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

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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IWOA: An improved whale optimization algorithm for optimization problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2019年第3期6卷 243-259页

作者： Bozorgi, Seyed Mostafa Yazdani, Samaneh Islamic Azad Univ North Tehran Branch Dept Comp Engn Tehran Iran

The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overcome this limitation of WOA, in this paper WOA is hybridized with differential evolution (DE) which has good exploration ability for function optimization problems. The proposed method is named Improved WOA (IWOA). The proposed method, combines exploitation of WOA with exploration of DE and therefore provides a promising candidate solution. In addition, IWOA(+) is presented in this paper which is an extended form of IWOA. IWOA(+) utilizes reinitialization and adaptive parameter which controls the whole search process to obtain better solutions. IWOA and IWOA(+) are validated on a set of 25 benchmark functions, and they are compared with PSO, DE, BBO, DE/BBO, PSO/GSA, SCA, MFO and WOA. Furthermore, the effects of dimensionality and population size on the performance of our proposed algorithms are studied. The results demonstrate that IWOA and IWOA(+) outperform the other algorithms in terms of quality of the final solution and convergence rate. (C) 2019 Society for Computational Design and Engineering. Publishing Services by Elsevier.

关键词： whale optimization algorithm Swarm intelligence Meta-heuristic algorithm optimization Differential evolution

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Improving whale optimization algorithm with Elite Strategy and Its Application to Engineering-Design and Cloud Task Scheduling Problems

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COGNITIVE COMPUTATION 2023年第5期15卷 1497-1525页

作者： Chakraborty, Sanjoy Saha, Apu Kumar Chhabra, Amit Natl Inst Technol Agartala Dept Comp Sci & Engn Agartala Tripura India Iswar Chandra Vidyasagar Coll Dept Comp Sci & Engn Belonia Tripura India Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India Guru Nanak Dev Univ Dept Comp Engn & Technol Amritsar 143005 Punjab India

The whale optimization algorithm (WOA), a biologically inspired optimization technique, is known for its straightforward design and effectiveness. Despite many advantages, it has certain disadvantages, such as a limited exploration capacity and early convergence as a result of the minimal exploration of the search process. The WOA cannot bypass the local solution;consequently, the search is unbalanced. This study introduces a new variant of WOA, namely elite-based WOA (EBWOA), to address the inherent shortcomings of traditional WOA. Unlike the three phases used in the traditional WOA, only the encircling prey and bubble-net attack phases are applied in the new variant. Using the local elite method, exploration will be conducted with an encircling prey phase to ensure some exploitation during exploration. The choice between exploration and exploitation is achieved by introducing a new choice parameter. An inertia weight (omega(i)) is used in both phases to scour the region. The EBWOA is used to evaluate twenty-five benchmark functions, IEEE CEC 2019 functions, and two design problems and compared to several fundamental techniques and WOA variants. In addition, the EBWOA is used to solve the practical cloud scheduling problem. Performance is compared against a variety of metaheuristics using real cloud workloads by running experiments on the standard CloudSim simulator. Comparing the numerical results of benchmark functions, IEEE CEC 2019 functions, statistical verification, and the solution generation speed of EBWOA confirmed the effectiveness of the proposed EBWOA approach. It has also shown a great improvement over baseline algorithms in creating efficient schedul-ing solutions by significantly reducing makespan time and energy consumption targets.

关键词： Metaheuristics whale optimization algorithm Elite mechanism Modified WOA Cloud scheduling problem Real-world application

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Stability Investigation of Improved whale optimization algorithm in the Process of Feature Selection

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IETE TECHNICAL REVIEW 2022年第2期39卷 286-300页

作者： Khaire, Utkarsh Mahadeo Dhanalakshmi, R. GH Raisoni Coll Engn Dept Comp Sci & Engn Nagpur 440016 Maharashtra India Indian Inst Informat Technol Dept Comp Sci & Engn Tiruchirappalli 620015 Tamil Nadu India

