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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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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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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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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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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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estimation of distribution algorithm with path relinking for the blocking flow-shop scheduling problem

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ENGINEERING OPTIMIZATION 2018年第5期50卷 894-916页

作者： Shao, Zhongshi Pi, Dechang Shao, Weishi Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Jiangsu Peoples R China

This article presents an effective estimation of distribution algorithm, named P-EDA, to solve the blocking flow-shop scheduling problem (BFSP) with the makespan criterion. In the P-EDA, a Nawaz-Enscore-Ham (NEH)-based heuristic and the random method are combined to generate the initial population. Based on several superior individuals provided by a modified linear rank selection, a probabilistic model is constructed to describe the probabilistic distribution of the promising solution space. The path relinking technique is incorporated into EDA to avoid blindness of the search and improve the convergence property. A modified referenced local search is designed to enhance the local exploitation. Moreover, a diversity-maintaining scheme is introduced into EDA to avoid deterioration of the population. Finally, the parameters of the proposed P-EDA are calibrated using a design of experiments approach. Simulation results and comparisons with some well-performing algorithms demonstrate the effectiveness of the P-EDA for solving BFSP.

关键词： Flow-shop scheduling with blocking estimation of distribution algorithm path relinking makespan

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estimation of distribution algorithm for permutation flow shops with total flowtime minimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2011年第4期60卷 706-718页

作者： Zhang, Yi Li, Xiaoping Southeast Univ Sch Engn & Comp Sci Nanjing 211189 Peoples R China Southeast Univ Minist Educ Key Lab Comp Network & Informat Integrat Nanjing 211189 Peoples R China

In this paper, an estimation of distribution algorithm (EDA) is proposed for permutation flow shops to minimize total flowtime. Longest Common Subsequence (LCS) is incorporated into the probability distribution model to mine good "genes". Different from common EDAs, each offspring individual is produced from a seed, which is selected from the population by the roulette method. The LCS between the seed individual and the best solution found so far is regarded as good "genes". which are inherited by offspring with a probability less than 100% to guarantee the population diversity. An effective Variable Neighborhood Search (VNS) is integrated into the proposed EDA to further improve the performance. Experimental results show that the inheritance of good "genes" obtained by LCS can improve the performance of the proposed EDA. The proposed hybrid EDA outperforms other existing algorithms for the considered problem in the literature. Furthermore, the proposed hybrid EDA improved 42 out of 90 current best solutions for Taillard benchmark instances. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： estimation of distribution algorithm Permutation flow shops Total flowtime Meta-heuristic

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estimation of distribution algorithm combined with chaotic sequence for dynamic optimisation problems

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2017年第1期8卷 12-19页

作者： Yu, Fahong Li, Wenping Tao, Jiang Deng, Kun Ma, Longhua He, Feng Jiaxing Univ Dept Coll Math & Informat Engn Jiaxing Zhejiang Peoples R China Zhejiang Univ Ningbo Inst Technol Hangzhou Zhejiang Peoples R China

To track the optima in dynamic environments with estimation of distribution algorithm, maintenance of the diversity of the population is an essential requirement. Taking this point into consideration, this paper proposes an estimation of distribution algorithm combined with a chaotic sequence (CEDA) for dynamic optimisation problems. In CEDA, a chaotic sequence is introduced to maintain the diversity of population and enhance improve the local search ability. Many numerical experiments are reported in order to compare the performance of the CEDA with the self-adaptive approach by other authors. The numerical results show that the performance of our algorithm is superior to that of other published algorithms on two dynamic benchmark problems.

关键词： estimation of distribution algorithm chaotic sequence dynamic optimisation problems

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estimation of distribution algorithm for Scheduling in Uplink Multiuser Wireless Communication System

Estimation of Distribution Algorithm for Scheduling in Uplin...

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IEEE Symposium on Computational Intelligence in Scheduling

作者： Naeem, M. Lee, D. C. Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9781424427574

In this paper, we present a real-time low-complexity user scheduling scheme for uplink multiuser multiple input multiple output (MIMO) wireless communication systems. We apply estimation of distribution algorithm (EDA) for user scheduling problem. The computational complexity of exhaustive search for user scheduling problem grows exponentially with the number of users. The proposed EDA has low computational complexity and can find a near-optimum solution in real time. In addition to applying the general EDA to user scheduling, we also present a specific improvement to EDA, which increases the likelihood of getting the optimum solution by generating cyclic shifted initial population.

关键词： estimation of distribution algorithm User Scheduling MIMO wireless communication

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