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automatic algorithm design for Hybrid Flowshop Scheduling Problems

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第3期282卷 835-845页

作者： Alfaro-Fernandez, Pedro Ruiz, Ruben Pagnozzi, Federico Stutzle, Thomas Univ Politecn Valencia Grp Sistemas Optimizat Aplicada Inst Tecnol Informat Ciudad Politecn Innovat Edif 8GAcc B Camino de Vera S-N Valencia 46021 Spain Univ Libre Bruxelles IRIDIA CP 194-6Av F Roosevelt 50 B-1050 Brussels Belgium

Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are ATP-hard. As a result, researchers resort to metaheuristics to obtain effective and efficient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an automatic algorithm design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed MD for three different optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Scheduling Hybrid flowshop automatic algorithm configuration automatic algorithm design

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automatic algorithm design of distributed hybrid flowshop scheduling with consistent sublots

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COMPLEX & INTELLIGENT SYSTEMS 2024年第2期10卷 2781-2809页

作者： Zhang, Biao Lu, Chao Meng, Lei-lei Han, Yu-yan Hu, Jiang Jiang, Xu-chu Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Hubei Univ Automot Technol Sch Econ & Management Shiyan 442002 Peoples R China Zhongnan Univ Econ & Law Sch Stat & Math Wuhan 430073 Peoples R China

The present-day globalized economy and diverse market demands have compelled an increasing number of manufacturing enterprises to move toward the distributed manufacturing pattern and the model of multi-variety and small-lot. Taking these two factors into account, this study investigates an extension of the distributed hybrid flowshop scheduling problem (DHFSP), called the distributed hybrid flowshop scheduling problem with consistent sublots (DHFSP_CS). To tackle this problem, a mixed integer linear programming (MILP) model is developed as a preliminary step. The NP-hard nature of the problem necessitates the use of the iterated F-Race (I/F-Race) as the automated algorithm design (AAD) to compose a metaheuristic that requires minimal user intervention. The I/F-Race enables identifying the ideal values of numerical and categorical parameters within a promising algorithm framework. An extension of the collaborative variable neighborhood descent algorithm (ECVND) is utilized as the algorithm framework, which is modified by intensifying efforts on the critical factories. In consideration of the problem-specific characteristics and the solution encoding, the configurable solution initializations, configurable solution decoding strategies, and configurable collaborative operators are designed. Additionally, several neighborhood structures are specially designed. Extensive computational results on simulation instances and a real-world instance demonstrate that the automated algorithm conceived by the AAD outperforms the CPLEX and other state-of-the-art metaheuristics in addressing the DHFSP_CS.

关键词： Distributed hybrid flowshop scheduling Consistent sublots Metaheuristics automatic algorithm design I/F-Race

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An Incremental Approach to automatic algorithm design

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Journal of Computer Science & Technology 1999年第4期14卷 314-319页

作者：栾尚敏李未 DepartmentofComputerScience BeijingUniversityofAeronauticsandAstronauticsBeijing100083PR.China Depa

This paper presents an incremental approach to automatic algorithm design, which can be described by algebraic specifications precisely and conveniently. The definitions of selection operator and extension operator which can bedefined by strategy relations and transformations are given in order to model theprocess of finding the solution of a problem. Also discussed is its object-orientedimplementation. The functional specification and the design specification for an algorithm are given in one framework so that the correctness of the algorithm can beeasily proved.

关键词： automatic algorithm design algebraic specification object-orientedmethodology

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Meta-Black-Box optimization for evolutionary algorithms: Review and perspective

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 93卷

作者： Yang, Xu Wang, Rui Li, Kaiwen Ishibuchi, Hisao Natl Univ Def Technol Coll Syst Engn Deya Rd 109 Changsha 410073 Hunan Peoples R China Xiangjiang Lab Changsha 410205 Hunan Peoples R China Southern Univ Sci & Technol Coll Engn Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

