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Grey Wolf Optimizer and whale optimization algorithm for Stochastic Inventory Management of Reusable Products in a Two-Level Supply Chain

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IEEE ACCESS 2023年 11卷 40278-40297页

作者： Sadeghi, Amir Hossein Bani, Erfan Amani Fallahi, Ali Handfield, Robert North Carolina State Univ Dept Ind & Syst Engn Raleigh NC 27606 USA Sharif Univ Technol Dept Ind Engn Tehran *** Iran North Carolina State Univ Dept Business Management Raleigh NC 27607 USA

Product reuse and recovery is an efficient tool that helps companies to simultaneously address economic and environmental dimensions of sustainability. This paper presents a novel problem for stock management of reusable products in a single-vendor, multi-product, multi-retailer network. Several constraints, such as the maximum budget, storage capacity, number of orders, etc., are considered in their stochastic form to establish a more realistic problem. The presented problem is formulated using a nonlinear programming mathematical model. The chance-constrained approach is suggested to deal with the constraints' uncertainty. Regarding the nonlinearity of the model, grey wolf optimizer (GWO) and whale optimization algorithm (WOA) as two novel metaheuristics are presented as solution approaches, and the sequential quadratic programming (SQP) exact algorithm validates their performance. The parameters of algorithms are calibrated using the Taguchi method for the design of experiments. Extensive analysis is established by solving several numerical results in different sizes and utilizing several comparison measures. Also, the results are compared statistically using proper parametric and non-parametric tests. The analysis of the results shows a significant difference between the algorithms, and GWO has a better performance for solving the presented problem. In addition, both algorithms perform well in searching the solution space, where the GWO and WOA differences with the optimal solution of the SQP algorithm are negligible.

关键词： Supply chains Inventory management Uncertainty Stochastic processes Metaheuristics whale optimization algorithms Programming Reuse and recovery chance-constrained programming grey wolf optimizer whale optimization algorithm Taguchi method

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A Hybrid Model Based on Complete Ensemble Empirical Mode Decomposition With Adaptive Noise, GRU Network and whale optimization algorithm for Wind Power Prediction

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IEEE ACCESS 2023年 11卷 62840-62854页

作者： Sheng, Andi Xie, Lewei Zhou, Yixiang Wang, Zhen Liu, Yuechao North China Elect Power Univ Comp Dept Baoding 071000 Hebei Peoples R China Huazhong Agr Univ Coll Sci Wuhan 430000 Hubei Peoples R China Anhui Med Univ Sch Hlth Adm Hefei 230012 Anhui Peoples R China Zhejiang Yuexiu Univ Hotel Management Dept Shaoxing 312000 Zhejiang Peoples R China North China Elect Power Univ Dept Math & Phys Baoding 071003 Hebei Peoples R China

To ensure the safe and stable operation of power systems, accurate prediction of wind power generation is particularly important. However, due to the randomness, fluctuation, and intermittency of wind energy, as well as the challenges in determining the hyperparameters of the gated recurrent unit (GRU) network, this paper proposes an innovative "decomposition-optimization-reconstruction" prediction method. To enhance the accuracy of wind power prediction, a sophisticated wind power hybrid prediction model based on the GRU network, combined with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the whale optimization algorithm (WOA) has been proposed in this paper. Among the efficient decomposition algorithms are successive variational mode decomposition (SVMD) and CEEMDAN. Taking into account adaptability issues, in the data decomposition stage, the CEEMDAN method is chosen to decompose the wind power time series into different sub-sequence components. This approach allows for a better representation of the fluctuation characteristics of each sub-sequence at various time scales. In the prediction optimization stage, the GRU prediction method is used to predict each sub-sequence component and the WOA algorithm is combined to optimize the hyperparameters of each GRU. This approach demonstrates significant advantages in improving wind power prediction accuracy, enhancing generalization capability, and strengthening adaptability. In the prediction reconstruction stage, the prediction results of each sub-sequence component are superimposed to obtain the final wind power prediction value. Finally, the model is verified using actual wind power data from a power plant in the northwest, and the simulation results show that the wind power prediction model based on CEEMDAN-WOA-GRU has significant advantages in prediction accuracy and stability compared to other models. This model provides strong support for optimizing wind energy grid integration

