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An Improved Lexicographical whale optimization algorithm for the Type-II Assembly Line Balancing Problem Considering Preventive Maintenance Scenarios

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IEEE ACCESS 2020年 8卷 30421-30435页

作者： Meng, Kai Tang, Qiuhua Zhang, Zikai Qian, Xinbo Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China

In the traditional assembly line balancing, all the workstations are assumed available and hence the unavailability of any workstation brings about the stoppage of the whole line and the waste of the production capacity in the rest workstations. Considering the planning characteristic of preventive maintenance, this paper proposes a novel methodology of integrating the preventive maintenance scenarios into assembly line balancing problems to bypass the unavailable workstation. A lexicographic model is formulated to generate multiple task assignment plans that ensure primarily the high productivity under regular operation scenario and guarantee secondarily the production continuity under preventive maintenance scenarios. And, an improved whale optimization algorithm (IWOA) with three modifications is proposed to solve this problem. Specifically, a combined crossover operator enhances better combination in exploration;three best search agents promote the exploitation;partial regeneration avoids being trapped in local optima. More than five thousand experiments demonstrate that the joint of three modifications endows the IWOA significant superiority over six variants and other six well-known algorithms. Moreover, integrating preventive maintenance scenarios into the assembly line balancing problem increases the production efficiency by 1% at the cost of small production adjustment.

关键词： Assembly line balancing lexicographical optimization preventive maintenance whale optimization algorithm

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Improved whale optimization algorithm Based on Nonlinear Adaptive Weight and Golden Sine Operator

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IEEE ACCESS 2020年 8卷 77013-77048页

作者： Zhang, J. Wang, J. S. Univ Sci & Technol Liaoning Sch Comp & Software Engn Anshan 114000 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114000 Peoples R China

whale optimization algorithm (WOA) is a swarm intelligence-based algorithm that simulates whale population predation in the sea. Aiming at the shortcomings of WOA such as low precision and slow convergence speed, an improved whale optimization algorithm based on nonlinear adaptive weight and golden sine operator (NGS-WOA) was proposed. NGS-WOA first introduced a non-linear adaptive weigh so that search agents can adaptively explore the search space, and balance the development and exploration stages. Secondly, the improved golden sine operator is incorporated into the WOA. Due to the special relationship between the sine function and the unit circle, traversing the sine function is equivalent to scanning the unit circle. The search agent performs an efficient search with a sine route so as to improve the convergence speed and global exploration capability of the algorithm. At the same time, the addition of the golden section coefficient allows search agents to exploit with a fixed shrink step. The search agent can develop to areas with excellent results, which improves the optimization accuracy and local exploitation ability of the algorithm. In the simulation experiments, the gold sine algorithm (GoldSA), whale optimization algorithm (WOA), particle swarm optimization (PSO) algorithm, firefly algorithm (FA), fireworks algorithm (FWA), sine cosine algorithm (SCA) and NGS-WOA were selected for comparison experiments. Then, the effectiveness of the proposed improved strategies is verified. Finally, the improved WOA is applied to high-dimensional optimization and engineering optimization problems. The experimental results show that the improved strategy can effectively improve the performance of the algorithm, so that NGS-WOA has the advantages of high global convergence and avoiding falling into local optimal values.

关键词： whale optimization algorithm nonlinear adaptive weight golden sine operator optimization

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Elman Neural Network Soft-Sensor Model of Conversion Velocity in Polymerization Process Optimized by Chaos whale optimization algorithm

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IEEE ACCESS 2017年 5卷 13062-13076页

作者： Sun, W. Z. Wang, J. S. Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan 114051 Peoples R China

According to the requirement of real-time monitoring of the conversion rate of vinyl chloride in the production process of polyvinyl chloride polymerization and the nonlinearity of the industrial data, the Elman neural network with strong nonlinear performance is chosen to build the soft-sensor model. However, because of the early stage of the Elman neural network to train the connection weights between the layers, the training effect is difficult to guarante with the connection weights. So the whale optimization algorithm (WOA) is adopted to optimize the Elman neural network, to avoid it falling into the local optimum. At the same time, to solve the problem that the position of the search agent is randomly distributed in the initialization process of the WOA algorithm, and to introduce the idea of chaos, a chaos WOA (CWOA) based on the idea of chaos is proposed to improve the diversity of all search agents and egocentricity of agent search by utilizing the chaotic features. At the end of this paper, considering that the input vector dimension is too large, the neural network topology is very large, which will lead to the complexity of the training process. Therefore, the locally linear embedding method is introduced to reduce the dimension of high-dimensional input vectors. The simulation results show that the chaotic whale algorithm can significantly improve the prediction accuracy of economic and technical indexes of PVC polymerization process, which especially has a significant improvement in the prediction effect in the early stage and meets the requirements of real-time control of the production process of the polymerization reactor.

