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14th International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Huynh, Luan N. T. Quoc-Viet Pham Nguyen, Tri D. T. Hossain, Md Delowar Park, Jae Ho Huh, Eui-Nam Kyung Hee Univ Dept Comp Sci & Engn Gwangju Si South Korea Inje Univ High Safety Core Technol Res Ctr Gimhae Si South Korea

ISBN: (纸本)9781728154534

In recent years, computation offloading in multi-access edge computing (MEC) has received a lot of attention. For example, the joint optimization of computing resource allocation and offloading decision was studied to reduce the overall latency and energy consumption of MEC offloading. Such problem of optimizing the computing resource allocation and offloading decision is often formulated as a mixed-integer nonlinear programming (MINLP) problem, which is also NP-hard in general. Various solution approaches have been proposed such as heuristics, dynamic programming, branch-and-bound, and machine learning. Motivated by applications to a variety of complex optimization problems, in this paper, we provide an alternative meta-heuristic method using whale optimization algorithm (WOA). We have proposed a joint computing resource allocation and offloading decision system using the WOA to minimize the total computing overhead of mobile users (MUs), including completion time and energy consumption. Through numerical simulations, our proposed algorithm is more efficient than several baseline schemes for reducing the total computing overhead of MUs.

关键词： Computation Offloading Meta-heuristic optimization whale optimization algorithm Multi-Access Edge Computing

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Location Allocation of Earthquake Relief Centers in Yazd City Based on whale optimization algorithm

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第5期34卷 1184-1194页

作者： Ashkezari, A. Zarepor Yazdi, H. Mosalman Islamic Azad Univ Dept Civil Engn Yazd Branch Yazd Iran Islamic Azad Univ Dept Civil Engn Maybod Branch Maybod Iran

Despite the fact that many governments try to set rules that guarantee having resistant buildings, there are many vulnerable structures in the world. Hence, establishing earthquake relief centers is an important issue in order to control the effect of an earthquake. Iran is a country in middle east which is severely vulnerable against earthquake. Yazd is a central city in Iran. Since there is no such a study for Yazd city, this city is considered in this study. The parcels' layer of the GIS map of Yazd city has been used as the input of the problem. Since the location allocation of relief centers is a problem with huge complexity and cannot be solved in polynomial time, whale optimization algorithm (WOA) has been used to solve the problem. The whale optimization algorithm or The WOA is a particle based heuristic algorithm which is suitable for solving hard problems. The main contributions of the research are modifying WOA function for the problem and designing a new method for creating whales. In order to reduce the time of reaching to the reasonable solution an innovative whale generating method has been designed. The results show that average distance of each parcel from its relief center is 1541 meters and the standard deviation of 114.

关键词： Disaster Management Earthquake Heuristic algorithms Relief Center whale optimization algorithm

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Presenting a New Method for Earthquake Relief Center Location Allocation Based on whale optimization algorithm

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第4期34卷 769-781页

作者： Ashkezari, A. Zarepor Yazdi, H. Mosalman Islamic Azad Univ Dept Civil Engn Yazd Branch Yazd Iran Islamic Azad Univ Maybod Branch Dept Civil Engn Maybod Iran

Despite the regulations set for the reinforcement of structures, many buildings in the world are vulnerable to earthquakes. Local governments try to be prepared to cope with this possible crisis through establishing earthquake relief centers. Considering the budget provision for creating n relief centers in a certain region, the main problem is yet in which place these centers should be constructed in order to achieve the highest speed and quality in rescuing after an earthquake. The enormous number of points that need relief, and of the many locations that could be candidates for constructing a relief center have caused this problem to be considered as an NP-Complete problem. In this article, there is a focus on solving the location allocation of the earthquake relief center problem. In order to find a reasonable solution, whale optimization algorithm has been used. Classic whale functions have been modified for this research dedicatedly. Results of the algorithm implementation and its execution on the map of region 1 of the city of Tehran show that with 9 relief centers, the average distance between each point and the center is roughly equal to 760 meters, showing almost 1.9% optimization compared to the best recent articles.

关键词： Disaster Management Earthquake Relief Center Heuristic algorithms whale optimization algorithm

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Proposing a New Image Watermarking Method Using Shearlet Transform and whale optimization algorithm

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第4期34卷 843-853页

作者： Saadati, M. Vahidi, J. Seydi, V Mashhadi, P. Sheikholharam Islamic Azad Univ Dept Comp Engn South Tehran Branch Artificial Intelligence Tehran Iran Iran Univ Sci & Technol Dept Appl Math Tehran Iran

Digital watermarking is a method for data hiding that ensures the security of multimedia data. The watermark can be a digital image or data stored within digital content. The Shearlet transform, a multi resolution and multi-directional conversion, can be used for watermarking in digital images. Due to its superior features, this conversion can increase the efficiency of applications such as watermarking of images. In this paper, Shearlet and the Singular Value Decomposition (SVD) transforms are used with the whale optimization algorithm to obtain the most appropriate scaling factor in the watermark extraction step after applying different types of image processing attacks. Shearlet transform has more transparency than traditional converts. The SVD transform also increases the robustness of watermarking operations. The results of different experiments show that the new method presented in this paper, in terms of robustness and imperceptibility compared to the evaluated methods, performs better than most image processing attacks.

关键词： Shearlet Transform whale optimization algorithm Singular Value Decomposition Image Watermarking

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Path Planning of Underwater Vehicles Based on Improved whale optimization algorithm

Path Planning of Underwater Vehicles Based on Improved Whale...

