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RETRACTED: A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem (Retracted article. See vol. 128, pg. 567, 2022)

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 85卷 129-145页

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

The flow shop scheduling problem is one of the most important types of scheduling with a large number of real-world applications. In this paper, we propose a new algorithm that integrates the whale optimization algorithm (WOA) with a local search strategy for tackling the permutation flow shop scheduling problem. The Largest Rank Value (LRV) requires the algorithm to deal with the discrete search space of the problem. The diversity of candidate schedules is improved using a swap mutation operation as well. In addition to the insert-reversed block operation is adopted to escape from the local optima. The proposed hybrid whale algorithm (HWA) is incorporated with Nawaz-Enscore-Ham (NEH) to improve the performance of the algorithm. It is observed that HWA gives competitive results compared to the existing algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Flow shop scheduling Makespan whale optimization algorithm Hybrid algorithm Local search

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Link Prediction Based on whale optimization algorithm

Link Prediction Based on Whale Optimization Algorithm

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Barham, Reham Aljarah, Ibrahim Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan

ISBN: (纸本)9781538605271

Link prediction problem has a wide variety of useful applications in diverse fields. For example, in bio-informatics, such as protein-protein interaction metabolic and diseases-gene networks, there are links between nodes, which indicate they have an interaction relationship. This paper presents a possible solution to the well known link prediction problem using a whale optimization algorithm (WOA), which is considered as one of the recent optimization algorithms. The link prediction problem is formulated as an optimization problem to predict the links in any type of the networks. Experimental results of the proposed algorithm (WOA-LP) on a number of real networks are good evidence that the proposed approach can enhance the link prediction accuracy.

关键词： algorithm Heuristic Link Prediction optimization Social Network Analysis Association whale optimization algorithm WOA

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TRIAD: whale optimization algorithm for 5G-IoT Resource Allocation Decision in Edge Computing 16

TRIAD: Whale Optimization Algorithm for 5G-IoT Resource Allo...

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16th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Lieira, Douglas Dias Quessada, Matheus Sanches Cristiani, Andre Luis Immich, Roger Meneguette, Rodolfo Ipolito Univ Estadual Paulista UNESP DCCE Sao Jose Do Rio Preto SP Brazil Inst Fed Sao Paulo IFSP Catanduva Sao Jose Do Rio Preto SP Brazil Univ Fed Sao Carlos UFSCar Dept Comp Sao Carlos SP Brazil Univ Fed Rio Grande do Norte UFRN Inst Metropole Digital Natal RN Brazil Univ Sao Paulo Dept Sistemas Comp ICMC Sao Carlos SP Brazil

ISBN: (纸本)9789895465910

The massive growth in the number of 5G-IoT devices circulating in the world has increased the demand for computing resources in recent years. That way, it is necessary to search for the development of new solutions or improvements to existing ones. Edge computing is one of the solutions that have been used to improve the care of these types of devices. In this work, we proposed a mechanism that uses the whale optimization algorithm for 5G-IoT resource allocation decision in edge computing (TRIAD). The TRIAD was compared with the Greedy and Reliable techniques, available in the literature. The results show that the proposed algorithm had excellent efficiency in the service of the devices, in addition to denying fewer requests and blocking fewer devices during the search. The TRIAD, in some situations of the simulation, served approximately 265% more services, denied 56% less requests and blocked 65% less services.

关键词： edge computing internet of things meta-heuristic resource allocation whale optimization algorithm

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Improving Energy Usage in Wireless Sensor Networks by whale optimization algorithm 29

Improving Energy Usage in Wireless Sensor Networks by Whale ...

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29th Telecommunications Forum (TELFOR)

作者： Strumberger, Ivana Bezdan, Timea Ivanovic, Milica Jovanovic, Luka Singidunum Univ Informat & Comp Belgrade Serbia Singidunum Univ Tech Fac Belgrade Serbia

ISBN: (纸本)9781665425858

The most important challenge in wireless sensor networks is the energy saving by reducing the energy consumption of the sensor nodes. If the sensor node's energy is depleted, it could affect the connectivity of the network as a whole. The available energy of sensor nodes is therefore the most valuable resource, and optimization can enhance the lifetime of the network. The optimization significantly depends of the energy model of the MAC layer. This paper proposes a whale optimization algorithm for optimizing the transmitted power model, with a goal to minimize the energy consumption per bit. The results of the conducted experiments indicate that the proposed model obtains better performances than the simple model before application of the algorithm, therefore affecting and prolonging the overall network lifetime.

关键词： wireless sensor networks optimization energy usage swarm intelligence metaheuristics whale optimization algorithm

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optimization of the Deployment of Wireless Sensor Networks Dedicated to Fire Detection in Smart Car Parks using Chaos whale optimization algorithm

Optimization of the Deployment of Wireless Sensor Networks D...

