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A hierarchical knowledge guided backtracking search algorithm with self-learning strategy

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 102卷 104268-104268页

作者： Zhao, Fuqing Zhao, Jinlong Wang, Ling Cao, Jie Tang, Jianxin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China

To improve the performance of the backtracking search optimization algorithm (BSA), a multi-population cooperative evolution strategy guided BSA with hierarchical knowledge (HKBSA) is proposed in this paper. According to the domain knowledge of the candidates in objective space, the population is divided into the dominant population, the ordinary population and the inferior population. The information between the sub populations has interacted with the evolution processes of the three sub-populations. The individuals in the dominant population are maintained as the optimal solutions and are utilized to guide the evolution of the other two sub-populations. A multi-strategy mutation mechanism is applied to solve non-separable problems. The distribution vector of inferior individuals is constructed by sampling, and a mechanism of the individual generation with feedback is proposed by combining self-learning strategy and elite learning strategy. The convergence of HKBSA is analyzed with the Markov model. Compared with the state-of-the-art BSA variants, HKBSA outperforms other algorithms in terms of the speed of convergence, solution accuracy and stability.

关键词： backtracking search algorithm Hierarchical knowledge Multi-strategy mutation Probability vector Self-learning strategy

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An optimal block knowledge driven backtracking search algorithm for distributed assembly No-wait flow shop scheduling problem

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APPLIED SOFT COMPUTING 2021年 112卷 107750-107750页

作者： Zhao, Fuqing Zhao, Jinlong Wang, Ling Tang, Jianxin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China

The distributed assembly flow shop scheduling problem (DAFSP) is an important scenario in manufacturing system. In this paper, an optimal block knowledge driven backtracking search algorithm (BKBSA) is proposed to solve the distributed assembly No-wait flow shop scheduling problem (DANWFSP) with the objective of minimizing the completion time of assembly process. In BKBSA, three constructive heuristics are proposed to generate a competitive initial solution. Block-shifting based on block knowledge is embedded in the mutation strategy of BKBSA. The proposed block-shifting ensures that the optimal subsequence of a candidate solution is not destroyed in the mutation operation. The similarity between candidate solutions is utilized as feedback indicator to control the utilization of block-shifting. In addition, the VND algorithm based on factory-to-factory is proposed to further improve the optimal solution. Finally, the BKBSA and the other three state-of-the-art algorithms for DANWFSP are tested on 810 large-scale instances and 900 small-scale instances. The statistical analysis results show that BKBSA is an effective algorithm to solve DANWFSP. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Distributed assembly No-wait flow shop scheduling Job block knowledge Constructive heuristics backtracking search algorithm

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A simulation-based modified backtracking search algorithm for multi-objective stochastic flexible job shop scheduling problem with worker flexibility

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APPLIED SOFT COMPUTING 2021年第PartB期113卷 107960-107960页

作者： Gnanavelbabu, A. Caldeira, Rylan H. Vaidyanathan, T. Anna Univ Dept Ind Engn CEG Campus Chennai 600025 Tamil Nadu India

The flexible shop scheduling problem (FJSSP) has been an extensively studied and applied manufacturing systems in recent years. However, research has mostly focused on addressing the FJSSP in its basic form by relaxing several practical constraints. In this work, we consider FJSSP with worker flexibility and uncertain processing times. A simheuristic approach addresses the multi-objective stochastic FJSSP with worker flexibility, minimizing the expected makespan and standard deviation of makespan. This approach integrates the Monte Carlo simulation (MCS) into the proposed multi-objective modified backtracking search algorithm (MBSA) framework. MBSA employs an effective population initialization strategy, dynamic mutation and crossover operators, and a transfer criterion. MCS evaluates' promising' solutions generated by MBSA to guide the search process. Extensive experiments are performed considering three FJSSP benchmark data sets. At first, the best parameters for the MBSA are identified using the Taguchi method. Then, for the deterministic variant of the problem, MBSA is compared with two other metaheuristics to demonstrate its effectiveness. Finally, the stochastic variant is investigated considering two factors: variation level and the probability distribution of the processing times. Computational results statistically show a significant effect of these factors on the objectives, demonstrating the importance of selecting an appropriate probability distribution in an uncertain environment. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Flexible job shop scheduling problem Uncertain processing times Simheuristic approach Worker flexibility backtracking search algorithm

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Modeling beach realignment using a neuro-fuzzy network optimized by a novel backtracking search algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第6期31卷 1747-1763页

作者： Chatzipavlis, Antonios Tsekouras, George E. Trygonis, Vasilis Velegrakis, Adonis F. Tsimikas, John Rigos, Anastasios Hasiotis, Thomas Salmas, Constantinos Univ Aegean Dept Marine Sci Univ Hill Mitilini 81100 Greece Univ Aegean Dept Cultural Technol & Commun Univ Hill Mitilini 81100 Greece Univ Aegean Dept Math Karlovassi 83200 Samos Greece Univ Ioannina Dept Mat Sci & Engn GR-45110 Ioannina Greece