The removal of irrelevant and insignificant features from the high-dimensional dataset is a necessary prerequisite for the exploration of information. Meta-heuristic optimization techniques have been widely used in the field of knowledge discovery over the last few years. The whale optimization algorithm (WOA) is a swarm-based metaheuristic technique that is often used in the field of dimensionality reduction. Among the various WOA-based feature selection techniques in the literature, not a single technology illuminates the stability issue of WOA. Stability is often identified as a sensitivity to the disruption of input data during the process of selecting significant features. In this study, a new feature selection model based on improved WOA (iWOA) is proposed to select significant features from a high-dimensional microarray dataset. The stability of the results obtained is evaluated with the existing stability index that satisfies all the required characteristics of the stability measure. In addition, the results of the proposed model are compared with other contemporary meta-heuristics techniques. The proposed iWOA proposes its identification as a well-stable feature selection technique according to the strength of the stability index agreement.

关键词： Feature selection High-dimensional data Meta-heuristics optimization Stability whale optimization algorithm

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Comparative performance analysis of 2DOF state feedback controller for automatic generation control using whale optimization algorithm

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OPTIMAL CONTROL APPLICATIONS & METHODS 2019年第1期40卷 24-42页

作者： Simhadri, Kumara Swamy Mohanty, Banaja Panda, Sanjaya Kumar Veer Surendra Sai Univ Technol Dept Elect Engn Burla India Veer Surendra Sai Univ Technol Dept Informat Technol Burla India

This paper presents the design of two-degree-of-freedom state feedback controller (2DOFSFC) for automatic generation control problem. A recently developed new metaheuristic algorithm called whale optimization algorithm is employed to optimize the parameters of 2DOFSFC. The proposed 2DOFSFC is analyzed for a two-area interconnected thermal power system including governor dead band nonlinearity and further extended to multiunit hydrothermal power system. The supremacy of the 2DOFSFC is established comparing with proportional-integral, proportional-integral-derivative (PID), and 2DOFPID controllers optimized with different competitive algorithms for the concerned system. The sensitivity analysis of the optimal 2DOFSFC is performed with uncertainty condition made by varying bias coefficient B and regulation R parameters. Furthermore, the proposed controller is also verified against random load variations and step load perturbation at different locations of the system.

关键词： governor dead band (GDB) two-degree-of-freedom state feedback controller (2DOFSFC) whale optimization algorithm

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An Effective Approach for Determining Rock Discontinuity Sets Using a Modified whale optimization algorithm

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ROCK MECHANICS AND ROCK ENGINEERING 2023年第8期56卷 6143-6155页

作者： Yi, Xiaoyu Feng, Wenkai Wu, Wenxuan Zhou, Yongjian Dong, Shan Chengdu Univ Technol Coll Environm & Civil Engn Chengdu 610059 Peoples R China Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China China Univ Geosci Badong Natl Observat & Res Stn Geohazards Wuhan 430074 Peoples R China

HighlightsAn effective method for determining rock discontinuity sets based on the MWOA is *** proposed method has a good clustering effect, convergence speed, and stabilityThe proposed method can determine rock disco... 详细信息

关键词： Rock discontinuity sets Cluster analysis whale optimization algorithm Discontinuity orientation

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An efficient facial emotion recognition using convolutional neural network with local sorting binary pattern and whale optimization algorithm

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2024年 1-16页

作者： Aghabeigi, Fereshteh Nazari, Sara Eraghi, Nafiseh Osati Islamic Azad Univ Dept Comp Engn & Informat Technol Arak Branch Arak Iran

Facial emotion recognition is one of the fields of machine learning and pattern recognition. Facial expression recognition is used in a variety of applications. For robust automatic facial emotion recognition, feature extraction from input image data is challenging. To address this issue, we propose an emotion recognition system based on a new feature extraction method, whale optimization algorithm, and convolutional neural network. In the feature extraction phase, a new efficient human face descriptor is expressed using a local sorting binary pattern and a convolutional neural network. Also, the hyperparameters of the convolutional neural network are optimized using the whale optimization algorithm. Then, the convolutional neural network is applied for classification. The performance of the proposed method is evaluated using three well-known face databases CK+ (extended Cohn-Kanade) with facial expressions (happiness, sadness, fear, anger, disgust, contempt, and surprise), JAFFE (Japanese female facial expression) with (happiness, sadness, anger, fear, neutral, disgust, and surprise), and MMI (MMI facial expression database) with facial emotions (happiness, sadness, anger, fear, disgust, and surprise). The accuracies with CK+, JAFFE, and MMI are 100%, 99.93%, and 99.83%, respectively. Experimental results demonstrate that the proposed model can provide better performance compared to alternative methods.