关键词： Decomposition-optimization-reconstruction gated recurrent unit complete ensemble empirical mode decomposition with adaptive noise whale optimization algorithm wind power prediction

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A novel improved whale optimization algorithm for optimization problems with multi-strategy and hybrid algorithm

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年 205卷 794-817页

作者： Deng, Huaijun Liu, Linna Fang, Jianyin Qu, Boyang Huang, Quanzhen Zhongyuan Univ Technol Sch Elect & Informat Zhengzhou 451191 Henan Peoples R China Henan Univ Engn Sch Elect Informat Engn Zhengzhou 450007 Henan Peoples R China

whale optimization algorithm (WOA), as an advanced optimization algorithm with simple structure, has been favored by various fields. However, there are some disadvantages of WOA, such as slow convergence speed, low precision and falling into local optimal value easily. In this paper, a novel improved whale optimization algorithm (IWOA) with multi-strategy and hybrid algorithm is proposed to overcome above shortcomings. Firstly, IWOA initializes the population by chaotic mapping to avoid the initial population distribution of WOA deviating from the optimal value. Secondly, IWOA combines the pheromone of the black widow algorithm and the opposition-based learning strategy to modify the population, which improves the convergence speed and the global performance of WOA respectively. Finally, the adaptive coefficients and the new update modes replace the original update modes, which makes the structure of WOA simpler and more accurate. In addition, the convergence of IWOA is also proved in this paper. On the one hand, to demonstrate the effectiveness of IWOA, 23 benchmark functions are used to test various performance of the algorithm. On the other hand, in order to prove the superiority of IWOA, the experimental results are compared and analyzed with other optimization algorithms. Simulation results show that IWOA proposed in this paper owns excellent performance in convergence speed, stability, accuracy and global performance, compared with other algorithms.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm Chaos map Adaptive weights Black widow algorithm Opposition -based learning strategy

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Energy cost optimization in distribution network considering hybrid electric vehicle and photovoltaic using modified whale optimization algorithm

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JOURNAL OF SUPERCOMPUTING 2023年第13期79卷 14427-14456页

作者： Zadehbagheri, Mahmoud Abbasi, Ali Reza Islamic Azad Univ Dept Elect Engn Yasuj Branch Yasuj Iran Univ Fasa Dept Elect Engn Fasa Fars Iran

The economic emission dispatch problem is one of the most crucial problems for the future smart power network. The integration of renewable energy resources and the use of electric vehicles (EVs) are various techniques that have been suggested to deal with the increase in greenhouse gas emissions. However, to carefully examine various scenarios of integration of the above elements is important to reduce emission rates while considering economic constraints. In this research, the effectiveness of the use of photovoltaics (PVs) and EVs' discharging/charging behavior is investigated from various perspectives in a hybrid economic emission dispatch. Here, a multi-objective model includes the optimization of two objectives: cost and pollution is suggested. Based on a penalty factor, we convert the multi-objective function into a single objective. For solving this non-convex, nonlinear and on-smooth problem, a modified whale optimization algorithm (WOA) is exerted to gain the optimum results. The idea of two sub-modifications is derived from Levy flight algorithm and the mutation operator of the genetic algorithm. The verification of the suggested model is performed by the IEEE 30-bus system with 6-units. The simulated results illustrate that the incorporation of PV along with EVs in economic dispatch problem can improve the pollution emission, the economic costs, and convergence rate, effectively.