Beach realignment is caused by sediment movement across and along the beach due to the ever-changing incident waves. Its driving mechanisms are highly nonlinear and cannot be easily resolved by numerical process-response modeling which may also involve high computational costs. In this contribution, an alternative approach is developed to cope with the nonlinearity of beach realignment, which utilizes a neuro-fuzzy neural network optimized by a novel backtracking search algorithm. The network comprises multiple layers operating in sequence and establishes input-output relationships in terms of first-order fuzzy rules. The proposed backtracking search algorithm improves its performance by effectively modifying the existing mutation and crossover operations of the standard algorithm. A novel experimental setup was deployed in a touristic beach of Santorini island, Greece, to collect high frequency morphological and hydrodynamic information and generate a data set described by few representative input variables. Three additional networks were designed using the same data set. Standard criteria and nonparametric statistical analysis showed that the proposed approach outperforms all other tested methods in the case of modeling beach realignment, achieving also a significant improvement over previous efforts.

关键词： backtracking search algorithm Neuro-fuzzy network Shoreline realignment Beach morphodynamics

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Improving backtracking search algorithm with variable search strategies for continuous optimization

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APPLIED SOFT COMPUTING 2019年 80卷 567-578页

作者： Tsai, Hsing-Chih Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei Taiwan

The backtracking search algorithm (BSA), a relatively new evolutionary algorithm (EA), has been shown to be a competitive alternative to other population-based algorithms. To effectively solve a variety of optimization problems, this paper suggests ten mutation strategies and compares the performance of selection mechanisms in employing these strategies. Moreover, following the original BSA design, new parameters of historical mean and best positions are proposed in order to implement several additional mutation strategies. In addition, as recommended in the literature, a one-dimensional crossover scheme is enacted for greedy strategies in order to prevent premature convergence. Furthermore, three settings for search factors of mutation strategies are proposed. As a result, improved BSA versions that employed, respectively, ten and four mutation strategies were found to significantly facilitate the ability of BSA to handle optimization tasks of different characteristics. The experimental results show that the proposed versions outperformed the basic BSA in terms of achieving high convergence speed in the early stage, reaching the convergence precision and plateau with better scores, and performing perfectly on tests of composition functions. In addition, the improved BSA versions outperformed five popular, nature-inspired algorithms in terms of achieving the best convergence precision and performing perfectly on six composition functions. (C) 2019 Elsevier B.V. All rights reserved.

关键词： backtracking search algorithm Continuous optimization Mutation strategies search factors One-dimensional crossover

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An improved backtracking search algorithm for casting heat treatment charge plan problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第3期30卷 1335-1350页

作者： Zhou, Jianxin Ye, Hu Ji, Xiaoyuan Deng, Weilin Huazhong Univ Sci & Technol State Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Hubei Peoples R China

This study investigates the optimization of the charge plan in casting heat treatment. The optimization problem is formulated as a 0-1 integer programming model aiming at maximizing the utilization of the furnaces, minimizing the holding temperature differences and the overall delivery deadline of castings in a furnace. To approach the mathematical model, a two-steps solution methodology is designed. First, the feasible casting candidate sets are generated in consideration of the holding temperature and cooling mode constraints. Then, an improved backtracking search algorithm (IBSA) is proposed to obtain optimal charge plan for each feasible candidate set. The best one among the optimal charge plans obtained by IBSA is selected as the final charge plan. In IBSA, a mapping mechanism is applied to make original backtracking search algorithm (BSA) suitable to discrete problems. Improvements that consist of the modification of historical population updating mechanism, the hybrid of mutation and crossover strategy of difference evaluation algorithm, a greedy local search algorithm and the re-initialization operator are also made to enhance the exploitation and exploration ability of IBSA. The comparisons of simulation experiments demonstrate the effectiveness of the proposed model and the performance of the proposed algorithm.

关键词： Charge plan Heat treatment backtracking search algorithm Difference evaluation Greedy local search

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backtracking search algorithm for color image multilevel thresholding

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SIGNAL IMAGE AND VIDEO PROCESSING 2018年第2期12卷 385-392页

作者： Pare, S. Bhandari, A. K. Kumar, A. Bajaj, V. PDPM Indian Inst Informat Technol Design & Mfg Jabalpur 482005 India GIST Sch Elect Engn & Comp Sci Gwangju South Korea Natl Inst Technol Patna 800005 Bihar India

Multilevel thresholding of the color images such as natural and satellite images becomes a challenging task due to the inherent fuzziness and ambiguity in such images. To address this issue, a modified fuzzy entropy (MFE) function is proposed in this paper. MFE function is the difference of adjacent entropies, which is optimized to provide thresholding levels such that all regions have almost equal entropies. To improve the performance of MFE, backtracking search algorithm is used. The numerical and statistical results indicate that MFE-BSA has higher peak signal-to-noise ratio, lower mean square error for all the images at different thresholding levels. Moreover, structural and feature similarity indices for MFE-BSA are closer to unity and the average fitness value obtained using MFE-BSA is minimum (lesser than 0.5). Overall, MFE-BSA shows very good segmentation results in terms of preciseness, robustness, and stability.

