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35th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Feng, Yi Liu, Mengru Yang, Zhile Feng, Wei Yang, Dongsheng Dalian Univ Technol Dalian Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Northeastern Univ Shenzhen Peoples R China

ISBN: (纸本)9781728176840

Job shop scheduling problem (JSP) is one of the most difficult scheduling problems, and flexible job shop scheduling problem (FJSP) is an extension of the classical JSP. In FJSP, a machine for each process should be selected from a given set, which introduces another decision element in the job path that makes FJSP more difficult than traditional JSP. In this paper, a recent proposed intelligence algorithm named grasshopper optimization algorithm (GOA) is used to solve FJSP. Numerical results with comparisons of other classic algorithm counterparts show that GOA has stronger global searching ability and performs better when solving FJSP.

关键词： grasshopper optimization algorithm Flexible job shop scheduling Makespan Global optimization

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Combining the Wolf Pack algorithm with the Curve-adaptive grasshopper optimization algorithm 5

Combining the Wolf Pack Algorithm with the Curve-adaptive Gr...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Yao, Lejia Hu, Haidong Jiang, Feng Fan, Pu Inner Mongolia Univ Sci & Technol Sch Digital Intelligence Ind Baotou Inner Mongolia Peoples R China

ISBN: (纸本)9798350386783;9798350386776

To address the issues of the grasshopper optimization algorithm (GOA) falling into local optima and achieving low optimization precision, this paper proposes a hybrid algorithm called Combining the Wolf Pack algorithm (WPA) with the Curve-adaptive grasshopper optimization algorithm (WCGOA). Firstly, the population is initialized using Logistic mapping to ensure an optimal initial population, thereby enhancing population diversity. Secondly, the linear weights are replaced with curve-adaptive weights to improve search speed and precision. Thirdly, by incorporating the hierarchical hunting concept from the Wolf Pack algorithm, individual awareness of grasshoppers is developed to enhance global search capabilities. Subsequently, Cauchy mutation is applied to the best individuals to strengthen their ability to escape local optima. Finally, GOA is compared with four other classical algorithms as benchmarks for WCGOA. Statistical analysis and Wilcoxon rank-sum tests conducted on 11 commonly used benchmark functions demonstrate that WCGOA significantly outperforms other algorithms in terms of convergence precision, convergence speed, stability, and optimization success rate.

关键词： grasshopper optimization algorithm Wolf pack algorithm curve adaptation Cauchy mutation

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Finite Impulse Response Filter Design using grasshopper optimization algorithm and Implementation on FPGA

Finite Impulse Response Filter Design using Grasshopper Opti...

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IEEE Applied Signal Processing Conference (ASPCON)

作者： Dutta, Tanay Aich, Raina Modak Dhabal, Supriya Venkateswaran, Palaniandavar Jadavpur Univ Dept Elect & Telecommun Engn Kolkata India Techno Int Batanagar Dept ECE Maheshtala W Bengal India Netaji Subhash Engn Coll Dept ECE Kolkata India

ISBN: (纸本)9781728168821

This paper establishes an efficient design style of Type-1 FIR filter using grasshopper optimization algorithm (GOA) and its implementation on FPGA. GOA is a newly developed population-based meta-heuristic optimization algorithm motivated by the swarming activities of grasshoppers. GOA with its better problem-solving capability has revolutionized the contemporary era. Minimization of faults in the optimum response and the estimated response in digital filters are the main feature of meta-heuristic optimization algorithms. Therefore, this algorithm has been broadly accepted in various fields due to its high efficiency in solving problem sets. Further, to emphasis the usefulness of the suggested algorithm, the simulated outcomes have been compared with the results of the well-established algorithms such as Parks McClellan (PM) algorithm and Sine Cosine algorithm (SCA) and it has been found that grasshopper optimization algorithm (GOA) outperform PM and SCA in terms of stop-band attenuation and pass-band ripple. Additionally, this paper also explains the hardware function of the concept being contemplated within the FPGA platform.

关键词： Finite Impulse Response Filter grasshopper optimization algorithm Parks McClellan algorithm Sine Cosine algorithm Field Programmable Gate Array

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A Modified grasshopper optimization algorithm 11

A Modified Grasshopper Optimization Algorithm

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11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Gao, Man Xu, Hui Su, Jun Yan, Lingyu Hubei Univ Technol 28 Nanli Rd Wuhan 430068 Peoples R China

ISBN: (纸本)9781665426053

In order to solve the problems of grasshopper optimization algorithm (GOA) in the optimization process, such as easily falling into the local optimum and the slow convergence speed, a Modified grasshopper optimization algorithm (MGOA) is proposed. And the proposed MGOA improves the original fixed parameters to linear decreasing parameters in order to avoid the original algorithm from falling into the local optimum problem. At the same time, weighting calculation is introduced to improve the optimization accuracy of the MGOA algorithm. Additionally, the proposed MGOA is combined with the Simulated Annealing (SA) to improve the convergence speed of the original GOA algorithm. Both the unimodal test function and the multimodal test function are respectively used for simulation experiments, and experimental results show that, compared with the other five swarm intelligent algorithms, the proposed MGOA has significant advantages in aspects of accuracy, stability and convergence speed.

