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作者： Slezak, J. Brno University of Technology

Hybrid evolutionary passive analog circuits synthesis method based on modified Univariate Marginal distribution algorithm (UMDA) and a local search algorithm is proposed in the paper. The modification of the UMDA algorithm which allows to specify the maximum number of the nodes and the maximum number of the components of the synthesized circuit is proposed. The proposed hybrid approach efficiently reduces the number of the objective function evaluations. The modified UMDA algorithm is used for synthesis of the topology and the local search algorithm is used for determination of the parameters of the components of the designed circuit. As an example the proposed method is applied to a problem of synthesis of the fractional capacitor circuit.

关键词： Evolutionary algorithm optimization estimation of distribution algorithm EDA analog circuit design fractional capacitor

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A scheduling algorithm for heterogeneous computing systems by edge cover queue

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KNOWLEDGE-BASED SYSTEMS 2023年第1期265卷

作者： Chen, Yu-meng Liu, Song -lin Chen, Yan-jun Ling, Xiang Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Sichuan Peoples R China

In heterogeneous computing systems, excellent task scheduling algorithms can shorten the task completion time and improve system parallelism. With the large-scale deployment of edge computing, the task scheduling algorithm in heterogeneous edge computing servers has become a critical factor in improving the overall system performance. This paper proposes a new task scheduling algorithm called the edge cover scheduling algorithm (ECSA), which schedules tasks based on the edge cover queue of the directed acyclic graph (DAG) for heterogeneous computing systems. Based on the estimation of distribution algorithm (EDA) and the graph random walk algorithm, the ECSA generates an edge cover queue from DAG. Then, the ECSA uses the heuristics greedy method with low time and computational complexity to allocate the edge cover queue to processors. Theoretical analysis and simulation results on random DAGs and real-world DAGs show that the ECSA can achieve better scheduling results in terms of makespan, the schedule length ratio (SLR), efficiency, and frequency of best results with low time and computational complexity.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Heterogeneous computing system Edge cover queue estimation of distribution algorithm Graph random walk algorithm

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Surrogate-assisted cooperative signal optimization for large-scale traffic networks

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KNOWLEDGE-BASED SYSTEMS 2021年 234卷 1页

作者： Liang, Yongsheng Ren, Zhigang Wang, Lin Liu, Hanqing Du, Wenhao Xi An Jiao Tong Univ Sch Automat Sci & Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Northwest Univ Sch Informat Sci & Technol Xian Peoples R China

Reasonable setting of traffic signals can be very helpful in alleviating congestion in urban traffic networks. Meta-heuristic optimization algorithms have proved themselves to be able to find high quality signal timing plans. However, they generally suffer from performance deterioration when solving large-scale traffic signal optimization problems due to the huge search space and limited computational budget. Directing against this issue, this study proposes a surrogate-assisted cooperative signal optimization (SCSO) method. Different from existing methods that directly deal with the entire traffic network, SCSO first decomposes it into a set of tractable sub-networks, and then achieves signal setting by cooperatively optimizing these sub-networks with a surrogate-assisted optimizer. The decomposition operation significantly narrows the search space of the whole traffic network, and the surrogate-assisted optimizer greatly lowers the computational burden by reducing the number of expensive traffic simulations. By taking Newman fast algorithm, radial basis function and a modified estimation of distribution algorithm as decomposer, surrogate model and optimizer, respectively, this study develops a concrete SCSO algorithm. To evaluate its effectiveness and efficiency, a large-scale traffic network involving crossroads and T-junctions is generated based on a real traffic network. Comparison with several existing meta-heuristic algorithms specially designed for traffic signal optimization demonstrates the superiority of SCSO in reducing the average delay time of vehicles. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Traffic signal optimization Large-scale traffic network Cooperative co-evolution Surrogate model estimation of distribution algorithm

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Reliability evaluation of a composite power system in the presence of renewable generations

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第0期222卷 108396-108396页

作者： Firouzi, Mohsen Samimi, Abouzar Salami, Abolfazl Arak Univ Technol Dept Elect Engn Arak Iran

Power system reliability evaluation plays a vital role in the planning and operation studies by reflecting the system safety level. In this paper, a combination of a non-sequential Monte Carlo simulation (MCS)-based model and an improved estimation of distribution algorithm (EDA) is exploited for evaluating the reliability of the composite power systems considering variability and uncertainty of wind farms (WFs) and Photovoltaic (PV) units. Variability of these resources is defined as the random fluctuation of wind speed and solar irradiation caused by changes in the atmosphere, while their uncertainty results from output power forecast errors. In the proposed model, the states of traditional generating units, transmission lines, WFs, and PV units are constructed using non-sequential MCS. These states can be achieved based on the component failure probability for dispatchable traditional generators and transmission lines along with the Probability distribution Functions (PDFs) of renewable generations. To enhance the computational efficiency of the MCS in the sampling step, the improved EDA upgraded with the Population-Based Incremental Learning (PBIL) algorithm is employed. The proposed mathematical model for reliability evaluation of composite power system is applied to the IEEE RTS 24-bus system, and numerical studies are performed under several case studies. The simulation results confirm the proficiency of the proposed method to improve the computational efficiency, while the high accuracy of reliability evaluation of the composite power system is attained.