关键词： Multilevel thresholding Modified fuzzy entropy backtracking search algorithm

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Short-Term Electricity Price Forecasting via Hybrid backtracking search algorithm and ANFIS Approach

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IEEE ACCESS 2019年 7卷 77674-77691页

作者： Pourdaryaei, Alireza Mokhlis, Hazlie Illias, Hazlee Azil Kaboli, S. Hr Aghay Ahmad, Shameem Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia Kuwait Univ Coll Engn & Petr Elect Engn Dept Safat 13060 Kuwait

In this paper, a hybrid electricity price forecasting method which is composed of two-stage feature selection method and optimized adaptive neuro-fuzzy inference system (ANFIS) technique as a forecasting engine is proposed to accurately forecast electricity price. A multi-objective feature selection approach comprises of multi-objective binary-valued backtracking search algorithm (MOBBSA) as an efficient evolutionary search algorithm and ANFIS method is developed in this paper to extract the most infiuential subsets of input variables with maximum relevancy and minimum redundancy. Through the combination of backtracking search algorithm (BSA) in learning process of ANFIS approach, a hybrid machine learning algorithm has been developed to forecast the electricity price more accurately. Real-world electricity demand and price dataset from Ontario power market;which is reported as among the most volatile market worldwide, has been used as case study to validate the performance of the proposed approach. From the simulation results, it has been seen that the proposed hybrid forecasting method was effective in accurately forecast the Ontario electricity price. In addition, to prove the superiority of the proposed hybrid forecasting method the simulation results obtained using ANN and ANFIS models optimized by other well-known optimization methods have been compared with that of proposed method.

关键词： Adaptive neuro-fuzzy inference system backtracking search algorithm electricity price forecasting feature selection

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A combined fuzzy backtracking search optimization algorithm to localize retinal blood vessels for diabetic retinopathy

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BIOMEDICAL PHYSICS & ENGINEERING EXPRESS 2023年第5期9卷 055025-055025页

作者： Neelapala, Anil Kumar Satapathi, Gnane Swarnadh Borra, Vamsi Mahapatra, Ranjan Kumar Shanbhag, Pavitra AJ Inst Engn & Technol Dept Elect & Commun Engn Mangalore Karnataka India

For diabetic retinopathy (DR) surgery, localization of retinal blood vessels is of paramount importance. Fundus images which are often used for DR diagnosis suffer from poor contrast (between the retinal background and the blood vessels, due to its size) limits the diagnosis. In addition to this, various pathological changes in retinal blood vessels may also be observed for different diseases such as glaucoma and diabetes. To alleviate, in this paper, an automated unsupervised retinal blood vessel segmentation technique, based on backtracking search optimization algorithm (BSA), is proposed. The BSA method is used to optimize the local search of fuzzy c-means clustering (FCM) algorithm to find micro-diameter sized vessels along with coarse vessels. The proposed technique is tested on two publicly available retinal datasets (i.e., STARE and DRIVE) and verified using the dataset collected from various hospitals in Bangalore and Mangalore, India. The results show that the performance of the proposed method is comparable to the conventional techniques in terms of sensitivity, specificity, and accuracy.

关键词： backtracking search algorithm fuzzy clustering retinal blood vessel diabetic retinopathy fundus image segmentation

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Decomposition-based Multi-objective backtracking search algorithm for Personalized Recommendation 38

Decomposition-based Multi-objective Backtracking Search Algo...

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38th Chinese Control Conference (CCC)

作者： Zou, Feng Chen, Debao Zhao, Yongqi HuaiBei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China

ISBN: (纸本)9789881563972

In this paper, a decomposition-based multi-objective backtracking search algorithm (DMOBSA) is proposed for personalized recommendation (PR) problems. In the proposed DMOBSA-PR, an effective individual representation is designed based on the characteristics of PR problems. Moreover, the new crossover and mutation operation in the framework of the BSA algorithm are introduced to update individuals. Furthermore, a decomposition-based multi-objective framework is utilized to balance accuracy and diversity metrics. Finally, the simulation results on Movielens 100K dataset show that DMOBSA-PR is effective compared with other algorithms.

关键词： Personalized Recommendation Multi-objective optimization backtracking search algorithm Decomposition

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