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An enhanced shuffled shepherd optimization algorithm for optimal design of large-scale space structures

ENGINEERING WITH COMPUTERS 2022年第SUPPL 2期38卷 1505-1526页

作者： Kaveh, Ali Zaerreza, Ataollah Hosseini, Seyed Milad Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran

shuffled shepherd optimization algorithm (SSAO) is a swarm intelligence-based optimizer inspired by the herding behavior of shepherds in nature. SSOA may suffer from some shortcomings, including being trapped in a local optimum and starting from a random population without prior knowledge. This study aims to enhance the performance of the SSOA by incorporating two efficient devices. The first device is utilized from the Opposition-Based Learning (OBL) approach to improve the initialization phase of the algorithm. The second device is incorporated a solution generator in the cyclic body of the SSOA based on the statistical results of the solutions. This feature is the so-called statistically regenerated stepsize. The proposed devices provide a good balance between exploration and exploitation capability of the algorithm and reduce the probability of getting tapped in a local optimum. The viability of the proposed Enhanced shuffled shepherd optimization algorithm (ESSOA) is demonstrated through three large-scale design examples. ESSOA is compared to the standard SSOA and some other existing metaheuristic algorithms. The optimization results reveal the competence and robustness of the ESSOA for optimal design of the large-scale space structures.

关键词： Structural optimization Large-scale space structures shuffled shepherd optimization algorithm Opposition-based learning Metaheuristic algorithms

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A set theoretical shuffled shepherd optimization algorithm for optimal design of cantilever retaining wall structures

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ENGINEERING WITH COMPUTERS 2021年第4期37卷 3265-3282页

作者： Kaveh, Ali Biabani Hamedani, Kiarash Zaerreza, Ataollah Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran

In this paper, a recently developed meta-heuristic algorithm, shuffled shepherd optimization algorithm (SSOA), is employed for optimal design of reinforced concrete cantilever retaining wall structures under static and seismic loading conditions. The concepts of set theory are employed to express the SSOA in a set theoretical term. The Rankine and Coulomb theories are utilized in order to estimate the lateral earth pressures under the static loading condition, whereas the Mononobe-Okabe method is employed for the seismic one. optimization aims to minimize the cost of cantilever retaining wall while satisfying some constraints on stability and strength limits. The design is based on the requirements of ACI 318-05. In order to investigate the efficiency of the SSOA, one benchmark cantilever retaining wall problem is considered from the literature. Comparing the optimization results obtained by the SSOA with those of other meta-heuristics shows the efficient performance of the SSOA in both aspects of accuracy and convergence rate.

关键词： shuffled shepherd optimization algorithm Set theory Cantilever retaining wall structures Structural optimization Meta-heuristic algorithms

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Enhanced versions of the shuffled shepherd optimization algorithm for the optimal design of skeletal structures

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STRUCTURES 2021年 29卷 1463-1495页

作者： Kaveh, A. Kamalinejad, M. Hamedani, K. Biabani Iran Univ Sci & Technol Dept Civil Engn POB 16846-13114 Tehran Iran

This paper consists of two main parts. The first part is devoted to developing a parameter-free variant of the shuffled shepherd optimization algorithm (SSOA), named as a parameter-free shuffled shepherd optimization algorithm (PF-SSOA). Next, a set-theoretical multi-phase framework is proposed for the population-based metaheuristic algorithms. The proposed framework's main idea is based on dividing the population of candidate solutions into some number of well-arranged sub-populations. The number of sub-populations gradually changes during the optimization process. The framework is applied to the PF-SSOA, leading to a set-theoretical multiphase variant of PF-SSOA named STMP-SSOA. The STMP-SSOA stands for a set-theoretical multi-phase shuffled shepherd optimization algorithm. Some skeletal structures are optimized to verify the validity and efficiency of the proposed algorithms. The optimization problems include two truss size optimization problems with multiple frequency constraints and two planar frame structures with strength and displacement constraints. The results indicate that the developed algorithms perform better than the standard version of the SSOA, especially in terms of robustness and convergence characteristics.

