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13th International Symposium on Computational Intelligence and Design (ISCID)

作者： Shi, Jiarui Yu, Jia Lee, Chiyan Todo, Yuki Gao, Shangce Univ Toyama Fac Engn Toyama 9308555 Japan Kanazawa Univ Fac Elect Informat & Commun Engn Kakuma Machi Kanazawa Ishikawa 9201192 Japan

ISBN: (纸本)9781728184463

In recent years, mixed-element heuristics have received increasing attention in optimization algorithms. The spherical search (SS)-a swarm-based meta-heuristic algorithm is used for solving global optimization problems with nonlinear constraints. The whale optimization algorithm (WOA), inspired by the bubble net hunting strategy, mimics the social behavior of humpback whales, whereas the heuristics method usually fall into a so-called "local optima trap". We proposed a hybrid algorithm based on the whale algorithm and the spherical search optimization hybrid algorithm, so that the two optimization strategies are merged into the presented algorithm which enables both algorithms to work in a co-evolutionary way. Experiment results show that the proposed hybrid algorithm can find the better solutions than some other new algorithms in term of convergence speed and solution accuracy.

关键词： sphere search whale optimization algorithm optimization meta-heuristics computational intelligence

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Autonomous Mission Planning for Multi-Agile Earth. Observation Satellites Using whale optimization algorithm

Autonomous Mission Planning for Multi-Agile Earth. Observati...

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Chinese Automation Congress (CAC)

作者： Gao, Xinzhou Zhang, Haibo Yu, Tianhang Ma, Guangfu Gao, Duozhi Harbin Inst Technol Dept Control Sci & Engn Harbin Peoples R China Beijing Inst Control Engn Beijing Peoples R China Southwest Univ Dept Elect Informat Engn Chongqing Peoples R China

ISBN: (纸本)9781728176871

In this paper, the whale optimization algorithm is used to realize the mission planning of Multi-Agile Earth Observation Satellites for multiple target points. First of all, in the face of the observation problem of multi-satellite multi-target points, the fitness function, which satisfies the constraints faced by satellites when observing target points, is proposed in this paper. Secondly, in order to solve the task allocation problem of Multi-satellite to Multi-target points, a task allocation method based on the maximal time window is proposed in this paper. Next, the new whale optimization algorithms that have emerged in recent years is introduced in this paper. Considering that there has been no research on applying this algorithm to the mission planning problem so far, this paper proposes a method to map its iterative formula from the real number domain to the observation sequence domain based on the iterative characteristics of the whale optimization algorithm. This method enables the whale optimization algorithm to realize the iteration of the satellite observation sequence. Finally, the effectiveness of the whale optimization algorithm in sohing the multi-satellite multi-target points planning problem is verified by simulation experiments.

关键词： mission planning and allocation agile earth observation satellite whale optimization algorithm

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Temperature dependent optimal power flow using chaotic whale optimization algorithm

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EXPERT SYSTEMS 2021年第4期38卷 e12685-e12685页

作者： Prasad, Dharmbir Mukherjee, Aparajita Mukherjee, Vivekananda Asansol Engn Coll Dept Elect Engn Asansol W Bengal India Meghnad Saha Inst Technol Dept Elect Engn Kolkata India Indian Sch Mines Dept Elect Engn Indian Inst Technol Dhanbad Bihar India

This work presents a novel, nature inspired evolutionary based approach, the chaotic whale optimization algorithm, to solve a temperature dependent optimal power flow problem of a power system. whale optimization is inspired by the bubble-net hunting strategy of the humpback whales;logistic chaotic maps are used to improve its performance. whale optimization and our proposal are evaluated on three test systems namely, the IEEE 30-bus test power system, the 2383-bus Winter Peak Polish system and the 2736-bus Summer Peak Polish system to give a solution to the temperature dependant optimal power flow of the power systems where control of generator bus voltages, transformer tap ratios and reactive power sources are involved. Minimization of total fuel cost is considered here as the objective function for this problem. The superiority and the effectiveness of the proposed algorithm technique have been exhibited in comparison to the other evolutionary optimization techniques identified in the recent literature.

