The improved grasshopper optimization algorithm and its applications

Qin, Ping; Hu, Hongping; Yang, Zheng-Min

doi:10.1038/s41598-021-03049-6

Cited by 21 publications

(9 citation statements)

References 36 publications

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“…Different from particle swarm optimization (PSO), the grasshopper optimization algorithm (GOA) is a group-based nature-inspired calculation method. The updating of the search agent positions is affected by the position of each search agent, allowing the GOA to effectively achieve gradual and balanced exploration of the search space when searching for the optimal results [29,30]. Therefore, the GOA is a suitable method for solving the proposed UFLS model.…”

Section: Solution Methods For the Ufls Modelmentioning

confidence: 99%

Underfrequency Load Shedding Strategy With an Adaptive Variation Capability for Multi-Microgrids

Chen

Zhou

et al. 2023

IEEE Access

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Multi-microgrids (MMGs) suffer from power shortages due to the loss of utility grid support when an unintentional transition occurs. This imposes a transient shock on the system voltage and frequency. To maintain the frequency stability and power balance of an islanded MMG, this paper presents an underfrequency load shedding (UFLS) strategy with adaptive variation. A comprehensive load evaluation method based on a composite least squares support vector machine (CLS-SVM) is proposed to ensure uninterrupted power for critical loads. This method considers the comprehensive evaluation influence factors (CEIFs) of loads. Then, a least squares support vector machine (LS-SVM) provides the load shedding determination factors, transforming the problem of determining critical loads into a 0-1 planning problem. A method with adaptive variation is proposed to solve the UFLS model. The effectiveness of the proposed strategy is verified for an MMG model based on a modified IEEE 33-bus system. The test results show that: 1) the average accuracy of the proposed method is 21.05% higher than that based on LS-SVM; 2) compared with UFLS strategies based on the load level alone and on an intelligent algorithm, the frequency fluctuation range of the proposed strategy is 12.50% and 19.23% lower, respectively, and the frequency recovery time is 3.90% and 5.73% shorter, respectively; 3) compared with PSO, GOA and GA, the standard deviation of the iterative mean of the proposed algorithm decreases by 36.22%, 53.42%, and 34.00%, respectively. The proposed strategy can reduce the frequency fluctuation range and frequency recovery time while maintaining strong adaptability.INDEX TERMS Adaptive solution method, comprehensive evaluation, load shedding, microgrid, power shortage.

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Section: Solution Methods For the Ufls Modelmentioning

confidence: 99%

Underfrequency Load Shedding Strategy With an Adaptive Variation Capability for Multi-Microgrids

Chen

Zhou

et al. 2023

IEEE Access

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“…Recent years have witnessed the potential use of GOA in the field of science and engineering like multi-objective test problems, image processing, scheduling, machine learning, and motion tracking [2528]. However, several variants of GOA approaches are suggested to overcome the gaps in GOA [29,30]. The combination of Bees algorithm (BA) and GOA has been proposed to address the deployment problem in WSN [31].…”

Section: Related Workmentioning

confidence: 99%

“…In traditional GOA, it can be observed that the effect of gravity force while updating the position of the grasshopper is not considered. In [30], the authors introduced a gravity force while updating the position of each grasshopper in the traditional GOA. The following arithmetic model is used to show the updated position of the grasshoppers.…”

Section: (∑ (| |) )mentioning

confidence: 99%

A modified grasshopper optimization algorithm based on levy flight for cluster head selection in wireless sensor networks

2022

IJATEE

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A wireless sensor network (WSN) is made up of numerous wireless sensors that may be used for a variety of purposes, including security surveillance, terror threat detection, health monitoring, and environmental monitoring. In these applications, thousands of wireless sensors are deployed in remote environments to operate autonomously. The wireless sensor nodes are largely confined by limited energy supply, memory, and bandwidth. Major issues in designing WSNs are energy consumption and maximizing the network lifetime. Low energy adaptive clustering hierarchy (LEACH) is a reliable routing protocol that utilizes the cluster head rotation strategy to uniformly allocate the energy burden among all the available nodes. LEACH maintains the steadiness of the energy consumed by the nodes. However, LEACH protocol does not guarantee the uniform allotment of the cluster heads (CHs), and eventually reduces the network lifetime. A clustering protocol offers a potential solution that guarantees energy saving of nodes and increases the lifetime of the network by organizing nodes into clusters to reduce the transmission distance between sensor nodes and the base station (BS). The traditional grasshopper optimization algorithm (GOA) has a set of shortcomings such as the ease with which it can fall into local optimum and the slow convergence speed. To address these drawbacks, a modified grasshopper optimization algorithm (MGOA) was proposed based on an energy efficient routing protocol in LEACH. It is called as modified grasshopper optimization algorithm, low energy adaptive clustering hierarchy (MGOA-LEACH). It has been proposed to minimize the energy consumption and maximize the network lifetime in WSNs. The levy flight (LF) strategy was used to increase the randomness of the search agent's movement, allowing GOA to have a greater global exploration capability. The evaluation results show that the suggested algorithm provides lower energy consumption and better life time compared to competitive clustering algorithms like LEACH, genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), GOA.

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“…Yi Feng et al 20 (2020) introduced Dynamic Opposite Learning assisted GOA for the Flexible Job Scheduling Problem. Qin, P et al 21 (2021) have successfully applied improved GOA to optimise the parameters of the BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province.…”

Section: Introductionmentioning

confidence: 99%

Enhancing grasshopper optimization algorithm (GOA) with levy flight for engineering applications

Wang

2023

Sci Rep

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The grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm proposed in 2017 mimics the biological behavior of grasshopper swarms seeking food sources in nature for solving optimization problems. Nonetheless, some shortcomings exist in the origin GOA, and GOA global search ability is more or less insufficient and precision also needs to be further improved. Although there are many different GOA variants in the literature, the problem of inefficient and rough precision has still emerged in GOA variants. Aiming at these deficiencies, this paper develops an improved version of GOA with Levy Flight mechanism called LFGOA to alleviate the shortcomings of the origin GOA. The LFGOA algorithm achieved a more suitable balance between exploitation and exploration during searching for the most promising region. The performance of LFGOA is tested using 23 mathematical benchmark functions in comparison with the eight well-known meta-heuristic algorithms and seven real-world engineering problems. The statistical analysis and experimental results show the efficiency of LFGOA. According to obtained results, it is possible to say that the LFGOA algorithm can be a potential alternative in the solution of meta-heuristic optimization problems as it has high exploration and exploitation capabilities.

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The improved grasshopper optimization algorithm and its applications

Cited by 21 publications

References 36 publications

Underfrequency Load Shedding Strategy With an Adaptive Variation Capability for Multi-Microgrids

Underfrequency Load Shedding Strategy With an Adaptive Variation Capability for Multi-Microgrids

A modified grasshopper optimization algorithm based on levy flight for cluster head selection in wireless sensor networks

Enhancing grasshopper optimization algorithm (GOA) with levy flight for engineering applications

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