A novel quantum grasshopper optimization algorithm for feature selection

Wan, Jihong; Chen, Hongmei; Li, Tianrui; Huang, Yanyong

doi:10.1016/j.ijar.2020.08.010

Cited by 44 publications

(12 citation statements)

References 40 publications

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“…The effectiveness of NBGOA was evaluated using 20 datasets with various sizes taken from the UCI datasets repository in comparison with five well-regarded optimization techniques in the feature selection field. Simulation results revealed that BGOA [78] NBGOA [79] BGOA [67] ECGOAs [80] LMGOA [81] ECGOA [82] CGOA [83] CGOA [84] SFECGOAs [85] OLCGOA [86] ECGOAs [87] ECAGOA [88] IGOA [70] EGOA [89] PGOA [90] LGOA [91] IGOA [92] AGOA [93] MI-LFGOA [94] LGOA [95] GOA_EPD [65] DJGOA [96] DQBGOA_MR [97] Fuzzy GOA [98] GO-FLC [99] EGOA-FC [100] AGOA [69] AGOA [101] GHO [102] self-adaptive GOA [103] OGOA [104] OBLGOA [105] IGOA [106] MOGOA [75] MOGOA [76] MOGOA [66] MOGOA [107] MOGOA [108] MOGOA [109] LWSGOA [110] MGOA [111] GOFS [112] PCA-GOA [113] OGOA [114] IGOA [115] Fractional-GOA…”

Section: ) Binary Grasshopper Optimization Algorithmmentioning

confidence: 99%

“…Wang et al [97] suggested a dynamic quantum binary GOA (DQBGOA_MR) based on quantum computing concept, mutual information strategy, and rough set mechanism for tackling the feature selection problem. DQBGOA_MR was tested using twenty UCI datasets by considering three metrics such as average classification accuracy, average feature subset size, and average fitness value.…”

Section: ) Dynamic Grasshopper Optimization Algorithmmentioning

confidence: 99%

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Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

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Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

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Section: ) Binary Grasshopper Optimization Algorithmmentioning

confidence: 99%

Section: ) Dynamic Grasshopper Optimization Algorithmmentioning

confidence: 99%

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

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“…Quantum gates modify the state of qubits like rotation gate, NOT gate, Hadamard gate, etc. Rotation gate is a mutation operator that develops quanta approach, results in optimal solutions and finally identifies the global best solution [20].…”

Section: Qssar-based Route Selection Techniquementioning

confidence: 99%

Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

Maharajan¹,

Abirami²,

Pustokhina³

et al. 2021

Computers, Materials &Amp; Continua

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Wireless Sensor Networks (WSN) started gaining attention due to its wide application in the fields of data collection and information processing. The recent advancements in multimedia sensors demand the Quality of Service (QoS) be maintained up to certain standards. The restrictions and requirements in QoS management completely depend upon the nature of target application. Some of the major QoS parameters in WSN are energy efficiency, network lifetime, delay and throughput. In this scenario, clustering and routing are considered as the most effective techniques to meet the demands of QoS. Since they are treated as NP (Non-deterministic Polynomial-time) hard problem, Swarm Intelligence (SI) techniques can be implemented. The current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence (QoSCRSI) algorithm. The proposed QoSCRSI technique performs two-level clustering and proficient routing. Initially, the fuzzy is hybridized with Glowworm Swarm Optimization (GSO)based clustering (HFGSOC) technique for optimal selection of Cluster Heads (CHs). Here, Quantum Salp Swarm optimization Algorithm (QSSA)-based routing technique (QSSAR) is utilized to select the possible routes in the network. In order to evaluate the performance of the proposed QoSCRSI technique, the authors conducted extensive simulation analysis with varying node counts. The experimental outcomes, obtained from the proposed QoSCRSI technique, apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency, network lifetime, overhead, throughput, and delay.

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“…The quantum operator is a probabilistic search method based on quantum computing. Compared with the classical swarm intelligent algorithm, quantum operators can effectively improve the algorithm's global search capability and convergence speed [32]. QCEGA…”

Section: Before Mutationmentioning

confidence: 99%

Quantum Clone Elite Genetic Algorithm‐Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks

2021

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In agriculture, soil moisture wireless sensor networks (SMWSNs) are used to monitor the growth of crops for obtaining higher yields. The purpose of this paper is to improve the network efficiency of SMWSNs. Therefore, we propose a novel network efficiency evaluation mechanism which is suitable for soil moisture sensors and design a sensor target allocation model (STAM) for the actual agricultural situation. After that, a quantum clone elite genetic algorithm (QCEGA) is proposed; then, QCEGA is applied to optimize the STAM for obtaining optimal results. QCEGA uses the parallel mechanism of quantum computing to encode individuals, integrates the quantum revolving gate in quantum computing and the concept of cloning in biology to avoid the algorithm from falling into local optimum, and applies the elite strategy to speed up the convergence of the algorithm. Subsequently, the proposed algorithm is compared with simulated annealing (SA) and particle swarm optimization (PSO). Under the novel network efficiency evaluation mechanism, the simulation results demonstrate that the network efficiency based on QCEGA is higher than that of SA and PSO; what is more, QCEGA has better convergence performance. In comparison with traditional wireless sensor network efficiency evaluation approaches, our methods are more in line with the development of modern agriculture and can effectively improve the efficiency of SMWSNs, thus ensuring that crops can have a better growth condition.

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A novel quantum grasshopper optimization algorithm for feature selection

Cited by 44 publications

References 40 publications

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

Quantum Clone Elite Genetic Algorithm‐Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks

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