Discrete Improved Grey Wolf Optimizer for Community Detection

Nadimi-Shahraki, Mohammad H.; Moeini, Ebrahim; Taghian, Shokooh; Mirjalili, Seyedali

doi:10.1007/s42235-023-00387-1

Cited by 15 publications

(3 citation statements)

References 125 publications

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“…Previous research indicated that AGDPSO can effectively solve the scheduling problem of AVS/RS. Three algorithms of the modified discrete shuffled frog leaping algorithm (MDSFLA) [49], discrete version of the improved grey wolf optimizer (DI-GWO) [50], and genetic Tabu search algorithm with neighborhood clipping (GTS_NC) [51] were employed to solve the same problems, and their performances were compared with the AGDPSO. Three scenarios involving different numbers of RGVs, various operation times, and racking configurations in AVS/RS were considered to evaluate the algorithm's performance.…”

Section: Performance Analysismentioning

confidence: 99%

Scheduling Optimization of Compound Operations in Autonomous Vehicle Storage and Retrieval System

Xu,

Lu,

Zhan

2024

Symmetry

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The increasing demand for storing various types of goods has led to a raise in the need for storage capacity in warehousing systems. Autonomous vehicle storage and retrieval systems (AVS/RSs) offer high flexibility by allowing different configurations to meet different storage requirements. The system mainly completes operations through elevators and multiple rail-guided vehicles (RGVs). This paper focuses on the scheduling optimization of compound operations in the AVS/RS to improve system performance. Compound operations involve the coordinated execution of both single-command and double-command operations. A mathematical model with compound operations was proposed and effectively decomposed into a horizontal component for RGVs and a vertical counterpart for the elevator, which can represent the operations of one elevator cooperating with multiple RGVs. The goal of this model was to minimize the makespan for compound operations and to determine the optimal operation sequence and path for RGVs. An improved discrete particle swarm optimization (DPSO) algorithm called AGDPSO was proposed to solve the model. The algorithm combines DPSO and a genetic algorithm in an adaptive manner to prevent the algorithm from falling into local optima and relying solely on the initial solution. Through rigorous optimization, optimal parameters for the algorithm were identified. When assessing the performance of our improved algorithm against various counterparts, considering different task durations and racking configurations, our results showed that AGDPSO outperformed the alternatives, proving its effectiveness in enhancing system efficiency for the model. The findings of this study not only contribute to the optimization of AVS/RS but also offer valuable insights for designing more efficient warehouses. By streamlining scheduling, improving operations, and leveraging advanced optimization techniques, we can create a more robust and effective storage and retrieval system.

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Section: Performance Analysismentioning

confidence: 99%

Scheduling Optimization of Compound Operations in Autonomous Vehicle Storage and Retrieval System

Xu,

Lu,

Zhan

2024

Symmetry

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“…Ou et al [21] proposed a method that combines the clone selection algorithm and GWO and uses a nonlinear function to adjust the convergence factor, thus overcoming the problems of slow convergence, low accuracy of the single peak function, and local optimum in the standard GWO. Nadimi-Shahraki et al [22] published a discrete improved GWO that uses local and dimension-based learning amount hunting search to enhance the algorithm's performance. Also, the AGWO [23] algorithm uses an adaptive GWO to solve the problem by automatically adjusting the exploration and exploitation parameters based on the fitness history.…”

Section: Related Workmentioning

confidence: 99%

Improved Grey Wolf Optimization Algorithm Based on Hyperbolic Tangent Inertia Weight

Lin

2023

IEEE Access

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The Grey Wolf Optimization Algorithm (GWO) is an algorithm that replicates the leadership and foraging mechanisms of the natural grey wolf, which excels at solving problems in a variety of domains. However, the algorithm tends to converge on local optimal and has a slow convergence rate. This paper proposes an enhanced Grey Wolf optimization algorithm (HTGWO) based on hyperbolic tangent inertia weights to solve this problem. HTGWO employs inertia weight based on hyperbolic tangent functions to balance GWO's global and local search capabilities. HTGWO has a quicker convergence rate and more accurate solutions than GWO. Five classical test functions construct comparative experiments between HWGWO and five classical intelligent optimization algorithms. The comparison results indicate that HWGWO has superior convergence speed, solution precision, and stability compared to the other five classical intelligent optimization algorithms. Moreover, experimental evidence suggests that HWGWO is more effective at solving multi-modal than uni-modal functions.

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“…MOMs are becoming more common in several applications due to their advantages, such as being easy to implement, not relying on gradient information, and being able to avoid strikes in local optima due to exploration and exploitation features. Some of the popular MOMs include particle swarm optimization (PSO) [4], binary aquila optimizer (BAO) [5], genetic algorithm (GA) [6], differential evolution (DE) [7], ant colony optimization (ACO) [8], grey wolf optimization (GWO) [9], bat algorithm (BA) [10], and fruity fly algorithm (FA) [11]. However, optimal solutions may not be guaranteed because more exploration and exploitation searches are needed, especially when dealing with complicated optimization tasks.…”

Section: Introductionmentioning

confidence: 99%

On the Cryptanalysis of a Simplified AES Using a Hybrid Binary Grey Wolf Optimization

Rizk-Allah,

Abdulkader,

Elatif

et al. 2023

Mathematics

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Cryptosystem cryptanalysis is regarded as an NP-Hard task in modern cryptography. Due to block ciphers that are part of a modern cipher and have nonlinearity and low autocorrelation in their structure, traditional techniques and brute-force attacks suffer from breaking the key presented in traditional techniques, and brute-force attacks against modern cipher S-AES (simplified-advanced encryption standard) are complex. Thus, developing robust and reliable optimization with high searching capability is essential. Motivated by this, this paper attempts to present a novel binary hybridization algorithm based on the mathematical procedures of the grey wolf optimizer (GWO) and particle swarm optimization (PSO), named BPSOGWO, to deal with the cryptanalysis of (S-AES). The proposed BPSOGWO employs a known plaintext attack that requires only one pair of plaintext–ciphertext pairs instead of other strategies that require more pairs (i.e., it reduces the number of messages needed in an attack, and secret information such as plaintext-ciphertext pairs cannot be obtained easily). The comprehensive and statistical results indicate that the BPSOGWO is more accurate and provides superior results compared to other peers, where it improved the cryptanalysis accurateness of S-AES by 82.5%, 84.79%, and 79.6% compared to PSO, GA, and ACO, respectively. Furthermore, the proposed BPSOGWO retrieves the optimal key with a significant reduction in search space compared to a brute-force attack. Experiments show that combining the suggested fitness function with HPSOGWO resulted in a 109-fold reduction in the search space. In cryptanalysis, this is a significant factor. The results prove that BPSOGWO is a promising and effective alternative to attack the key employed in the S-AES cipher.

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Discrete Improved Grey Wolf Optimizer for Community Detection

Cited by 15 publications

References 125 publications

Scheduling Optimization of Compound Operations in Autonomous Vehicle Storage and Retrieval System

Scheduling Optimization of Compound Operations in Autonomous Vehicle Storage and Retrieval System

Improved Grey Wolf Optimization Algorithm Based on Hyperbolic Tangent Inertia Weight

On the Cryptanalysis of a Simplified AES Using a Hybrid Binary Grey Wolf Optimization

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