关键词： Set theory Population-based metaheuristics shuffled shepherd optimization algorithm Structural optimization Skeletal structures

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Grey Wolf shuffled shepherd optimization algorithm-Based Hybrid Deep Learning Classifier for Big Data Classification

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-20页

作者： Banchhor, Chitrakant Srinivasu, N. MIT Coll Engn Sch Comp Engn & Technol Pune Maharashtra India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Andhra Pradesh India

In recent days, big data is a vital role in information knowledge analysis, predicting, and manipulating process. Moreover, big data is well-known for organized extraction and analysis of large or difficult databases. Furthermore, it is widely useful in data management as compared with the conventional data processing approach. The development in big data is highly increasing gradually, such that traditional software tools faced various issues during big data handling. However, data imbalance in huge databases is a main limitation in the research area. In this paper, the Grey wolf shuffled shepherd optimization algorithm (GWSSOA)-based Deep Recurrent Neural Network (DRNN) algorithm is devised to classify the big data. In this technique, for classifying the big data a hybrid classifier, termed as Holoentropy driven Correlative Naive Bayes classifier (HCNB) and DRNN classifier is introduced. In addition, the developed hybrid classification model utilizes the Map-Reduce structure to solve big data issues. Here, the training process of the DRNN classifier is employed using GWSSOA. However, the developed GWSSOA is devised by integrating shuffled shepherd optimization algorithm (SSOA) and Grey Wolf Optimizer (GWO) algorithms. The developed GWSSOA-based DRNN model outperforms other big data classification techniques with regards to accuracy, specificity, and sensitivity of 0.966, 0.964, 0.870, and 209837ms.

关键词： Big Data Classification Deep Recurrent Neural Network Grey Wolf Optimizer MapReduce Framework shuffled shepherd optimization algorithm

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shuffled shepherd optimization method: a new Meta-heuristic algorithm

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ENGINEERING COMPUTATIONS 2020年第7期37卷 2357-2389页

作者： Kaveh, Ali Zaerreza, Ataollah Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

Purpose This paper aims to present a new multi-community meta-heuristic optimization algorithm, which is called shuffled shepherd optimization algorithm (SSOA). In this algorithm. Design/methodology/approach The agents are first separated into multi-communities and the optimization process is then performed mimicking the behavior of a shepherd in nature operating on each community. Findings A new multi-community meta-heuristic optimization algorithm called a shuffled shepherd optimization algorithm is developed in this paper and applied to some attractive examples. Originality/value A new metaheuristic is presented and tested with some classic benchmark problems and some attractive structures are optimized.

关键词： shuffled shepherd optimization algorithm Sheep shepherd Meta-heuristic optimization Mathematical bench mark Truss structures optimization

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Wrapper-Based Feature Selection and optimization-Enabled Hybrid Deep Learning Framework for Stock Market Prediction

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第1期23卷 475-500页

作者： Patil, Pankaj Rambhau Parasar, Deepa Charhate, Shrikant Amity Univ Maharashtra Amity Sch Engn & Technol Dept Comp Sci & Engn Mumbai India Amity Univ Maharashtra Dept Comp Sci & Engn Mumbai India Amity Univ Maharashtra Dept Civil Engn Mumbai India

Stock market is a significant element of economic market. Accurate forecasting of stock market is very helpful to shareholders because future prediction of a stock value will elevate the profits of investors. The data acquired from the stock market is a time-series data, in which the values of the stock prices are inherently varied with respect to time. Due to its complexity nature and nonlinearity characteristics, the prediction of stock market becomes very difficult and still it remains a challenging task. In order to cope up with such limitation, this research proposes an effective strategy called Deep Recurrent Rider LSTM to provide an accurate detection of stock market values. The accurate forecasting of stock market is carried out with two classifiers, namely Rider Deep Long Short-Term Memory (Rider Deep LSTM) and Deep Recurrent Neural Network (Deep RNN). The Rider Deep LSTM is derived by the integration of Rider concept with Deep LSTM, whereas the Deep RNN is trained using the proposed shuffled Crow Search optimization (SCSO). Moreover, the SCSO is derived by the integration of shuffled shepherd optimization (SSO) algorithm and Crow Search algorithm (CSA). Finally, the predicted output is determined based on the error condition. Furthermore, the proposed Deep Recurrent Rider LSTM achieved the MSE and RMSE of 0.018 and 0.132 that shows higher performance with better accuracy. The stock market prediction using the proposed classification model is accurate and improves the effectiveness of the classification.

