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2nd Asia Conference on algorithms, Computing and Machine Learning (CACML)

作者： Gao, Yuan Y. G. Zheng, Chengfeng C. Z. Chen, Ju J. C. Guo, Yueling Y. G. Chengdu Univ Tradit Chinese Med Sch Med Informat Engn Chengdu 611137 Peoples R China Univ Sains Malaysia Sch Math Sci Minden 11800 Penang Malaysia

ISBN: (纸本)9781450399449

The Discrete Hopfield Neural Network introduces a G-Type Random 3 Satisfiability logic structure, which can improve the flexibility of the logic structure and meet the requirements of all combinatorial problems. Usually, Exhaustive Search (ES) is regarded as the basic learning algorithm to search the fitness of neurons. To improve the efficiency of the learning algorithm. In this paper, we introduce the estimation of distribution algorithm (EDA) as a learning algorithm for the model. To study the learning mechanism of EDA to improve search efficiency, this study focuses on the impact of EDA on the model under different proportions of literals and evaluates the performance of the model at different phases through evaluation indicators. Analyze the effect of EDA on the synaptic weights and the global solution. From the discussion, it can be found that compared with ES, EDA has a larger search space at the same efficiency, which makes the probability of obtaining satisfactory weights higher, and the proportion of global solutions obtained is higher. Higher proportions of positive literals help to improve the model performance.

关键词： Hopfield Neural Network Exhaustive Search estimation of distribution algorithm Meta-heuristic

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Scheduling of stochastic distributed hybrid flow-shop by hybrid estimation of distribution algorithm and proximal policy optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 271卷

作者： Luo, Lin Yan, Xuesong China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China Minist Educ Engn Res Ctr Nat Resource Informat Management & Di Wuhan 430074 Peoples R China

Flexible manufacturing systems face major challenges in improving productivity when dealing with uncertain factors. Consequently, it is crucial to address production scheduling problems that involve these uncertain elements. In this paper, a hybrid optimization algorithm of estimation of distribution algorithm and proximal policy optimization (EDA/PPO) is proposed to solve the stochastic distributed hybrid flow-shop scheduling problem (SDHFSP) with processing time perturbation in order to minimize makespan. A hybrid initialization strategy is developed to ensure population diversity. In the EDA component, a three-dimensional (3-D) probability matrix corresponding to the solution representation is utilized. For the PPO component, 44 state features are selected to characterize the environmental situation, 7 composite rule-based actions are defined for job sequencing, and unique reward associated with scheduling objective is designed. Through extensive experimentation, we analyze the effects of key parameters and determine optimal numerical combinations. Comparative numerical experiments with existing algorithms demonstrate the effectiveness and robustness of the EDA/PPO approach. This study offers valuable insights for production managers addressing stochastic distributed manufacturing with processing time perturbation.

关键词： Stochastic distributed hybrid flow-shop scheduling problem estimation of distribution algorithm Proximal policy optimization Processing time perturbation

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Improving the exploitation in the estimation of distribution algorithm through simulated annealing strategies for solar energy problems

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KNOWLEDGE-BASED SYSTEMS 2025年 310卷

作者： Ramos-Frutos, Jorge Oliva, Diego Miguel-Andres, Israel Navarro, Mario A. Valdivia, Arturo Zapotecas-Martinez, Saul Campos-Pena, Diego Ctr Innovac Aplicada Tecnol Competit Ind Delta Leon 37545 Guanajuato Mexico Univ Guadalajara Dept Ingn Electrofoton CUCEI Guadalajara 44430 Jalisco Mexico CIATEC Lab Nacl CONAHCYT Biomecan Cuerpo Humano Ind Delta Leon 37545 Guanajuato Mexico Inst Nacl Astrofis Opt & Electr Comp Sci Dept Santa Maria Tonantzintla 72840 Puebla Mexico