关键词： chaotic maps optimal power flow (OPF) optimization temperature dependent OPF whale optimization algorithm

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Design of Low-Cost Fuzzy Controllers with Reduced Parametric Sensitivity Based on whale optimization algorithm

Design of Low-Cost Fuzzy Controllers with Reduced Parametric...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： David, Radu-Codrut Precup, Radu-Emil Preitl, Stefan Szedlak-Stinean, Alexandra-Iulia Roman, Raul-Cristian Petriu, Emil M. Politehn Univ Timisoara Dept Automat & Appl Informat Timisoara Romania Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169323

The aim of this paper is to demonstrate the feasibility of whale optimization algorithm (WOA) in solving complex control design and tuning problems of fuzzy control systems (FCSs) with a reduced parametric sensitivity. The sensitivity analysis of these FCSs implies the use of sensitivity models defined with respect to the parametric variations of the processes. The main goal is solving the optimization problems defined for servo system processes controlled by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs), through minimization of objective functions that include the output sensitivity functions. A design method is proposed in this regard, and it is validated through experimental results using a laboratory nonlinear servo system.

关键词： experimental validation fuzzy control system parametric sensitivity whale optimization algorithm

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Cyclic Path Planning of Hyper-redundant Manipulator using whale optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第8期12卷 677-686页

作者： Machmudah, Affiani Parman, Setyamartana Abbasi, Aijaz Solihin, Mahmud Iwan Abd Manan, Teh Sabariah Beddu, Salmia Ahmad, Amiruddin Rasdi, Nadiah Wan Univ Airlangga Fac Adv Technol & Multidisciplinary Ind Engn Kampus C Jalan Mulyorejo Surabaya Indonesia Univ Teknikal Malaysia Melaka Fak Teknol Kejuruteraan Mekanikal & Pembuatan Ayer Keroh 75450 Melaka Indonesia Quaid E Awam Univ Engn Sci & Technol Mech Engn Nawabshah Pakistan UCSI Univ Fac Engn Technol & Built Environm Kuala Lumpur 56000 Malaysia Univ Malaysia Terengganu Inst Trop Biodivers & Sustainable Dev Kuala Nerus 21030 Terengganu Daru Malaysia Univ Tenaga Nas Dept Civil Engn Jalan Ikram Uniten Kajang 43000 Selangor Darul Malaysia Univ Malaysia Terengganu Fac Sci & Marine Environm Kuala Nerus 21030 Terengganu Daru Malaysia Univ Malaysia Terengganu Fac Fisheries & Food Sci Kuala Nerus 21030 Terengganu Daru Malaysia

This paper develops a path planning algorithm of hyper-redundant manipulators to achieve a cyclic property. The basic idea is based on a geometrical analysis of a 3-link planar series manipulator in which there is an orientation angle boundary of a prescribed path. To achieve the repetitive behavior, for hyper-redundant manipulators consisting of 3-link components, an additional path is chosen in such away so that it is a repetitive curve which has the same curve frequency with the prescribed end-effector path. To solve the redundancy resolution, meta-heuristic optimizations, namely Genetic algorithm (GA) and whale optimization algorithm (WOA), are applied to search optimal trajectories inside local orientation angle boundaries. Results show that using constant of the local orientation angle trajectories for the 3-link component, the cyclic properties can be achieved. The performance of the WOA shows very promising result where generally it obtains the lowest fitness value as compare with the GA. Depending on the complexity of the path planning, dividing the path into several stages via intermediate points may be necessary to achieve the good posture. The performance of the swarm based meta-heuristic optimization, namely the WOA, shows very promising result where generally it obtains the lowest fitness value as compare with the GA. Using the developed approach, not only the cyclic property is obtained but also the optimal movement of the hyper-redundant manipulator is achieved.

关键词： Hyper-redundant path planning whale optimization algorithm sustainable manufacturing

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Wearable 5-Gigahertz Wi-Fi Antenna Design Using whale optimization algorithm 14

Wearable 5-Gigahertz Wi-Fi Antenna Design Using Whale Optimi...

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14th European Conference on Antennas and Propagation (EuCAP)

作者： Boursianis, Achilles D. Koulouridis, Stavros Georgoulas, Dimitrios Goudos, Sotirios K. Aristotle Univ Thessaloniki Dept Phys Thessaloniki 54124 Greece Univ Patras Elect & Comp Engn Dept Patras Greece

ISBN: (数字)9788831299008

ISBN: (纸本)9788831299008

In this paper, we design an antenna for wearable wireless applications. The proposed antenna is a planar inverted- F antenna (PIFA) for operation at 5 GHz. The antenna design procedure is accomplished using a new nature inspired algorithm, the whale optimization algorithm. Numerical results exhibit the applicability and validity of the proposed design framework.