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IEEE International Conference on Communications (ICC)

作者： Benghelima, Slimane Charafeddine Ould-Khaoua, Mohamed Benzerbadj, Ali Baalat, Oumaya Ben-Othman, Jalel Univ Blida 1 Dept Informat LRDSI Ouled Yaich Algeria Ain Temouchent Univ Belhadj Bouchaib Temouchent Algeria Univ Oran 1 Ahmed Ben Bella Res Lab Ind Comp & Networks RIIR Es Senia Algeria Univ Bourgogne Franche Comte FEMTO ST Inst CNRS Belfort France Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst F-91190 Gif Sur Yvette France Univ Sorbonne Paris Nord Paris France Zayed Univ Coll Technol Innovat Dubai U Arab Emirates

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

Smart Car Parks (SCPs) based on Wireless Sensor Networks (WSNs) are one of the most interesting Internet of Things applications. This paper addresses the deployment optimization problem of two-tiered WSNs dedicated to fire monitoring in SCPs. Networks deployed inside the SCP consist of three types of nodes: Sensor Nodes (SNs) which cover the spots within the parking area, Relay Nodes (RNs) which forward alert messages generated by SNs, and the Sink node which is connected to the outside world (e.g, firefighters), through a high bandwidth connection. We propose an algorithm based on chaos theory and whale optimization algorithm (WOA), which minimizes simultaneously the deployed number of SNs, RNs, and network diameter while ensuring coverage and connectivity. To evaluate the effectiveness of our proposal, we have conducted extensive tests. The results show that the Chaos WOA (CWOA) outperforms the original WOA in terms of solution quality and computation time and by comparison with an exact method, CWOA provides results very close to the optimal in terms of fitness value and is efficient in terms of computational time when the problem becomes more complex.

关键词： whale optimization algorithm Chaos map WSN deployment Smart parking fire surveillance Internet of things

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whale optimization algorithm for Active Damping of LCL-Filter-Based Grid-Connected Converters

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2019年第2期9卷 986-996页

作者： Hato, Mohammed Mazen Bouallegue, Soufiene Univ Tunis El Manar Res Lab Automat Control LARA Natl Engn Sch Tunis ENIT BP 37 Tunis 1002 Tunisia

Grid-connected converters which are equipped with LCL-filters have drawn more and more attention due to their capability to accomplish a better harmonic reduction in comparison with the L-filters based ones. However, due to the resonance problem of the LCL-filters, stability issues may emerge in the current loop in case of the resonance peak is not fairly damped. Active damping strategy is preferred to the passive one due to the power losses on passive resistor and its additional cost. The capacitor current feedback active damping control approach, which is based on voltage oriented of Proportional Integral (PI), can be adopted as a good candidate to outperform the resonance hazard. Since the selection of PI controllers' parameters and capacitor current gains by iterative trials-errors based procedure becomes a time consuming and difficult task, an optimization problem under operating time-domain restrictions is modeled for tuning these decision variables. Such an optimization based-current feedback active damping control problem is managed by means of the advanced whale optimization algorithm (WOA). To evaluate the effectiveness of the introduced WOA metaheuristic, an empirical comparison study with the homologous Water Cycle algorithm (WCA), Grey Wolf Optimizer (GWO), Artificial Bee Colony (ABC) and Particle Swarm optimization methods is achieved. Statistical analysis is performed by using nonparametric Friedman's rank and Bonferroni-Dunn's test to check the significance of each algorithm. Demonstrative simulations highlight that the WOA method provides competitive solutions in terms of robustness and performance for the active damping controllers' tuning problem.

关键词： Active damping grid-connected converter LCL-filter PI tuning whale optimization algorithm Bonferroni-Dunn's and Friedman's tests

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whale optimization algorithm for Energy- Efficient Task Allocation in the Internet of Things

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第10期14卷 235-243页

作者： Yang, Shan Yu, Renping Jin, Xin State Grid Hubei Elect Power Co Ltd Wuhan 430077 Peoples R China Hubei Huazhong Elect Power Technol Dev Co Ltd Wuhan 430077 Peoples R China

Internet of Things (IoT) represents a new paradigm where various physical devices interact and collaborate to achieve common goals. This technology encompasses sensors, mobile phones, actuators, and other smart devices that work together to perform tasks and applications. In order to ensure optimal performance of these tasks and applications, task allocation becomes a critical aspect in IoT networks. Task allocation in IoT networks is a complex problem due to the intricate connections and interactions among devices. The task allocation issue is generally recognized as NP-hard, necessitating the development of effective optimization solutions. This paper proposes a solution using the whale optimization algorithm (WOA) to address the task allocation problem in IoT networks. By leveraging the capabilities of the WOA, our algorithm aims to improve energy efficiency and enhance network stability. The performance of the algorithm was tested comprehensively on the MATLAB simulation platform. Our algorithm outperforms existing algorithms in the literature, especially in terms of energy efficiency, as per the findings.