关键词： grasshopper optimization algorithm weighting calculation Simulated Annealing

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Design optimization of concrete gravity dams using grasshopper optimization algorithm

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2024年第12期9卷 1-22页

作者： Abbasi, Salim Seifollahi, Mehran Farzaneh, Shahin Daneshfaraz, Rasoul Sume, Veli Sadraei, Naghi Abraham, John Univ Mohaghegh Ardabili Dept Civil Engn Ardebil Iran Univ Tabriz Dept Civil Engn Tabriz Iran Univ Maragheh Dept Civil Engn Maragheh Iran Recep Tayyip Erdogan Univ Dept Civil Engn Rize Turkiye Islamic Azad Univ Dept Civil Engn Ardebil Iran Univ St Thomas Sch Engn St Paul MN USA

The efficiency and sustainability of dams can be significantly improved by structural optimization during the design process. This study aims to optimize geometric dimensions and minimize the concrete volume of three benchmark Concrete Gravity Dams (CGDs) including Pine-Flat, Middle-Fork, and Richard dams subjected to seismic excitations by applying the grasshopper optimization algorithm (GOA). Employing GOA effectively reduces the concrete volume, achieving reductions of 30.88% (399 m3), 12.5% (1705 m3), and 28.09% (241 m3) for Richard, Pine-Flat, and Middle-Fork dams, respectively. These findings highlight that Richard Dam exhibits the maximum optimization value while Pine-Flat Dam demonstrates minimum optimization value and greatest volume reduction due to its initially larger volume. The optimized dams reduce concrete volume, effectively meeting stability requirements and enhancing stability against applied forces. The Safety Factor against Overturning (SOF) improves from 1.62 to 2.23, and the Safety Factor against Sliding (SFF) increases from 1.31 to 1.48. As a result, the dams are more stable and secure against overturning and sliding. The study underscores the efficiency of the GOA in optimizing CGD design process, offering significant implications for cost savings and resource efficiency in dam construction. This study emphasizes the robustness of GOA as a powerful meta-heuristic algorithm and its high potential for application in various optimization scenarios in structural engineering, and it recommends GOA as a highly effective tool for the optimal design of CGDs.

关键词： Seismic force Concrete gravity dam Geometric dimensions grasshopper optimization algorithm

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GPS/INS Integrated Navigation Based on grasshopper optimization algorithm 5th

GPS/INS Integrated Navigation Based on Grasshopper Optimizat...

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5th IFAC Symposium on Telematics Applications (TA)

作者： Meng, Hao Han, Yu Xun, Yuting Chen, Jian Du, Nannan Cao, Yi Wang, Guangqi Yi, Xiangwei Zheng, Yongjun China Agr Univ Coll Engn Beijing 100083 Peoples R China China Agr Univ Coll Water Resources & Civil Engn Beijing 100083 Peoples R China Beijing Key Lab Optimized Design Modern Agr Equip Beijing Peoples R China Wuhan Univ Elect Informat Sch Wuhan 430072 Hubei Peoples R China

In GPS/INS integrated navigation, Kalman filter is usually used for data fusion between GPS and INS. The filtering algorithm of integrated navigation is related to whether the advantages of each sensor in the integrated navigation can be utilized, the navigation accuracy is improved, the reliable working time of the navigation system is improved and the navigation requirement are met. In this paper, we proposed an approach based on the grasshopper optimization algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarm of grasshoppers. The goal of the proposed approach is to optimize the parameters of the Kalman filter. The Kalman filter method is improved by grasshopper optimization algorithm, which improves the accuracy of integrated navigation and reduces the errors caused by system noise and measurement noise. For verification, the proposed approach is compared with particle swarm optimization. The simulation experiment results show that the proposed approach has a better effect. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.

关键词： grasshopper optimization algorithm GNSS/INS integrated navigation Kalman filter longitudinal and latitude errors simulation experiment

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RESEARCHING ON FLEXIBLE JOB-SHOP SCHEDULING PROBLEM BASED ON COMPOSITE grasshopper optimization algorithm 17

RESEARCHING ON FLEXIBLE JOB-SHOP SCHEDULING PROBLEM BASED ON...