Black-Box Optimization (BBO) is increasingly vital for addressing complex real-world optimization challenges, where traditional methods fall short due to their reliance on expert knowledge and time-consuming processes. Meta-Black-Box Optimization (MetaBBO) emerges as a pivotal solution, leveraging meta-learning to enhance or discover optimization algorithms automatically. Originating from automatic algorithm design (AAD), MetaBBO has branched into areas such as Learn to Optimize (L2O), Automated design of Meta-heuristic algorithm (ADMA), and automatic Evolutionary Computation (AEC), each contributing to the advancement of the field. This comprehensive survey integrates and synthesizes the extant research within MetaBBO for Evolutionary algorithms (EAs) to develop a consistent community of this research topic. Specifically, a mathematical model for MetaBBO is established, and its boundaries and scope are clarified. The potential optimization objects in MetaBBO for EAs is explored, providing insights into design space. A taxonomy of MetaBBO methodologies is introduced, reflecting the state-of-the-art from a meta-level perspective. Additionally, a comprehensive overview of benchmarks, evaluation metrics, and platforms is presented, streamlining the research process for those engaged in learning and experimentation in MetaBBO for EA. The survey concludes with an outlook on research, underscoring future directions and the pivotal role of MetaBBO in automatic algorithm design and optimization problem-solving.

关键词： Black-box optimization Evolutionary algorithms automatic algorithm design Learn to optimize Comprehensive survey

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Real-time data-driven automatic design of multi-objective evolutionary algorithm: A case study on production scheduling

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APPLIED SOFT COMPUTING 2023年 138卷

作者： Zhang, Biao Meng, Lei-lei Lu, Chao Li, Jun-qing Liaocheng Univ Sch Comp Sci Liaocheng 25200 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Shandong Normal Univ Sch Comp Sci Jinan 252000 Peoples R China

Multi-objective evolutionary algorithms (MOEAs) have become an important choice for solving multi -objective optimization problems. The performance of MOEAs is highly dependent on the algorithm configuration. Therefore, the algorithm configuration is an essential task in the development and application of MOEAs. In this paper, a real-time data-driven automatic design method for configuring an MOEA with minimal user interference is developed. Real-time data are driven in two ways. One lies in that the learning model is constructed based on the elite configurations selected by the Iterated F-Race (I/F-Race), which is used to bias the sampling toward the best configurations. Another is that the decision tree model is constructed by collecting the evaluated configurations in the process of I/F-Race as the data, and used to help identify the potential configurations to improve the sampling quality. In addition, a configurable MOEA (CMOEA) framework is summarized by integrating three general fitness assignment methods. In the experimental study, a case study on a multi-objective hybrid flowshop scheduling problem is conducted. By comparing with other variants of I/F-Race, the developed method is verified to have the ability of evaluating the promising configurations more fully and conceiving the best MOEA. Compared with the famous frameworks and state-of-the-art MOEAs, the proposed CMOEA framework and the automated algorithm show their superiorities based on different performance metrics.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Multi-objective evolutionary algorithm automatic algorithm design Hybrid flowshop scheduling problem I F-race Decision tree

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automatic design of hybrid stochastic local search algorithms for permutation flowshop problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期276卷 409-421页

作者： Pagnozzi, Federico Stutzle, Thomas ULB IRIDIA CP 194-6Av F Roosevelt 50 B-1050 Brussels Belgium

Stochastic local search methods are at the core of many effective heuristics for tackling different permutation flowshop problems (PFSPs). Usually, such algorithms require a careful, manual algorithm engineering effort to reach high performance. An alternative to the manual algorithm engineering is the automated design of effective SLS algorithms through building flexible algorithm frameworks and using automatic algorithm configuration techniques to instantiate high-performing algorithms. In this paper, we automatically generate new high-performing algorithms for some of the most widely studied variants of the PFSP. More in detail, we (i) developed a new algorithm framework, EMILI, that implements algorithm-specific and problem-specific building blocks;(ii) define the rules of how to compose algorithms from the building blocks;and (iii) employ an automatic algorithm configuration tool to search for high performing algorithm configurations. With these ingredients, we automatically generate algorithms for the PFSP with the objectives makespan, total completion time and total tardiness, which outperform the best algorithms obtained by a manual algorithm engineering process. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Scheduling Stochastic local search automatic algorithm design

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On the automatic design of multi-objective particle swarm optimizers: experimentation and analysis

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SWARM INTELLIGENCE 2024年第2-3期18卷 105-139页

作者： Nebro, Antonio J. Lopez-Ibanez, Manuel Garcia-Nieto, Jose Coello, Carlos A. Coello Univ Malaga ITIS Software Ada Byron Res Bldg Malaga 29071 Spain Univ Malaga Dept Lenguajes & Ciencias Comp ETS Ingn Informat Malaga 29071 Spain Univ Manchester Alliance Manchester Business Sch Booth St West Manchester M15 6PB England CINVESTAV IPN Evolutionary Computat Grp Ave IPN 2508 Mexico City 07360 Mexico Tecnol Monterrey Sch Engn & Sci Eugenio Garza Sada 2501 Sur Monterrey 64849 NL Mexico