关键词： Task allocation internet of things energy efficiency optimization whale optimization algorithm

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Nonlinear predictive control based on echo state network and whale optimization algorithm 39

Nonlinear predictive control based on echo state network and...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Wang, Shuo Zhao, Xiyuan Li, Xuyue Zhou, YuTing Zhang, Danfeng Jin, Xin Liaoning Petrochem Univ Coll Informat & Control Engn Fushun Peoples R China

ISBN: (纸本)9798350390780;9798350379228

In response to the challenge of difficulty in modeling and low prediction accuracy for complex nonlinear processes, this paper proposes a nonlinear predictive control method that combines Echo State Networks and whale optimization algorithm. Firstly, an optimized Echo State Network is utilized to construct a predictive model, capturing the nonlinear dynamic characteristics of the system. Subsequently, this predictive model is integrated with model predictive control to achieve predictive control of the system. During the control process, the whale optimization algorithm is employed to solve the objective function in the rolling optimization loop, facilitating optimization adjustments to the control parameters. Finally, the proposed controller is applied to simulate and validate its effectiveness in a continuous stirred-tank reactor process. Simulation results demonstrate the efficacy of this approach.

关键词： Model Predictive Control Neural network Echo State Network Nonlinear system whale optimization algorithm

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An Improved Binary whale optimization algorithm for Feature Selection of Network Intrusion Detection 4

An Improved Binary Whale Optimization Algorithm for Feature ...

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4th IEEE International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)

作者： Xu, Hui Fu, Yingchun Fang, Ce Cao, Qianqian Su, Jun Wei, Siwei Hubei Univ Technol Wuhan 430068 Hubei Peoples R China CCCC Second Highway Consultants Co Ltd Wuhan 430052 Hubei Peoples R China

ISBN: (纸本)9781538675878

As for network intrusion detection, the time and space consumption of the detection algorithm increases with the feature dimension. The irrelevant or redundant features may cause the detection accuracy to decrease. In order to improve the timeliness and reliability of intrusion detection, feature selection is one of the most effective methods proposed currently. Considering that whale optimization algorithm (WOA) has good classification performance and dimensionality reduction in reducing feature redundancy, it can be applied to feature selection of network intrusion detection. The WOA converges slowly in the search process and easily falls into a local optimum in the updating mechanism, which restricts the classification performance and dimensionality reduction of the algorithm. Therefore, this paper adopts the nonlinear convergence factor strategy in the search process and introduces the Particle Swarm optimization (PSO) strategy in the updating mechanism. An Improved Binary whale optimization algorithm (IBWOA) is then proposed in order to achieve a better dimension reduction on the premise of ensuring accuracy for feature selection of intrusion detection. In the experimental stage, multiple data sets in the UCI database for machine learning were used for verification by comparing with other feature selection methods. Finally, the KDD CUP 99 data set was used for verification of feature selection in intrusion detection. The experimental results show that, compared with Genetic algorithm (GA), PSO and WOA, the IBWOA improves the classification accuracy and dimensionality reduction in feature selection of intrusion detection.

关键词： network intrusion detection feature selection whale optimization algorithm convergence factor Particle Swarm optimization

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Tuning of PID Controller using whale optimization algorithm for Different Systems

Tuning of PID Controller using Whale Optimization Algorithm ...

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IEEE International Conference on Digital Futures and Transformative Technologies (ICoDT2)

作者： Zaman, Uzair Khaleeq uz Naveed, Kanwal Kumar, Atal Anil Natl Univ Sci & Technol Coll Elect & Mech Engn Dept Mechatron Engn Rawalpindi Pakistan Univ Lorraine Arts & Metiers Inst Technol Lab Concept Fabricat Commande Metz France

ISBN: (纸本)9781665412858

In this paper, a recently developed meta-heuristic optimization method, whale optimization algorithm (WOA) is used to tune the parameters of PID controller for different systems. The algorithm is evaluated by setting integral absolute error and integral time absolute error as objective function for various transfer functions. The results obtained are compared with Ziegler-Nichols (Z-N), Genetic algorithm (GA) and Particle Swarm optimization (PSO). The results prove the effectiveness of the proposed algorithm and provides the researchers with options for improved system performance.

关键词： integral absolute error meta-heuristic algorithm PID controller whale optimization algorithm

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