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作者： Guangqiang Li Wenchao Dong Yongjie Wang Daqing Zhu Qi Liu School of Marine Electrical Engineering Dalian Maritime University

In order to better solve path planning problems of underwater vehicles on complicated seabed terrains under ocean currents,a global path planning method based on whale optimization algorithm(WOA) is *** focus on the fixed-depth navigation,and the underwater environment is modeled based on the MAKLINK *** criteria of the length,smoothness and energy cost are used to evaluate a *** that the original WOA algorithm lacks of balance in the exploration and exploitation at different positions in the search space and is easy to fall into local optima,the improved whale optimization algorithm(IWOA) is *** proposed algorithm creates an adaptive elite set by selecting best individuals from the population,and the members of the set will be randomly selected to be combined into new individuals to guide the searching process of the proposed *** corresponding guidance strategy is that the search agents update the position directionally based on the locations and orientations of the new individuals,replacing the encircling update centered on the position of prey in the original *** algorithm is utilized for "coarse-grained planning" of path planning,and an approach to augment the waypoints is developed to cooperate with the proposed IWOA to complete the "fine-grained planning".Above two-step planning forms the global path planning method proposed in this *** simulation results show that the proposed method is feasible and effective through testing under different *** with original WOA,paths planned by proposed method and IWOA have higher qualities,with shorter lengths,smoother paths and more energy saving.

关键词： path planning whale optimization algorithm underwater vehicles

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An Adaptive Evolutionary whale optimization algorithm

An Adaptive Evolutionary Whale Optimization Algorithm

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第33届中国控制与决策会议

作者： CHEN Juan RONG Hongkun ZHANG Zheng LUO Ruihan College of Information Science & Technology Beijing University of Chemical Technology

Compared with traditional swarm intelligence algorithm,whale optimization algorithm(WOA) has unique mechanism and simple parameters,with certain advantages,such as high search precision and strong generalization *** the shortcomings of whale optimization algorithm:slow convergence speed,and easily getting fallen into local optimum,this paper proposes an adaptive evolutionary optimization algorithm whales(Adaptive elite strategy whale optimization algorithm,AWOA),a method of searching for food and spiral update location introduced adaptive adjusting weighting function,abundant diversity of population while strengthening local optimization ability;Adaptive differential variation disturbance is introduced in the contraction enveloping stage to provide the searching power in the later stage and avoid falling into the local *** simulation results show that the improved adaptive evolutionary whale algorithm enriches the diversity of the search population in the early stage and enhances the search power in the later *** proposed algorithm has advantages including:fast convergence speed,high optimization precision and stronger generalization ability.

关键词： Meritocratic strategy Adaptive Test function whale optimization algorithm Simulation test Weight function Differential variation Global optimization

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TENT CHAOS AND NONLINEAR CONVERGENCE FACTOR whale optimization algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2021年第2期17卷 687-700页

作者： Zhang, Huanlong Pan, Youmei Zhang, Jie Dai, Kejie Feng, Yuxing Zhengzhou Univ Light Ind Sch Elect & Informat Engn 5 Dongfeng Rd Zhengzhou 450000 Peoples R China Pingdingshan Coll Elect & Mech Engn Southern Sect Weilai Rd Pingdingshan 467000 Peoples R China

To solve the problems of slow convergence speed and difficulty in balancing exploration and development in whale optimization algorithm (WOA), a TWOA algorithm integrating Tent chaos and nonlinear convergence factors is proposed. First, the whale population in WOA algorithm uses random walk, which causes the uneven distribution of whale individuals in the exploration stage, and by adding the Tent chaotic mapping strategy and taking advantage of the ergodicity of Tent chaotic mapping, the whale population is distributed more evenly in the random walk stage, which enhances the global search ability of the algorithm. Second, because WOA algorithm adopts linear convergence factor, it cannot effectively solve the balance problem of balancing exploration and development, and by changing linear convergence factor into nonlinear convergence factor, the optimization accuracy of the algorithm can be improved. Finally, by using 10 standard test functions, the problems of unimodal functions and multimodal functions were tested. The results show that the whale optimization algorithm with Tent chaotic map and nonlinear convergence factor is superior to the original algorithm in both mean and standard deviation. From the convergence curve, it can be observed that the convergence speed and convergence accuracy are obviously improved.

关键词： whale optimization algorithm Tent chaotic map Nonlinear convergence factor Intelligent optimization algorithm Global optimization

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WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks

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COMPUTER NETWORKS 2024年 254卷

作者： Kumar, Navneet Singh, Karan Lloret, Jaime Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India Univ Politecn Valencia Inst Invest Gest Integrada Zonas Costeras Camino Vera S-N Valencia 46022 Spain

Wireless Sensor Networks (WSNs) are vital for collecting data from remote environments. Nevertheless, the limited energy resources of sensor nodes render energy-efficient routing a critical concern for the successful operation of WSNs. To address these concerns, clustering, and routing are essential tasks in WSNs;clustering aims to organize sensor nodes into groups or clusters to minimize energy usage and prolong the network's lifespan. On the other hand, routing involves determining the optimum paths for transmitting data from the source nodes to the destination nodes. Nonetheless, it has been established that the current energy-efficient routing problem is an NP-hard, requiring a trade-off between energy and overall network performance. In this paper, we proposed a Hybrid whale-Ant optimization algorithm (WAOA) for energy-efficient routing in WSNs. The proposed WAOA utilizes the whale optimization algorithm (WOA) to find the suitable cluster head in the predefined search space, while the Ant Colony optimization (ACO) searches the optimal route from the source cluster sensors to the cluster head within its predefined space. Linear programming construction is employed to formulate optimization problems for cluster head selection and search for the optimal route. The performance analysis demonstrates that the proposed WAOA performs better than MOORP, MMABC, and AZEBR by 5.78 %,16.11 %, and 18.52 %, respectively, in terms of network lifetime.

关键词： Wireless sensor networks whale optimization algorithm Ant colony optimization Energy-efficient routing

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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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