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A modified hybrid whale optimization algorithm for the scheduling problem in multimedia data objects

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第4期32卷

作者： Abdel-Basset, Mohamed El-Shahat, Doaa El-henawy, Ibrahim Zagazig Univ Fac Comp & Informat Zagazig Egypt

The scheduling of the Multimedia Data Objects (MDO) is a difficult and extraordinary issue that faces us in the World Wide Web (WWW) to minimize the response time for clients to finish rapidly their tasks. MDO scheduling problem can be modeled as a Two-Machine Flow Shop Scheduling Problem (T-MFSSP) that minimizes the makespan. Notwithstanding another objective is to decrease the average lateness time of the clients' jobs. This paper proposes a Modified Hybrid whale algorithm (MHWA) to solve a standout among the most imperative applications of flow shop scheduling in the field of MDO. MHWA is hybridized with a local search strategy for solving the scheduling problem of MDO. To cope up with the combinatorial nature of the MDO scheduling, LRV maps the continuous search space into a sequence of jobs. To get better solutions, some operations are applied to the solutions such as swap mutation and reversed block insertion operations. Nawaz-Enscore-Ham (NEH) is added to MHWA to upgrade the performance of the algorithm. The solution of Johnson heuristic is added to the initial population of MHWA. It can be inferred that MHWA gives competitive outcomes appeared differently in relation to Johnson and Earliest Due Date (EDD) algorithms. MHWA can obtain the optimal makespan and can minimize the average lateness of the jobs.

关键词： flow shop scheduling local search makespan multimedia data objects whale algorithm

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Using Response Surface Methodology and providing a modified model using whale algorithm for estimating the compressive strength of columns confined with FRP sheets

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CONSTRUCTION AND BUILDING MATERIALS 2018年 183卷 163-170页

作者： Moodi, Yaser Mousavi, Seyed Roohollah Ghavidel, Alireza Sohrabi, Mohammad Reza Rashki, Mohsen Univ Sistan & Baluchestan Civil Engn Dept Zahedan Iran Univ Sistan & Baluchestan Dept Architecture Engn Zahedan Iran

This study presented a modified model for determining the compressive strength of square and rectangular concrete columns using whale algorithm and a widespread database. In this model, the effective strain coefficient of FRP and cross section coefficient considered as an individual coefficient. Furthermore, the coefficients of the rising strength for specimens with unconfined compressive strength in upper and lower of 35 MPa are differently considered. In addition, Response Surface Methodology used for estimating the compressive strength of the confined specimens by FRP. Finally, results compared with the existing models. The predictions of the modified model and RSM show satisfactory estimations. So that RSM and the modified model have increased R-2 approximately 34% and 26% rather other models, respectively. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Response Surface Methodology Columns FRP confinement Compressive strength Strengthening whale algorithm

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Integrating the whale algorithm with Tabu search for quadratic assignment problem: A new approach for locating hospital departments

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APPLIED SOFT COMPUTING 2018年 73卷 530-546页

作者： Abdel-Basset, Mohamed Manogaran, Gunsekaran El-Shahat, Doaa Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Dept Operat Res Zagazig Egypt Univ Calif Davis Davis CA 95616 USA Griffith Univ Sch Informat & Commun Technol Nathan Campus Brisbane Qld 4111 Australia

The Quadratic Assignment Problem (QAP) is a combinatorial NP-hard optimization problem that is not solvable in a polynomial time. It has a large number of real-world applications in diverse fields (e.g. facility arrangement in a hospital). The whale Optimization algorithm is a new meta-heuristic that achieves a great success in solving the continuous problems. In this paper, we propose a memetic algorithm using the whale optimization algorithm (WA) Integrated with a Tabu Search (WAITS) for solving QAP. In fact, this work employs Tabu Search to improve the quality of solution obtained by WA for QAP problem as a local search algorithm. This is an attempt to improve the convergence speed and local search of WA as its main drawbacks. Due to the combinatorial nature of QAP, the continuous values generated from the standard WA were converted to discrete values by the largest real value mapping. The WAITS algorithm is enhanced by a local search that defines a set of neighborhood solutions to improve the accuracy of the obtained solutions. Fourteen different case studies including 122 test problems are employed for analyzing the performance of the proposed WAITS. The results show that the proposed memetic algorithm finds near-optimal solutions with an acceptable computational time. WAITS is compared to several algorithms in the literature. The results show that the proposed algorithm outperforms similar algorithms in the literature. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Quadratic assignment problem whale algorithm Tabu search Local search Hospital facility layout Combinatorial problems Facilities

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Terrorism Risk Prediction Model Based on Support Vector Machine Optimized by whale algorithm

Terrorism Risk Prediction Model Based on Support Vector Mach...