关键词： PVC polymerization process Elman neural network Soft sensor whale optimization algorithm Locally linear embedding method

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A parallel numerical method for solving optimal control problems based on whale optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2018年 151卷 114-123页

作者： Mehne, Hamed Hashemi Mirjalili, Seyedali Aerosp Res Inst Tehran 14665834 Iran Griffith Univ Sch Informat & Commun Technol Nathan Campus Brisbane Qld 4111 Australia

Development of a numerical algorithm for solving optimal control problems is reported in this article. The method is a combination of multi-staging of the original problem to a finite dimensional optimization problem and the recently proposed whale optimization algorithm (WOA). The method is proposed to reduce the required number of iterations. The parallel implementation is also proposed and discussed. Numerical examples are given to check the validity and accuracy of the proposed method. Results show that method converges to the exact solution with accuracy comparable to other numerical methods. Note that the source codes are publicly available at http://***/SourceCodes/***. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Optimal control whale optimization algorithm Numerical solution Parallel computing

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Deep neural network and whale optimization algorithm to assess flyrock induced by blasting

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ENGINEERING WITH COMPUTERS 2021年第1期37卷 173-186页

作者： Guo, Hongquan Zhou, Jian Koopialipoor, Mohammadreza Jahed Armaghani, Danial Tahir, M. M. Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Teknol Malaysia UTM Construct Res Ctr Inst Smart Infrastruct & Innovat Construct ISIIC Sch Civil EngnFac Engn Johor Baharu 81310 Johor Malaysia

A wide variety of artificial intelligence methods have been utilized in the prediction of flyrock induced by blasting. This study focuses on developing a model based on deep neural network (DNN) which is an advanced version of artificial neural network (ANN) for the prediction of flyrock based on the data obtained from the Ulu Thiram quarry that is located in Malaysia. To evaluate and document the success and reliability of the new DNN model, an ANN model based on five different data categories from the established database, was also developed and then compared with the DNN model. Based on the obtained results [i.e. coefficient of determination (R-2) = 0.9829 and 0.9781, root mean square error (RMSE) = 8.2690 and 9.1119 for DNN and R-2 = 0.9093 and 0.8539, RMSE = 19.0795 and 25.05120 for ANN], a significant increase in predicting flyrock is achieved by developing this DNN predictive model. Then, the DNN model was selected as a function for optimizing flyrock by a powerful optimization technique namely whale optimization algorithm (WOA). The WOA was able to minimize the flyrock resulting from blasting and provide a suitable pattern for blasting operations in mines.

关键词： Deep neural network Artificial neural network whale optimization algorithm Flyrock optimization

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Modified Search Strategies Assisted Crossover whale optimization algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

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REMOTE SENSING 2019年第23期11卷 2795-2795页

作者： Xiong, Guojiang Zhang, Jing Shi, Dongyuan Zhu, Lin Yuan, Xufeng Yao, Gang Guizhou Univ Coll Elect Engn Guizhou Key Lab Intelligent Technol Power Syst Guiyang 550025 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Guizhou Elect Power Grid Dispatching & Control Ct Guiyang 550002 Peoples R China

Extracting accurate values for involved unknown parameters of solar photovoltaic (PV) models is very important for modeling PV systems. In recent years, the use of metaheuristic algorithms for this problem tends to be more popular and vibrant due to their efficacy in solving highly nonlinear multimodal optimization problems. The whale optimization algorithm (WOA) is a relatively new and competitive metaheuristic algorithm. In this paper, an improved variant of WOA referred to as MCSWOA, is proposed to the parameter extraction of PV models. In MCSWOA, three improved components are integrated together: (i) Two modified search strategies named WOA/rand/1 and WOA/current-to-best/1 inspired by differential evolution are designed to balance the exploration and exploitation;(ii) a crossover operator based on the above modified search strategies is introduced to meet the search-oriented requirements of different dimensions;and (iii) a selection operator instead of the "generate-and-go" operator used in the original WOA is employed to prevent the population quality getting worse and thus to guarantee the consistency of evolutionary direction. The proposed MCSWOA is applied to five PV types. Both single diode and double diode models are used to model these five PV types. The good performance of MCSWOA is verified by various algorithms.