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17th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Ding Zhe Wu Chunwang Zhao Jun Li Binyong Li Liangxiao Xian Mingzhe Liao Junqing Chengdu Univ Informat Technol Sch Cybersecur Chengdu 610225 Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9780738142593

In the practice, the essence of most scheduling problem belongs to flexible Job-Shop Scheduling problem, denoted as FJSP. As a consequence, researching for flexible job-shop scheduling has become hot spot in academy. It is difficult to directly apply traditional scheduling problems to solve FJSP because of complexity of FJSP. So, in this paper, based on grasshopper optimization algorithm, a novel algorithm is proposed and denoted as CGOA. CGOA firstly applies technology of chaotic sequence to initialize grasshopper population. And then CGOA utilizes the technology of quasi opposition-based learning, splitting strategy and Cauchy mutation to balance the ability of global search and local search. In the end, two public datasets are applied to verify the efficiency of CGOA. The result show that the efficiency of CGOA is better than traditional scheduling algorithms.

关键词： Flexible job-shop scheduling grasshopper optimization algorithm Quasi opposition-based learning Cauchy mutation

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A Dynamic Weight grasshopper optimization algorithm with Random Jumping

A Dynamic Weight Grasshopper Optimization Algorithm with Ran...

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International Conference on Computer, Communication and Computational Sciences (IC4S)

作者： Zhao, Ran Ni, Hong Feng, Hangwei Zhu, Xiaoyong Chinese Acad Sci Inst Acoust Natl Network New Media Engn Res Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9789811368615;9789811368608

grasshopper optimization algorithm (GOA) is a novel meta-heuristic algorithm for solving single-objective numeric optimization problems. While it has a simple principle and it is easy to implement, grasshopper optimization algorithm performs badly in some aspects. GOA cannot make full utilization of every iteration, and it is not good at getting rid of local optima. To solve these problems and improve the performance of GOA, this paper proposed an improved grasshopper optimization algorithm based on dynamic weight mechanism and random jumping strategy (DJGOA). The dynamic weight mechanism promoted the utilization of the iterations of the algorithm. The random jumping strategy was introduced to help the algorithm jumping out of the local optima. Several experiments relating to 13 benchmark functions and 4 algorithms were conducted to demonstrate the performance of DJGOA. The results of the experiments demonstrated that DJGOA performed better than GOA and the other algorithms.

关键词： grasshopper optimization algorithm Dynamic weight Random jumping Global optimization problem

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Implementation of grasshopper optimization algorithm based cascaded fuzzy PD-PI controller for frequency stability in a multi-area power system

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2020年第2期23卷 335-345页

作者： Tripathy, Debasis Behera, Sadasiva Choudhury, N. B. Dev Natl Inst Technol Dept Elect Engn Silchar 788010 Assam India

This article proposed a grasshopper optimization algorithm (GOA) based cascade controller i.e. fuzzy-PD with PI controller (FPD-PI) for the study of load frequency control. A three unequal-area interconnected power system employed with thermal units has been considered for the present study. Initially, the performance of the GOA technique is verified over the Ant-lion optimization technique with integral square error (ISE) as an objective function. Finally, the supremacy of the proposed FPD-PI controller is verified over other benchmark controllers namely PID and fuzzy-PID with GOA, after ensuring the superiority of integral time multiplied absolute error (ITAE) over ISE.

关键词： Cascade control Fuzzy logic control grasshopper optimization algorithm Load frequency control Multi area power system

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Visual Tracking with Levy Flight grasshopper optimization algorithm 2nd

Visual Tracking with Levy Flight Grasshopper Optimization Al...

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2nd Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Zhang, Huanlong Gao, Zeng Zhang, Jie Yang, Guanglu Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou Peoples R China China Tobacco Henan Ind Co Ltd Nanyang Cigarette Factory Nanyang Peoples R China

ISBN: (纸本)9783030316532;9783030316549

grasshopper optimization algorithm (GOA) is a new meta-heuristic optimization algorithm that it simulates behavior of grasshopper swarms in nature. In this paper, a tracking framework called improved levy flight grasshopper optimization algorithm (LGOA) tracker is proposed. The levy flight can increase the diversity of population, prevent premature convergence and enhance the capability of jumping out of local optimal optima, thus improving the tracking accuracy. In addition, GOA has been applied to visual tracking for the first time as far as we know. Finally, compared with other optimization-based trackers, experimental results show that our tracker has obvious advantages.

关键词： Visual tracking grasshopper optimization algorithm Levy flight Local optimal

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