estimation of distribution algorithms (EDAs) have gained substantial attention in optimization due to their ability to efficiently explore complex search spaces by modeling promising regions through probability distributions. However, their effectiveness can be further enhanced by integrating complementary optimization techniques. This paper proposes a hybrid approach that combines the strengths of an EDA with the powerful optimization capabilities of Simulated Annealing (SA) in synergy with a mutation operator and the initialization using the Latin Hypercube. It is called directed EDA (R-EDA). The hybrid algorithm permits abetter exploration since the early optimization stages. It weighs the diversity-preserving mechanisms of EDAs while incorporating the robust exploitation abilities of SA in combination with a mutation operator. The synergy of the elements introduced in the R-EDA permits an algorithm that can provide accurate solutions to complex and high- dimensional problems. The R-EDA is tested over a series of experiments on 36 benchmark optimization problems in 30, 50, and 100 dimensions with unimodal, multimodal, composite, and shifted optimization landscapes. Additionally, a comparison of the application of modeling solar cells using three different approaches is presented. The experimental results demonstrate the efficacy of R-EDA in achieving superior optimization performance compared with well-known state-of-the-art algorithms over the benchmark function and solar cells modeling application. Furthermore, the impact of key algorithmic parameters is analyzed, providing insights into the synergistic effects of the hybridization process.

关键词： estimation of distribution algorithm Global optimization Latin Hypercube Simulated Annealing Solar cells

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A collaborative estimation of distribution algorithm based on fitness landscape characteristic

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Zhao, Fuqing Li, Mengjie Yu, Yang Zhu, Ningning Xu, Tianpeng Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Jiangsu Univ Technol Sch Elect Informat Engn Changzhou 213001 Peoples R China

The estimation of distribution algorithm (EDA) employs a probabilistic model to characterize the distribution of promising solutions, making it effective for tackling complex optimization problems. However, sampling based on a single probabilistic model generates a population with poor diversity and adequately fails to explore the solution space, leading to premature convergence of the algorithm. A collaborative estimation of distribution algorithm based on fitness landscape (FL-CEDA) is proposed to improve the performance of EDA in complex optimization problems. The shift of the mean and the adaptive shrinking of the covariance matrix are combined to guide the rapid evolution of the population to promising regions. The collaboration-operation of integrating mirrored sampling and Gaussian sampling is exploited to balance exploration and exploitation. By quantifying the ruggedness of the local fitness landscape, the sampling method that fits the problem characteristics is adaptively selected to improve the sampling efficiency. The performance of the FL-CEDA is verified on the CEC2017 benchmark test suite. The results illustrate that the FL-CEDA outperforms other state-of-the-art algorithms.

关键词： estimation of distribution algorithm Fitness landscape Mirrored sampling Collaboration-operation

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A knowledge-guided estimation of distribution algorithm for energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 88卷

作者： Wu, Chu-ge Zhang, Ruochen Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

The assembly line serves as a fundamental system in discrete production. To address the challenges in balancing robotic assembly lines, timely part feeding, and the need for sustainable manufacturing, this paper studies an energy -efficient Joint Robotic Assembly Line Balancing and Feeding Problem (JRALB-FP) with the criteria of minimizing both cycle time and total fuel consumption cost. Considering the complexity of the multi -problem and multi -objective optimization, a knowledge -guided estimation of distribution algorithm (KEDA) is proposed to solve energy -efficient JRALB-FP. First, a probability model of EDA for task -workstation allocation paired with a heuristic method -based sampling mechanism is created. Using this probability model, a specific encoding mechanism is designed for part -trailer allocation, and good initial solutions are produced. Second, several properties of the bi-objective problem are analyzed to guide the design of local search operators for both objectives optimization. Third, the updating mechanism of the probability model is designed to learn from the elite solutions. Fourth, two knowledge -guided local search operators are designed and implemented to exploit better non -dominated solutions sufficiently. A design of experiment is carried out to determine the parameters. Extensive computational tests and comparisons with the state-of-the-art multi -objective algorithms are carried out, which verify the effectiveness of the knowledge -guided local search operators, the problem -oriented heuristic -based sampling mechanism, and the special designs of the KEDA in solving the energy -efficient JRALB-FP.