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作者： Meng Luan Duoyong Sun Zhanfeng Li College of systems engineering National University of Defense Technology

To realize the short-term prediction of regional terrorism risk index, this paper proposes a terrorism risk prediction model based on support vector machine（SVM） optimized by whale optimization algorithm（WOA）. WOA is used to select penalty parameter and kernel function parameter of SVM. The model takes 16 typical quantitative indicators as independent variables. And the Global Terrorist Index（GTI） of the World Economic and Peace Research Institute is used as the output of the prediction model. The simulation experiments are carried out with the data of Asian countries from 2008 to 2018, and the prediction results are compared with those of BP neural network, traditional SVM model and particle swarm optimization SVM model. The experimental results show that the terrorism risk prediction model based on WOA-SVM has higher prediction accuracy, more stable prediction performance, and can effectively realize the prediction of terrorist risk index.

关键词： whale algorithm support vector machine terrorism risk prediction model

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Improvement of whale algorithm and Application

Improvement of Whale Algorithm and Application

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作者： Ming HUANG Xin ZHAN Xu LIANG Software Institute Dalian Jiaotong University

The whale algorithm is a kind of artificial intelligence optimization algorithm. This paper adds nonlinear convergence factor and adaptive inertia weight to the traditional whale algorithm, introduces the concept of quantum revolving door in whale position update, and uses nonlinear convergence factor to whale. The variable variation of the algorithm is modified to adjust the balance between global search and local search, and solves the problem of slow convergence in traditional whale algorithm. The concept of adaptive inertia weight in particle swarm optimization is applied. In the improvement of the local optimization ability of the whale algorithm, the concept of quantum revolving door is introduced in every position update of the whale algorithm, which makes the overall performance of the whole algorithm and the optimal solution have significant performance improvement. The superiority of the improved algorithm is verified by simulation experiments.

关键词： whale algorithm quantum revolving gate adaptive weight nonlinear convergence factor

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L1-norm optimization of problems with arbitrary column rank by whale method and its improved algorithm for outlier detection

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EARTH SCIENCE INFORMATICS 2024年第4期17卷 3599-3609页

作者： Mahboub, Vahid Golestan Univ Fac Engn Dept Surveying Engn Aliabad Katoul Iran

In this contribution L1-norm target function is minimized by whale algorithm for the first time. It is a meta-heuristic optimization method which mimics the social behavior of humpback whales. The whale algorithm is simple and flexible. It takes advantage of a derivation-free mechanism. L1-norm is an efficient tool for outlier detection, nevertheless, its implementation is complex since after formulation of L1-norm minimization for a certain problem, one must solve a linear programming problem by a cumbersome search method while here we only need to set the corresponding L1-norm cost function. During this contribution we also investigate other advantages of the proposed method over traditional methods numerically. Furthermore, the proposed algorithm still has better performance in convergence rate compared to some well-known metaheuristic methods. As the whale algorithm cannot deal with rank deficient problems, it must be improved. Thus the second algorithm of this contribution is an improved whale algorithm which is developed here. Three geodetic applications approve the robustness of the proposed approach.

关键词： whale algorithm Rank deficient model L1-norm minimization Outlier detection Robust estimation

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A NEW HYBRID whale PARTICLE SWARM OPTIMISATION algorithm FOR ROBOT TRAJECTORY PLANNING AND TRACKING CONTROL

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MECHATRONIC SYSTEMS AND CONTROL 2024年第1期52卷 48-57页

作者： Zhang, Huakai Yancheng Teachers Univ Logist Support Grp Yancheng 224000 Peoples R China

In recent years, the use of robotic arms in the automation industry has become increasingly prevalent due to advances in science and technology. This paper aims to enhance the control accuracy of manipulators by designing a hybrid optimisation algorithm. The whale algorithm has been optimised and combined with the particle swarm optimisation (PSO) algorithm to achieve this goal. The trajectory of the manipulator is planned through the utilisation of the hybrid algorithm. The membership function of the fuzzy controller is optimised via the hybrid whale PSO algorithm, and combined with the sliding-mode controller, resulting in the design of a fuzzy sliding-mode controller. The results show that in the convergence curve, the iterations for the hybrid algorithm to reach the global optimal solution is 52, 75, and 183. The rate of convergence is faster. In the joint angle change curve, the total time of optimised B-spline interpolation is 19.95 s. The working efficiency of the manipulator is improved. In the calculation of tracking error, the optimised controller has a tracking error between-0.25 and 0.25, resulting in higher trajectory tracking accuracy. Therefore, the hybrid algorithm has good convergence, and the optimised controller has high accuracy, providing good technical support for trajectory planning and tracking control of robotic arms.