关键词： metaheuristic parameter extraction solar photovoltaic whale optimization algorithm

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A modified nature inspired meta-heuristic whale optimization algorithm for solving 0-1 knapsack problem

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第3期10卷 495-514页

作者： Abdel-Basset, Mohamed El-Shahat, Doaa Sangaiah, Arun Kumar Zagazig Univ Fac Comp & Informat Dept Operat Res Zagazig Egypt Zagazig Univ Fac Comp & Informat Comp Sci Dept El Zera Sq Zagazig Sharqiyah Egypt VIT Univ Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

The whale optimization algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social character of the humpback whales. WOA has an efficient performance in solving the continuous problems and engineering optimization problems. This paper presents an improved whale optimization algorithm (IWOA) for solving both single and multidimensional 0-1 knapsack problems with different scales. A penalty function is added to the evaluation function so that the fitness of the feasible solutions can outperform the fitness of the infeasible ones. The sigmoid function can take the real-valued solutions as input and produces the binary solutions as output. A two-stage repair operator is employed for handling the infeasible solutions. IWOA can give a better tradeoff between the diversification and the intensification through using two strategies: Local Search Strategy (LSS) and the Levy flight walks. In addition to the bitwise operation which increases the IWOA efficiency. The proposed IWOA is compared with other state-of-art algorithms to validate the effectiveness of the proposed algorithm in solving 0-1 knapsack problems. Further, the experimental results and extensive numerical illustrations have been indicated that IWOA is efficient, effective, and robust for solving the hard 0-1 knapsack problems than the other existing approaches in the available literature. The source code of the IWOA algorithm will be available after the paper accepted as a toolbox in MATLAB library.

关键词： whale optimization algorithm optimization Knapsack problem Multidimensional knapsack Nature inspired meta-heuristic algorithms

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Evolving Support Vector Machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts

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KNOWLEDGE-BASED SYSTEMS 2018年 153卷 91-104页

作者： Al-Zoubi, Ala' M. Faris, Hossam Alqatawna, Ja'far Hassonah, Mohammad A. Univ Jordan Business Informat Technol Dept King Abdullah II Sch Informat Technol Amman Jordan Jordan Informat Secur & Digital Forens Res Grp JI Amman Jordan Higher Coll Technol Dubai Comp Informat & Sci Abu Dhabi U Arab Emirates

Detecting spam profiles is considered as one of the most challenging issues in online social networks. The reason is that these profiles are not just a source for unwanted or bad advertisements, but could be a serious threat;as they could initiate malicious activities against other users. Realizing this threat, there is an incremental need for accurate and efficient spam detection models for online social networks. In this paper, a hybrid machine learning model based on Support Vector Machines and one of the recent metaheuristic algorithms called whale optimization algorithm is proposed for the task of identifying spammers in online social networks. The proposed model performs automatic detection of spammers and gives an insight on the most influencing features during the detection process. Moreover, the model is applied and tested on different lingual datasets, where four datasets are collected from Twitter in four languages: Arabic, English, Spanish, and Korean. The experiments and results show that the proposed model outperforms many other algorithms in terms of accuracy, and provides very challenging results in terms of precision, recall, f-measure and AUC. While it also helps in identifying the most influencing features in the detection process. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Spam detection Feature selection Social networks Twitter Classification whale optimization algorithm WOA Metaheuristic Support Vector Machine SVM Multilingual

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Emphasizing the importance of shift invariance in metaheuristics by using whale optimization algorithm as a test bed

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SOFT COMPUTING 2021年第22期25卷 14209-14225页

作者： Askari, Qamar Younas, Irfan Saeed, Mehreen GIFT Univ Dept Comp Sci Gujranwala Pakistan Natl Univ Comp & Emerging Sci FAST Sch Comp Lahore Pakistan

To solve global optimization problems, nature-inspired metaheuristics have emerged as the best option in many cases. The researchers have mimicked or mapped the optimal behaviors of living organisms, natural phenomena and human behaviors to solve optimization problems. Whenever a new algorithm is proposed, its exploration, exploitation and convergence capabilities are tested;however, in this study, it is highlighted that a metaheuristic should also exhibit invariance to function translation/shifting. whale optimization algorithm (WOA) is a well-reputed and highly referred algorithm in the literature;however, we find that its performance is strongly affected under certain circumstances involving function shifting. Our analysis reveals that WOA performs really well when the global optimum is situated at the origin (0, 0, ..., 0);on the contrary, the performance amazingly degrades whenever the global optimum is situated away from the origin. Moreover, in most cases, our findings reveal that the performance degradation is directly proportional to the distance of the global optimum from the origin. Furthermore, the root cause of the problem is discovered and discussed. Finally, it is suggested how WOA can be made shift-invariant.

关键词： whale optimization algorithm Shift invariance Metaheuristics WOA analysis WOA limitations

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Multi-trial Vector-based whale optimization algorithm

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Journal of Bionic Engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The whale optimization algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based whale optimization algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： Swarm intelligence algorithms Metaheuristic algorithms optimization Engineering design problems whale optimization algorithm

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