关键词： Robotic assembly line balancing Part feeding estimation of distribution algorithm Production Optimization

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Q-learning based estimation of distribution algorithm for scheduling distributed heterogeneous flexible flow-shop with mixed buffering limitation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 149卷

作者： Xuan, Hua Zheng, Qian-Qian Lv, Lin Li, Bing Zhengzhou Univ Sch Management Zhengzhou 450001 Peoples R China

There are growing interests in the distributed shop scheduling research owing to the diversification of market demand. However, most prevailing studies disregard the synergistic influence of mixed buffering and due window on production efficiency. To reduce cost loss caused by delay in due window, this paper studies a distributed heterogeneous flexible flow-shop scheduling with mixed buffering limitation, i.e., finite buffers and no-wait requirements. The motivation of this work is to fill in void and offer practical insights for exploring how to intelligently implement, optimize and deploy a distributed production system. A mathematical model is established, aiming to minimize total weighted earliness and tardiness cost. An innovative Q-learning based estimation of distribution algorithm (QLEDA) is well-designed to address this problem. The QLEDA proposes well-tailored three-stage dynamic decoding and opposition-based learning to decode and promote the job sequence group. To balance global and local searchability of QLEDA, we introduce problem-specific Q-learning and Chebyshev chaotic mapping. To build a probability model of self-adaptation and self-selection, the job sequence group implements discrete actions by interacting with distributed environment and state space through Q-learning. Numerous experiments demonstrate that the QLEDA can generate more satisfactory results over other three well-performing rivals. The finding corroborates the applicability and effectiveness of presented QLEDA in solving the considered problem.

关键词： Distributed heterogeneous flexible flow-shop Finite buffers and no-wait limitations Total weighted earliness and tardiness cost estimation of distribution algorithm Q -learning

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Test Data Generation for Mutation Testing Based on Markov Chain Usage Model and estimation of distribution algorithm

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2024年第3期50卷 551-573页

作者： Wei, Changqing Yao, Xiangjuan Gong, Dunwei Liu, Huai China Univ Min & Technol Sch Math Xuzhou 221116 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic 3122 Australia

Mutation testing, a mainstream fault-based software testing technique, can mimic a wide variety of software faults by seeding them into the target program and resulting in the so-called mutants. Test data generated in mutation testing should be able to kill as many mutants as possible, hence guaranteeing a high fault-detection effectiveness of testing. Nevertheless, the test data generation can be very expensive, because mutation testing normally involves an extremely large number of mutants and some mutants are hard to kill. It is thus a critical yet challenging job to find an efficient way to generate a small set of test data that are able to kill multiple mutants at the same time as well as reveal those hard-to-detect faults. In this paper, we propose a new approach for test data generation in mutation testing, through the novel applications of the Markov chain usage model and the estimation of distribution algorithm. We first utilize the Markov chain usage model to reduce the so-called mutant branches in weak mutation testing and generate a minimal set of extended paths. Then, we regard the problem of generating test data as the problem of covering extended paths and use an estimation of distribution algorithm based on probability model to solve the problem. Finally, we develop a framework, TAMMEA, to implement the new approach of generating test data for mutation testing. The empirical studies based on fifteen object programs show that TAMMEA can kill more mutants using fewer test data compared with baseline techniques. In addition, the computation overhead of TAMMEA is lower than that of the baseline technique based on the traditional genetic algorithm, and comparable to that of the random method. It is clear that the new approach improves both the effectiveness and efficiency of mutation testing, thus promoting its practicability.