关键词： Composite power system Reliability Uncertainty Renewable energy resources Non-sequential Monte Carlo simulation estimation of distribution algorithm

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Aligning heterogeneous optimization problems with optimal correspondence assisted affine transformation for evolutionary multi-tasking

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APPLIED SOFT COMPUTING 2023年 136卷

作者： Chen, An Ren, Zhigang Wang, Muyi Su, Shenyu Yun, Jiaqi Wang, Yichuang Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian Peoples R China Xi An Jiao Tong Univ Sch Automat Sci & Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

Evolutionary multi-tasking optimization (EMTO) aims to boost the overall efficiency of optimizing multiple tasks by triggering knowledge transfer among them. Unfortunately, it may suffer from negative transfer on heterogeneous composite tasks that have low similarity. Some studies try to learn an intertask alignment transformation based on the paired samples from the involved tasks, but risk a failed alignment with improper pairwise methods. To solve this issue, this study proposes an optimal correspondence assisted affine transformation (OCAT) algorithm. OCAT explicitly constructs a mathematical model for the intertask alignment problem and theoretically deduces its optimal solution in an iterative method. As a result, the sample correspondences that enable the learned transformation to achieve the maximum intertask similarity can be located. Besides, a novel approach to deriving the affine transformation formula is also developed for OCAT. The resulting affine alignment transformation will not impair the knowledge contained in the tasks during the alignment process. By integrating OCAT with the estimation of distribution algorithm, this study finally develops a many-tasking optimization algorithm named MaT-EDA, where the solutions from other tasks are explicitly transferred as the samples for estimating the current distribution model. Extensive simulation studies have indicated that OCAT can significantly enhance the performance of EMTO, and MaT-EDA also achieves impressive many-tasking optimization performance.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Evolutionary multi-tasking Intertask alignment Affine transformation Many-tasking optimization estimation of distribution algorithm

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Manufacturing rescheduling after crisis or disaster-caused supply chain disruption

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COMPUTERS & OPERATIONS RESEARCH 2023年第1期157卷

作者： Bo, Hongguang Chen, Xiao Alison Luo, Qian Wang, Wenpeng Dalian Univ Technol Sch Econ & Management 2 Linggong Rd Dalian Peoples R China Univ New Hampshire Peter T Paul Coll Business & Econ 10 Garrison Ave Durham NH 03824 USA Xian Jiaotong Liverpool Univ Int Business Sch Suzhou 111 RenAi Rd Suzhou Peoples R China

In this paper, we study the problems a repair shop has with rescheduling after major supply disruptions. The repair shop provides repair and maintenance services to its customers. After a major disruption to production, the repair shop faces delays in production and order delivery due to shortages in materials and/or labor, which requires rescheduling of all the unfinished parts. We observe that the finished parts incur high holding costs until the entire order is completed, while any unfinished parts (in the form of raw material or work-in-progress) incur low holding costs until production starts. Moreover, the repair shop incurs a setup cost when switching between different types of parts. Considering these new features, we formulate the rescheduling problem for the repair shop under a coordinated supply chain as an integer program to minimize the total tardiness, setup cost, and holding cost. To solve the model, we propose an innovative two-stage genetic algorithm, which utilizes the estimation of distribution algorithm (EDA) to improve the search process of the optimal solution. We test the performance of this algorithm on a dataset generated from the order data of a heavy machinery maintenance provider. The numerical results show that our model generates solutions that outperform the initial schedule, which was obtained by minimizing holding and setup costs without disruption. In addition, using other closely-related genetic algorithms as benchmarks, we show that our algorithm outperforms the benchmarks without sacrificing the computational time. We also discuss an extension of the main model by considering the recovery of productivity in terms of processing time.

关键词： Production rescheduling Supply chain disruption Two-stage genetic algorithm estimation of distribution algorithm Productivity recovery

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An effective hyper heuristic-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem

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APPLIED SOFT COMPUTING 2023年 135卷

作者： Song, Hong-Bo Yang, You-Hong Lin, Jian Ye, Jing-Xuan Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China

In this paper, an effective Hyper Heuristic-based Memetic algorithm (HHMA) is proposed to solve the Distributed Assembly Permutation Flow-shop Scheduling Problem (DAPFSP) with the objective of minimizing the maximum completion time. A novel searching-stage-based solution representation scheme is presented for both improving the search efficiency and maintaining potential solutions. In the global search stage, estimation of distribution algorithm (EDA) is employed as the high level strategy of EDA-based Hyper Heuristic (EDAHH) to find promising product sequences for further exploitation. Based on the newly found knowledge of critical-products, several efficient Low-Level Heuristics (LLHs) are well designed to construct the LLH set so that the powerful exploration ability of the EDAHH can be guaranteed. A simulated-annealing-like type of acceptance criterion is also embedded into each LLH to avoid premature convergence. Then a Critical-Products-based Referenced Local Search (CP-RLS) method is proposed to improve the quality of superior sub-population by operating on the sub-job-sequences derived from the critical products. The benefit of the presented CP-RLS lies in the excellent exploitation ability with substantially reduced computational cost. Finally, performance evaluation and comparison are both carried out on a benchmark set and the results demonstrate the superiority of HHMA over the state-of-the-art algorithms for the DAPFSP.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Distributed assembly flow-shop scheduling estimation of distribution algorithm Hyper heuristic Memetic algorithm Critical-products-based local search