关键词： Feature extraction Convolutional neural network Local sorting binary pattern Facial emotion recognition Deep learning whale optimization algorithm

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Application of Adaptive whale optimization algorithm Based BP Neural Network in RSSI Positioning

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Journal of Beijing Institute of Technology 2024年第6期33卷 516-529页

作者： Duo Peng Mingshuo Liu Kun Xie School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss *** algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction *** address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation *** processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging ***,the position of the node to be measured is determined by combining the weighted centroid *** simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.

关键词： wireless sensor network received signal strength neural network whale optimization algorithm adaptive weight factor extended Kalman filter

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A new recommendation system using map-reduce-based tournament empowered whale optimization algorithm

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COMPLEX & INTELLIGENT SYSTEMS 2021年第1期7卷 297-309页

作者： Tripathi, Ashish Kumar Mittal, Himanshu Saxena, Pranav Gupta, Siddharth Malviya Natl Inst Technol Jaipur Rajasthan India Jaypee Inst Informat Technol Noida India

In the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding similarity. However, this approach fails in managing large-scale datasets. To mitigate the same, an efficient map-reduce-based clustering recommendation system is presented. The proposed method uses a novel variant of the whale optimization algorithm, tournament selection empowered whale optimization algorithm, to attain the optimal clusters. The clustering efficiency of the proposed method is measured on four large-scale datasets in terms of F-measure and computation time. The experimental results are compared with state-of-the-art map-reduce-based clustering methods, namely map-reduce-based K-means, map-reduce-based bat algorithm, map-reduce-based Kmeans particle swarm optimization, map-reduce-based artificial bee colony, and map-reduce-based whale optimization algorithm. Furthermore, the proposed method is tested as a recommendation system on the publicly available movie-lens dataset. The performance validation is measured in terms of mean absolute error, precision and recall, over a different number of clusters. The experimental results assert that the proposed method is a permissive approach for the recommendation over large-scale datasets.

关键词： Recommendation system Big data Map-reduce Clustering whale optimization algorithm

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An Intelligent Probabilistic whale optimization algorithm (i-WOA) for Clustering in Vehicular Ad Hoc Networks

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INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS 2022年第2期29卷 143-156页

作者： Husnain, Ghassan Anwar, Shahzad Univ Engn & Technol Dept Mechatron Engn Peshawar Pakistan Iqra Natl Univ Dept Comp Sci Peshawar Pakistan

Vehicular ad hoc networks an important network type plays a significant role in various applications, such as traffic administration, media applications, secure financial transaction, etc. In VANETs, topology rapidly changes due to high vehicle movements, and scarce vehicle distribution (on highways) affects network scalability which makes the cluster of VANETs unstable and difficult to maintain routes of all vehicles in a network These challenges appeal to researchers' attention to allow vigorous, consistent, and scalable transmission and receiving of data, particularly in a highly compact network. This framework proposes and demonstrates an efficient clustering technique for routing optimization in Intelligent Transportation Systems (ITS). An intelligent probability-based bio-inspired whale optimization algorithm for clustering in VANETs (i-WOA) has been proposed considering communication range, the number of nodes (density), velocity, route on the highway during the process of cluster formation for vehicular communication by incorporating fitness function probability thus minimizing the randomness. The results were compared with already established methods and demonstrate that the proposed i-WOA method produces an optimal number of cluster heads (CHs) in various scenarios, for instance, communication ranges, network size, and node density. Statistical tests are performed to further validate developed method superiority over other established bio-inspired methods. The results exhibit a 75% (regression analysis) improvement in cluster optimization for VANETs with application in ITS, consequently reducing communication cost and routing overhead hence increasing network lifetime.

关键词： Bio-inspired metaheuristic Intelligent clustering Vehicular ad hoc networks whale optimization algorithm Intelligent systems

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