Research in multi-objective particle swarm optimizers (MOPSOs) progresses by proposing one new MOPSO at a time. In spite of the commonalities among different MOPSOs, it is often unclear which algorithmic components are crucial for explaining the performance of a particular MOPSO design. Moreover, it is expected that different designs may perform best on different problem families and identifying a best overall MOPSO is a challenging task. We tackle this challenge here by: (1) proposing AutoMOPSO, a flexible algorithmic template for designing MOPSOs with a design space that can instantiate thousands of potential MOPSOs;and (2) searching for good-performing MOPSO designs given a family of training problems by means of an automatic configuration tool (irace). We apply this automatic design methodology to generate a MOPSO that significantly outperforms two state-of-the-art MOPSOs on four well-known bi-objective problem families. We also identify the key design choices and parameters of the winning MOPSO by means of ablation. AutoMOPSO is publicly available as part of the jMetal framework.

关键词： automatic algorithm design Multi-objective optimization Particle swarm optimization Benchmarking

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automatic design of constructive heuristics for a reconfigurable distributed flowshop group scheduling problem

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COMPUTERS & OPERATIONS RESEARCH 2024年 161卷

作者： Zhang, Biao Meng, Lei-lei Lu, Chao Han, Yu-yan Sang, Hong-yan Liaocheng Univ Sch Comp Sci Liaocheng Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

This study addresses a reconfigurable distributed flowshop group scheduling problem (RDFGSP), the characteristics of which lie in the reconfigurability of the flowlines, and the families with grouped jobs. Given its NPhard property, we are committed to developing constructive heuristics to meet real-time requirements. By combining different algorithm components, a large number of constructive heuristics can be obtained, rendering the identification of the best heuristic through artificial experimental design quite difficult. To take full advantage of the problem domain knowledge, the iterated F-Race (I/F-Race), an automatic algorithm design (AAD) methodology, is employed to automatically conceive constructive heuristics with minimum human intervention. Initially, a general and configurable meta-algorithm is developed by considering the problemspecific characteristics, which integrates the configurable routing rule, sequencing rule, dispatching rule, and non-delay factor. Subsequently, by using the AAD, the meta-algorithm can be instantiated to a complete constructive heuristic, which can generate active, non-delay, or hybrid schedules. In the experimental study, compared with the full factorial design, the AAD can conceive a much more effective automated heuristic by tuning a much smaller number of configurations. Furthermore, the solution accuracy and efficiency of the generated heuristic are validated in solving small-scale problems by comparison with the commercial solver and other heuristics. The generated heuristic substantiates an advancement of approximately 28-fold in contrast to the best compared heuristic at a very small cost when solving large-scale problems.

关键词： Constructive heuristic automatic algorithm design Distributed flowshop scheduling Reconfigurability Group scheduling

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Evolutionary design of Decision-Tree algorithms Tailored to Microarray Gene Expression Data Sets

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2014年第6期18卷 873-892页

作者： Barros, Rodrigo C. Basgalupp, Marcio P. Freitas, Alex A. de Carvalho, Andre C. P. L. F. Pontificia Univ Catolica Rio Grande do Sul Fac Informat BR-90619900 Porto Alegre RS Brazil Univ Sao Paulo Dept Comp Sci BR-13566590 Sao Carlos SP Brazil Univ Fed Sao Paulo Inst Ciencia & Tecnol BR-12231280 Sao Jose Dos Campos Brazil Univ Kent Dept Comp Sci Canterbury CT2 7NF Kent England

Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure.

关键词： automatic algorithm design classification decision trees evolutionary algorithms hyper-heuristics machine learning

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automatic design of Decision-Tree algorithms with Evolutionary algorithms

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EVOLUTIONARY COMPUTATION 2013年第4期21卷 659-684页

作者： Barros, Rodrigo C. Basgalupp, Marcio P. de Carvalho, Andre C. P. L. F. Freitas, Alex A. Univ Sao Paulo Sao Carlos SP Brazil Univ Fed Sao Paulo Sao Jose Dos Campos Brazil Univ Kent Canterbury Kent England

This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.

关键词： Decision trees hyper-heuristics automatic algorithm design supervised machine learning data mining

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