关键词： Robotic arm whale algorithm particle swarm optimisation algorithm fuzzy sliding-mode controller trajectory planning tracking control

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Research on soft measurement model of flow in bends of primary circuits of the nuclear power plant

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FLOW MEASUREMENT AND INSTRUMENTATION 2024年 98卷

作者： He, Bing Xu, Tao Zhu, Yudi Zhao, Chengping Zhou, Xinzhi Sichuan Univ Coll Elect Informat Chengdu 610000 Peoples R China Nucl Power Inst China Natl Key Lab Sci & Technol Reactor Syst Design Tec Chengdu 610213 Peoples R China

When measuring the coolant flow in a nuclear power plant using the elbow flowmeter, the complex fluid-heat coupling environment at the measurement location and other factors will affect the accuracy of the flow measurement, and the uncertainty of the influencing factors on the flow measurement needs to be considered to improve the measurement accuracy. To address this problem, this paper adopts the finite element simulation method to simulate and analyze the flow-heat field of the bend section of the primary circuit of a nuclear power plant and optimizes the Optimal Cross-section selection of the pipeline for flow measurement. Based on the pressure values measured using the traditional method, temperature information is added, and a BP neural network bend pipe flow soft measurement model based on the whale optimization algorithm is established to quantify the effects of temperature and pressure on flow measurement. The experimental results show that compared with the traditional engineering empirical method, the average absolute percentage error measured by the soft measurement method is reduced from 2.57 % to 0.21 %, which realizes the accurate measurement of the coolant flow rate of the elbow pipe.

关键词： Elbow flowmeter Optimal cross-section Flow measurement BP neural network whale algorithm

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Estimation of road friction coefficient based on WOA-BP neural network

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Xu, Yiren Xu, Zhongming Ye, Jia Zhang, Zhifei Chongqing Univ Coll Mech & Vehicle Engn Chongqing Peoples R China Chongqing Univ Coll Mech & Vehicle Engn 174 Shazhengjie Chongqing 400030 Peoples R China

The road friction coefficient is often estimated based on vehicle dynamics models, but the ideal mathematical model is difficult to describe the actual vehicle operation, which in turn leads to deviations between the estimated friction coefficient and the actual situation. In order to take full account of the actual vehicle driving conditions, back propagation (BP) neural network model is often applied to assess the friction coefficient. To further improve the generalization ability of the network model, this paper proposes a method to predict the friction coefficient using the whale optimized algorithm-BP (WOA-BP) neural network model. The whale algorithm improves the weights and thresholds of the BP neural network model to improve generalization ability and convergence speed. First, the vehicle dynamics analysis is utilized to determine the vehicle dynamics parameters as a function of the friction coefficient. Then, the WOA-BP is compared against the BP under various realistic operating scenarios. The results demonstrate that the WOA-BP estimator is accurate and efficient in estimating the friction coefficient, and the proposed method reduces the mean absolute error by 51.9% and the root mean squared error by 49.1% compared to the BP.

关键词： Road friction coefficient vehicle dynamics neural network generalization ability whale algorithm

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Optimization of process parameters for minimizing the temperature field of high-speed milling of titanium alloy thin-walled parts

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第4期19卷 2415-2430页

作者： Liu, Jianping Liu, Changfu Tong, Hao Wang, Daohai Liaoning Petrochem Univ Sch Mech Engn Fushun 113001 Peoples R China

The high milling temperature in the high-speed milling process restricts the production efficiency and processing quality of titanium alloy thin-walled parts. Optimizing the processing parameters can control the temperature within a reasonable range, which can improve its production efficiency and processing quality. Therefore, it is imperative to conduct research on milling temperature and multi-objective parameter optimization during the milling process. In this paper, the temperature field model of the workpiece was established using the moving heat source method. The finite element method was used to simulate the temperature field of the workpiece during the milling process. The milling temperature was measured by an infrared thermal imager, and a milling temperature prediction model was set up based on the experimental data. Considering the minimum milling temperature and the maximum material removal rate, the whale optimization algorithm was applied to optimize the processing parameters. The results showed that the maximum error of simulation and experimental results is less than 15%. The milling temperature decreases with the increase of spindle speed and increases with the increase of cutting depth and feed speed. The Pareto solution set obtained by the optimization method can provide a reference for the selection of subsequent milling parameters.

关键词： Titanium alloy thin-walled parts Milling temperature whale algorithm Parameter optimization

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