribution algorithm framework and applied to address the optimal product design problem and the indoor lighting optimization problem. The experimental results demonstrate that the proposed method outperforms the comparative method in terms of search efficiency and fitness prediction accuracy.

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“…During past decades, optimization techniques have been developed widely to solve complex problems that emerged in different fields of science, such as engineering [1][2][3][4][5][6][7][8][9], clustering [10][11][12][13][14][15][16][17][18], feature selection [19][20][21][22][23][24][25][26][27][28], and task scheduling [29][30][31][32]. Such optimization problems mainly involve characteristics such as linear/non-linear constraints, nondifferentiable functions, and a substantial number of decision variables.…”

Section: Introductionmentioning

confidence: 99%

Migration-Based Moth-Flame Optimization Algorithm

et al. 2021

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Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.

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A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

Cited by 5 publications

References 55 publications

An Estimation of Distribution Algorithm for Permutation Flow-Shop Scheduling Problem

An Estimation of Distribution Algorithm for Permutation Flow-Shop Scheduling Problem

An Interactive Estimation of the Distribution Algorithm Integrated with Surrogate-Assisted Fitness

Migration-Based Moth-Flame Optimization Algorithm

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