关键词： Stock market prediction deep RNN rider deep LSTM crow search algorithm shuffled shepherd optimization algorithm

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Size/Layout optimization of Truss Structures Using shuffled shepherd optimization Method

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2020年第2期64卷 408-421页

作者： Kaveh, Ali Zaerreza, Ataollah Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran

The main purpose of this paper is to investigate the ability of the recently developed multi-community meta-heuristic optimization algorithm, shuffled shepherd optimization algorithm (SSOA), in layout optimization of truss structures. The SSOA is inspired by mimicking the behavior of shepherd in nature. In this algorithm, agents are first divided into communities which are called herd and then optimization process, inspired by the shepherd's behavior in nature, is operated on each community. The new position of agents is obtained using elitism technique. Then communities are merged for sharing the information. The results of SSOA in layout optimization show that SSOA is competitive with other considered meta-heuristic algorithms.

关键词： meta-heuristic algorithms shuffled shepherd optimization algorithm size/layout optimization truss structures

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A new framework for reliability-based design optimization using metaheuristic algorithms

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STRUCTURES 2022年 38卷 1210-1225页

作者： Kaveh, Ali Zaerreza, Ataollah Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran

This paper presents a new framework for reliability-based design optimization (RBDO) using metaheuristic algorithms based on decoupled methods. The sequential optimization and reliability assessment (SORA) is utilized as a decoupled method in this study. In the present framework, unlike the previous RBDO based on decoupled methods, a metaheuristic is employed on both reliability assessment and the optimization parts. Here, first a new reliability assessment method based on the metaheuristics is introduced. Then, a new termination condition for the metaheuristic inspired by the termination condition of the gradient-based method is presented. In order to investigate the efficiency of the proposed framework, an enhanced shuffled shepherd optimization algorithm (ESSOA) is used as an optimization approach. The efficiency of the proposed framework is evaluated by four well-known RBDO benchmarks investigated in the previous studies using the gradient-based method. Also, the two new RBDO problems are introduced in this study. The results show that the proposed framework can have better performance than the gradient-based method in the RBDO and can easily be utilized in a wide range of RBDO problems.

关键词： Reliability-based design optimization Metaheuristic algorithms Reliability assessment method shuffled shepherd optimization algorithm

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shuffled shepherd optimization Method Simplified for Reducing the Parameter Dependency

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2021年第3期45卷 1397-1411页

作者： Kaveh, Ali Zaerreza, Ataollah Hosseini, Seyed Milad Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran

shuffled shepherd optimization algorithm (SSOA) is a new multi-community algorithm inspired by shepherd behavior. SSOA is an algorithm dependent on its parameters and needs to find the right parameters for each problem to work best. In this paper, SSOA is modified to make it less dependent on parameter tuning. The new version is called parameter-reduced SSOA (PRSSOA) and requires less parameters to be tuned. The results of the SSOA and those of the PRSSOA are compared for some structural optimization design problems indicating the reliability of proposed version.

关键词： shuffled shepherd optimization algorithm Reduced parameter SSOA Metaheuristic Structural optimal design

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Hybrid optimization-Based Structural Design of Deep Q Network With Feature Selection algorithm for Medical Data Classification

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷 1-20页

作者： Patra, Radhanath Khuntia, Bonomali Panda, Dhruba Charan Berhampur Univ Dept Elect Sci Berhampur Odisha India Berhampur Univ Dept Comp Sci Berhampur Odisha India

In the area of medical informatics, the medical data classification is considered a complicated job. However, accurate classification of medical data is a complex task. Therefore, a robust and effective hybrid optimization-based deep learning method for classifying the medical data is developed in this research. The input data is pre-processed using data normalization method. Then, the features are selected using the proposed Henry Sea Lion optimization (HSLnO), which is the combination of Henry Gas Solubility optimization (HGSO) and Sea Lion optimization (SLnO). The classification process is achieved using an optimized Deep Q Network (DQN). The DQN is optimized using the proposed shuffled shepherd Whale optimization algorithm (SSWOA). The proposed SSWOA is developed by the integration of the shuffled shepherd optimization algorithm (SSOA) and Whale optimization algorithm (WOA). The developed technique achieves better performance of testing accuracy, sensitivity, and specificity with values of 95.413%, 95.645%, and 95.364%, respectively.

关键词： Henry Gas Solubility optimization Medical Data Classification Sea Lion optimization shuffled shepherd optimization algorithm Whale optimization algorithm

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