关键词： Economic dispatch Electricity distribution network Hybrid electric vehicle Cost reduction Pollution reduction whale optimization algorithm

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Pipeline damage identification based on an optimized back-propagation neural network improved by whale optimization algorithm

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APPLIED INTELLIGENCE 2023年第10期53卷 12937-12954页

作者： Wu, Lei Mei, Jiangtao Zhao, Shuo China Univ Petr Sch Petr Engn Qingdao 266580 Peoples R China China Univ Petr Natl Engn Res Ctr Offshore Geophys & Explorat Equ Qingdao 266580 Peoples R China Nanyang Technol Univ Maritime Inst NTU Sch Civil & Environm Engn Singapore 639798 Singapore China Univ Petr Coll Mech & Elect Engn Qingdao 266580 Peoples R China

With the advantages of high economy and large transportation capacity, pipeline transportation is commonly used in industrial production. Pipeline damage induced by various factors will result in changes of physical properties, further leading to changes of dynamic parameters such as natural frequency and vibration mode. Recently, as a new type of tool, artificial intelligence is widely used for pipeline damage identification. In this study, to promote the accuracy of pipeline damage identification, a novel method that employs the artificial neural network (ANN) and swarm intelligence algorithm is proposed. In detail, based on the original whale optimization algorithm (WOA), an improved WOA (IWOA) is presented in which an adaptive coefficient strategy and a stochastic optimal substitution strategy are introduced. Then, the IWOA and back-propagation neural network (BPNN) are hybridized into IWOA-BPNN. Subsequently, a damage location detector and a damage degree detector are established based on the proposed IWOA-BPNN. By taking a pipeline fixed at both ends and its curvature and displacement modes, the proposed damage identification method is verified to confirm its effectiveness and accuracy in different damage states. Experimental results demonstrate that the comprehensive performance of IWOA-BPNN is better than other compared models. The relative error of the predicted results obtained by IWOA-BPNN is less than 2.2% when evaluating the damage location and degree for 12 randomly selected test samples, indicating the superiority of the proposed method. The proposed method has broad application prospects in modern industries.

关键词： Pipeline damage identification Back-propagation neural network whale optimization algorithm Damage location and degree

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New network interface selection based on MADM and multi-objective whale optimization algorithm in heterogeneous wireless networks

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JOURNAL OF SUPERCOMPUTING 2023年第4期79卷 3580-3615页

作者： Mefgouda, Brahim Idoudi, Hanen Univ Manouba Natl Sch Comp Sci Manouba Tunisia

Multi-attribute decision-making (MADM) approaches are commonly used to model and solve the network interface selection problem in heterogeneous wireless networks. Despite their importance and advantages to deal with this issue, they suffer from the rank reversal problem (RRP) and the selection of the highest-ranking score network without considering the user's/service requirements. In this paper, a novel method is introduced to solve the MADM limitations. Besides, the weights assignment technique is modelled as a multi-objective problem. Then, an extended version of the whale optimization algorithm is applied to obtain the suitable weights of the decision criteria. The obtained results showed that applying the developed technique with MADM approaches reduces (sometimes avoids completely) the RRP by an average up to 94%, compared to Analytical Hierarchy Process. It also allows meeting the user's/service requirements by optimizing data rate and packet loss, for streaming services, by an average up to 14.3 (kbps) and 20 x 10(6) (ms), respectively.

关键词： Network interface selection Heterogeneous wireless networks Multi-attribute decision making whale optimization algorithm Rank reversal problem Quality of service

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Theoretical rotational stiffness of the flexible base connection based on parametric study via the whale optimization algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2023年第1期88卷 43-52页

作者： Nawar, Mahmoud T. Matar, Ehab B. Maaly, Hassan M. Alaaser, Ahmed G. Hamdy, Osman Prince Sultan Univ Coll Engn Engn Management Dept Riyadh 11586 Saudi Arabia Zagazig Univ Dept Struct Engn Zagazig Egypt Zagazig Higher Inst Engn & Technol Dept Civil Engn Zagazig Egypt

This paper handles the results of an extensive parametric study on the rotational stiffness of the flexible base connection using ABAQUS program. The results of the parametric study show the relation between the applied moment and the relative rotation for 96 different base connections. The configurations of the studied connections considered different numbers, diameters, and spacing of the anchor bolts along with different thicknesses of the base plate to investigate the effect of these parameters on the rotational stiffness behavior. The results of the previous parametric research used through the whale optimization algorithm (WOA) to detect different equation formulation of the moment-rotation (M-0r) equation to detect optimum equation simulates the general nonlinear rotational behavior of the flexible base connection considering all variables used in the parametric study. WOA is a relatively new promising algorithm, which is used in different types of optimization problems. For more verification, the classical genetic algorithm (GA) is used to make a comparison with WOA results. The results show that WOA is capable of getting an optimum equation of the M-0r relation, which can be used to simulate the actual rotational stiffness of the flexible base connections. The rotational stiffness at H/150 can be calculated using WOA (1) method and be used as a design aid for engineering design.