关键词： Testing Markov processes estimation Software algorithms Genetic algorithms Data models Costs Mutation testing weak mutation test data generation Markov chain usage model coverage of extended path estimation of distribution algorithm

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Multi-objective fitness landscape-based estimation of distribution algorithm for distributed heterogeneous flexible job shop scheduling problem

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APPLIED SOFT COMPUTING 2025年 171卷

作者： Zhao, Fuqing Li, Mengjie Zhu, Ningning Xu, Tianpeng Jonrinaldi Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Univ Andalas Dept Ind Engn Padang 25163 Indonesia

The distributed heterogeneous flexible job shop scheduling problem with sequence-dependent setup time (DHFJSP-SDST) exists in discrete manufacturing systems. However, without incorporating problem knowledge to achieve effective exploration, many existing algorithms struggle to find high-quality solutions. A multi-objective fitness landscape-based estimation of distribution algorithm (MFLEDA) is introduced to address the DHFJSPSDST and minimize both makespan and total energy consumption (TEC). For the three sub-problem, three probabilistic models are utilized to generate new solutions to overcome the issue of premature convergence. The multi-objective fitness landscape is exploited to extract problem knowledge and achieve adaptive selection of local search operators. Moreover, an energy-saving strategy is proposed to further reduce energy consumption. Twenty instances are exploited to evaluate the effectiveness of the MFLEDA. The experimental results indicate that the MFLEDA outperforms the comparison algorithms in solving DHFJSP-SDST.

关键词： estimation of distribution algorithm Multi-objective fitness landscape Distributed flexible job shop scheduling Sequence-dependent setup time

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Adaptive archive exploitation for Gaussian estimation of distribution algorithm

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APPLIED INTELLIGENCE 2025年第5期55卷 1-31页

作者： Zhao, Dongmin Tian, Yi Zeng, Lingshun Liang, Chunquan Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Shaanxi Engn Res Ctr Agr Informat Intelligent Perc Shaanxi Peoples R China

The Gaussian estimation of distribution algorithm (GEDA) is a fundamental evolutionary algorithm widely applied to continuous optimization problems but often encounters premature convergence. While external archives have been introduced to mitigate this issue, they frequently misuse historical information, leading to suboptimal results. To address this, we propose an Adaptive Archive Exploitation for GEDA (AAE-GEDA). AAE-GEDA incorporates two key mechanisms: adaptive selection of archive quantities (ASAQ) and angle skewness-landscape (ASL) eigenvalue adaptation. ASAQ selectively utilizes a subset of solutions from the archive to improve the accuracy of covariance estimation, preventing the algorithm from being misled by outdated or irrelevant information. ASL dynamically adjusts the search range, ensuring a balanced trade-off between exploration and exploitation. Experimental results on the IEEE CEC2014 and CEC2017 test suites demonstrate that AAE-GEDA consistently outperforms state-of-the-art evolutionary algorithms.

关键词： estimation of distribution algorithm Adaptive selection of archive quantities Angle skewness-landscape eigenvalue adaptation

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Characteristics-based estimation of distribution algorithm for the steelmaking-refining-continuous casting scheduling problem in the real-world steel plants

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COMPUTERS & OPERATIONS RESEARCH 2025年 181卷

作者： Zhang, Long Hu, Xi Wu, Xiaoming Guangxi Key Lab Automat Detect Technol & Instrumen Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China

As a key production process in the steel industry, excellent scheduling of Steelmaking-refining-Continuous Casting (SCC) manufacturing process can improve production efficiency, shorten the steel production cycle, and reduce the production cost for steel enterprises. This paper presents a Characteristics-based estimation of distribution algorithm (CEDA) for the SCC scheduling problem in the real-world steel plants. Considering the processing characteristics of the continuous casting machine, a novel caster-based encoding scheme and an improved decoding scheme are proposed. Also, a distance concept is introduced to mitigate the impact of similar individuals on the probability model, and an importance-based probability model updating mechanism is designed to increase the impact of excellent individual on the probability model. Furthermore, an individual sampling scheme with enhanced probability is constructed to ensure continuous processing of the continuous casting machine as much as possible. Finally, this paper designs a limited insertion operation in the local search to address the exploitation of the proposed algorithm. Extensive numerical simulations demonstrate that the proposed CEDA for the SCC scheduling process is more efficient than some state-of-the-art algorithms in the literature.

关键词： Steelmaking-refining-continuous casting estimation of distribution algorithm Scheduling Enhanced probability

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