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GA-EDA: Hybrid Design Space Exploration Engine for Multicore Architecture

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2021年第10期30卷 2150181-2150181页

作者： Waris, Hira Ahmad, Ayaz Qadri, Muhammad Yasir Raja, Gulistan Malik, Tahir Nadeem Univ Engn & Technol Taxila Pakistan COMSATS Univ Islamabad Dept Elect & Comp Engn Wah Campus Wah Cantt Pakistan Univ Essex Colchester Essex England

Emergence of modern multicore architectures has made runtime reconfiguration of system resources possible. All reconfigurable system resources constitute a design space and the proper selection of configuration of these resources to improve the system performance is known as Design Space Exploration (DSE). This reconfiguration feature helps in appropriate allocation of system resources to improve the efficiency in terms of performance, energy consumption, throughput, etc. Different techniques like exhaustive search of design space, architect's experience, etc. are used for optimization of system resources to achieve desired goals. In this work, we hybridized two optimization algorithms, i.e., Genetic algorithm (GA) and estimation of distribution algorithm (EDA) for DSE of computer architecture. This hybrid algorithm achieved optimal balance between two objectives (minimal energy consumption and maximal throughput) by using decision variables such as number of cores, cache size and operating frequency. The final set of optimal solutions proposed by this GA-EDA hybrid algorithm is explored and verified by running different benchmark applications derived from SPLASH-2 benchmark suite on a cycle level simulator. The significant reduction in energy consumption without extensive impact on throughput in simulation results validate the use of this GA-EDA hybrid algorithm for DSE of multicore architecture. Moreover, the simulation results are compared with that of standalone GA, EDA and fuzzy logic to show the efficiency of GA-EDA hybrid algorithm.

关键词： Design space exploration multicore architecture estimation of distribution algorithm genetic algorithm

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An improved quantum-behaved particle swarm optimization and its application to medical image registration

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2011年第6期88卷 1208-1223页

作者： Zhou, Di Sun, Jun Lai, Choi-Hong Xu, Wenbo Lee, Xiaoguang Jiangnan Univ Sch Informat Technol Wuxi 214122 Peoples R China Univ Greenwich Sch Comp & Math Sci London SE18 6PF England CREALIFE Med Technol Inc Dept Res & Dev Beijing Peoples R China

This paper investigates the quantum-behaved particle swarm optimization (QPSO) algorithm from the perspective of estimation of distribution algorithm (EDA) which reveals the reason of QPSO's superiority. A revised QPSO (RQPSO) technique with a novel iterative equation is also proposed. The modified technique is deduced from the distribution function of the sum of two random variables with exponential and normal distribution, respectively. We present a diversity-controlled RQPSO (DRQPSO) algorithm, which helps prevent the evolutionary algorithms' tendency to be easily trapped into local optima as a result of rapid decline in diversity. Both the RQPSO and DRQPSO are tested on three benchmark functions, as well as in medical image registration for performance comparison with the particle swarm optimization and QPSO.

关键词： quantum-behaved particle swarm optimization estimation of distribution algorithm hybrid probability distribution diversity control mutual information image registration

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Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network

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APPLIED SOFT COMPUTING 2024年 166卷

作者： Gao, Yuan Kasihmuddin, Mohd Shareduwan Mohd Chen, Ju Zheng, Chengfeng Romli, Nurul Atiqah Mansor, Mohd. Asyraf Zamri, Nur Ezlin Chengdu Univ Tradit Chinese Med Sch Med Informat Engn Chengdu 610037 Peoples R China Univ Sains Malaysia Sch Math Sci George Town 11800 Malaysia Univ Sains Malaysia Sch Distance Educ George Town 11800 Malaysia Univ Putra Malaysia Fac Sci Dept Math & Stat Serdang 43400 Selangor Malaysia

This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the estimation of distribution algorithm and a new pheromone updating formula. It aimed to optimize the performance of G-type random high-order satisfiability logic structures embedded in Discrete Hopfield Neural Networks, thereby enhancing the efficiency of the Hopfield Neural Network learning algorithm. Through comparative analysis with other metaheuristic algorithms, our model demonstrated superior performance in terms of global convergence, time complexity, and algorithm complexity. Additionally, we evaluated the learning phase, retrieval phase, and similarity analysis using various ratios of literals and clauses. It was shown that our proposed model exhibits stronger search ability compared to other metaheuristic algorithms and Exhaustive Search. Our model enhanced the efficiency of the learning phase, resulting in the number of global solutions accounting for 100 %, and significantly improved the global solution diversity. These advancements contributed to the efficiency of the model in convergence, rendering it applicable to a wide range of nonlinear classification and prediction problems.

关键词： Ant colony optimization estimation of distribution algorithm Discrete Hopfield neural network Metaheuristic algorithms Classification

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