关键词： flexible base connection flexural stiffness genetic algorithm rotational stiffness whale optimization algorithm

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A modified whale optimization algorithm with multi-strategy mechanism for global optimization problems

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NEURAL COMPUTING & APPLICATIONS 2023年 1-14页

作者： Li, Mingyuan Yu, Xiaobing Fu, Bingbing Wang, Xuming Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China

whale optimization algorithm (WOA) is an outstanding nature-inspired algorithm widely used to solve many complex engineering optimization problems. However, WOA has a poor balance in exploration and exploitation, which converges to local optimum easily. This article proposes a Modified whale optimization algorithm (MWOA) with multi-strategy mechanism, which introduces the elite reverse learning strategy, nonlinear convergence factor, DE/rand/1 mutation strategy and Levy flight disturbance strategy. MWOA can improve the convergent ability and maintain the balance of exploitation and exploration to avoid local optimum. Compared with WOA, PSO, MFO, SOA, SCA and other four WOA variants on the CEC2017 benchmark suite, MWOA has strong competitiveness and can better improve the efficiency of WOA according to the experimental results and analysis.

关键词： whale optimization algorithm Multi-strategy Elite reverse learning strategy Nonlinear convergence factor

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Hybrid whale optimization algorithm with simulated annealing for load frequency controller design of hybrid power system

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SOFT COMPUTING 2023年 1-24页

作者： Nayak, Pratap Chandra Mishra, Sonalika Prusty, Ramesh Chandra Panda, Sidhartha VSSUT Dept Elect Engn Burla 768018 Odisha India

This research paper presents an innovative approach by hybridizing the whale optimization algorithm-WOA and the Simulated Annealing-SA, named as h-WOA-SA algorithm. The preeminence of the projected hybridized algorithm is evaluated over the basic form, modified form and some popular algorithms in respect of computation time and eminence in the tuning of fitness function by considering various standard test functions. Further, the hybrid algorithm is utilized for the scaling of interval type-2 fuzzy fractional order PID-IT2F-FO-PID controller engaged in load frequency control-LFC of hybrid power system-HPS. It presents a strategy to manage the critical challenges encountered due to the penetration of solar & wind power for their operational dynamics. The sturdiness of the hybrid technique based controller is examined in the HPS. The unrivalled superiority of the proposed approach has been validated by contrasting some recent research outcomes and with a real-time validation which justifies the effectiveness in improved performance as compared to the traditional approaches.

关键词： Hybrid power system whale optimization algorithm Simulation annealing Fractional order Type 2 fuzzy logic controller Renewable energy sources Frequency regulation

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Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第9期20卷 16304-16329页

作者： Du, Juan Hou, Jie Wang, Heyang Chen, Zhi Taiyuan Univ Sci & Technol Sch Mech Engn Taiyuan 030024 Peoples R China

To address the issues of unstable, non-uniform and inefficient motion trajectories in traditional manipulator systems, this paper proposes an improved whale optimization algorithm for time-optimal trajectory planning. First, an inertia weight factor is introduced into the surrounding prey and bubble-net attack formulas of the whale optimization algorithm. The value is controlled using reinforcement learning techniques to enhance the global search capability of the algorithm. Additionally, the variable neighborhood search algorithm is incorporated to improve the local optimization capability. The proposed whale optimization algorithm is compared with several commonly used optimization algorithms, demonstrating its superior performance. Finally, the proposed whale optimization algorithm is employed for trajectory planning and is shown to be able to produce smooth and continuous manipulation trajectories and achieve higher work efficiency.

关键词： trajectory planning time-optimal whale optimization algorithm reinforcement learning VNS algorithm

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