关键词： wearable antennas whale optimization algorithm nature-inspired algorithms printed inverted-F antenna

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Performance Analysis of whale optimization algorithm

Performance Analysis of Whale Optimization Algorithm

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7th International Conference on Communications, Signal Processing, and Systems (CSPS)

作者： Zhang, Xin Wang, Dongxue Zhang, Xiu Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin Peoples R China Tianjin Normal Univ Coll Elect & Commun Engn Tianjin Peoples R China

ISBN: (纸本)9789811365041;9789811365034

Through the research and analysis of a relatively novel natural heuristic, meta-heuristic swarm intelligence optimization algorithm, this swarm intelligence algorithm is defined as a whale optimization algorithm. The algorithm builds a mathematical model by simulating a social behavior of humpback whales. This optimization algorithm was inspired by the bubble-like net hunting phenomenon that humpback whales prey on. By analyzing the four benchmark optimization problems with or without offset and rotation, the convergence performance of the whale optimization algorithm and the ability to solve the optimization problem are proved. The performance of the whale optimization algorithm is based on the computer simulation technology. Through the convergence curve obtained from the experiment, we can see that the whale optimization algorithm performs best for the five benchmark optimization problems without rotation.

关键词： Swarm intelligence whale optimization algorithm Convergence Numerical optimization

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Research of Improved whale optimization algorithm Based on Variable Convergence Factor and Forced Global Search

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电脑学刊 2021年第5期32卷 148-160页

作者： Cheng Zhu Xu-Hua Pan Qi Chen Yong Zhang Xin-Yi Gao

optimization algorithm is new meta-heuristic optimization algorithm which mimicking the hunting behavior of humpback whales. It enjoys the advantages of simple principle, less parameters, remarkable search ability and good global convergence, also suffer many defects, such as slow convergence speed, low convergence precision and easy to fall into the local optimum. This paper analyzes the problems of original WOA, making use of Good- Point Set to generate initial population. Through variable convergence factor, the progress of search is more flexible and pertinent. At the same time, mechanism of forced global search makes the ability of jumping out local optimum is promoted in substance. Results and convergence curves of benchmark functions indicate that exploration, exploitation, local optima avoidance of algorithm in this paper are competitive with original WOA.

关键词： whale optimization algorithm good-point set nonlinear variable convergence factor meta-heuristic swarm optimization

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An Enhanced whale optimization algorithm for the Two-Dimensional Irregular Strip Packing Problem 33rd

An Enhanced Whale Optimization Algorithm for the Two-Dimensi...

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33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Liu, Qiang Huang, Zehui Zhang, Hao Wei, Lijun Guangdong Univ Technol Guangdong Prov Key Lab Comp Integrated Mfg Syst State Key Lab Precis Elect Mfg Technol & Equipmen Guangzhou 510006 Peoples R China

ISBN: (纸本)9783030557881;9783030557898

In this paper, we propose an enhanced whale optimization algorithm for the two-dimensional strip packing problem, which requires cutting a given set of polygons from a sheet with fixed-width such that the used length of the sheet is minimized. Based on the original whale swarm algorithm, this algorithm introduces strategies such as adaptive weighting factors, local perturbation, and global beat variation, which can better balance the global optimization and local optimization search capabilities of the whale algorithm. The proposed algorithm was tested using the standard test case and compared with other algorithms in the literature. The results show that the proposed algorithm can improve the best-known solution for some instances.

关键词： Irregular packing whale optimization algorithm Local search

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A modified whale optimization algorithm for parameter estimation of software reliability growth models

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JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2021年 15卷

作者： Lu, Kezhong Ma, Zongmin Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Chizhou Univ Coll Comp Sci Chizhou Peoples R China

Software reliability growth models are nonlinear in nature, so it is difficult to estimate the proper parameters. An estimation method based on a modified whale optimization algorithm in which parameters are estimated is discussed in this paper. The whale optimization algorithm is a new swarm intelligence optimization algorithm. This algorithm is not perfect enough. Based on the analysis of whale optimization algorithm, we point out the disadvantages of whale optimization algorithm, and propose a modified whale optimization algorithm algorithm from four aspects: choice regarding the dimension, exploration control, encircling prey modified, and candidate solution selection. The experimental results based on 34 benchmark functions demonstrate that the proposed modified whale optimization algorithm has better accuracy. The modified whale optimization algorithm is used to predict software reliability by predicting the faults during the software testing process using software faults' historical data. The proposed modified whale optimization algorithm shows significant advantages in handling a variety of modeling problems such as the exponential model, power model, delayed s-shaped model, and modified sigmoid model. Experimental results show that the fitting accuracy of the modified sigmoid model model is minimal on three data sets. The modified whale optimization algorithm with the modified sigmoid model can provide a better estimate of the software faults.

关键词： Software reliability growth models parameter estimation whale optimization algorithm modified sigmoid model

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