A novel metaheuristic optimization algorithm: the monarchy metaheuristic

Ahmia, Ibtissam

doi:10.3906/elk-1804-56

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…The GLS algorithm's performance in solving TSP was compared to previous well-known meta-heuristics such as the genetic algorithm (GA) 27 , ant colony optimization (ACO) 28 , artificial bee colony (ABC) 29 , and monarchy metaheuristic (MN2) 30 by testing it on various sizes of TSPLIB instances, as shown in Table 5. The enhancement percentage of GLS compared to the other algorithms is shown in Table 6.…”

Section: Resultsmentioning

confidence: 99%

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Sabry,

Shabana,

Handosa

et al. 2023

Preprint

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The blockchain network uses a Proof-of-Work (PoW) mechanism to validate transactions and keep the blockchain growth safe against tampering, but it is hugely energy-consuming with no benefit to the peer-to-peer network participants. In this paper, we propose a blockchain network for distributing products to different locations based on the use of the Proof of Useful Work (PoUW) mechanism, in which miners use computing resources to optimize the travelling salesman problem (TSP) as an alternative to solving mathematical problems that represent the basis of the traditional PoW mechanism to get a new block. According to this proposed blockchain, it not only receives and securely stores the distribution locations but also improves the paths for salesmen when travelling between different locations. This strategy aims to take advantage of the miners’ efforts to minimize the distance travelled by salesmen by applying the clustering technique and computing the shortest path by Guided Local Search (GLS) for each cluster at the same time. According to the tested results on TSP-LIB instances, the used technique works efficiently with an average of 0.08 compared to the rest of the meta-heuristics, and the proposed architecture reduced total distances with an average of 0.025.

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Section: Resultsmentioning

confidence: 99%

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Sabry,

Shabana,

Handosa

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The ability of GLS to reach shorter distances, more efficient paths, and the best utilization of local search heuristics make it preferred over other TSP optimization techniques. We compared GLS with previous well-known meta-heuristics such as the genetic algorithm (GA) 29 , ant colony optimization (ACO) 30 , artificial bee colony (ABC) 31 , and monarchy metaheuristic (MN2) 32 by testing it on various sizes of TSPLIB instances, the shortest distances, measured in kilometers, were obtained as shown in Table 6 . Table 7 shows the enhancement percentages of GLS compared to the other algorithms.…”

Section: Resultsmentioning

confidence: 99%

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Sabry,

Shabana,

Handosa

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The blockchain network uses a Proof-of-Work (PoW) mechanism to validate transactions and keep the blockchain growth safe against tampering, but it is hugely energy-consuming with no benefit to the peer-to-peer network participants. In this paper, we proposed a blockchain network for distributing products to different locations based on the use of the Proof of Useful Work mechanism, in which miners use computing resources to optimize the traveling salesman problem (TSP) as an alternative to solving mathematical problems that represent the basis of the traditional PoW mechanism to get a new block. According to this proposed blockchain, it not only receives and securely stores the distribution locations but also improves the paths for salesmen when traveling between different locations during the transportation process. This strategy aims to take advantage of the miners’ efforts to minimize the traveled distance by applying the clustering technique and computing the shortest path by Guided Local Search (GLS) for each cluster at the same time. According to the tested results on TSP-LIB instances, the used strategy works efficiently with an average of 0.08 compared to the rest of the meta-heuristics, and the proposed architecture reduced total distances with an average of 0.025%. In addition, the block generation time in the blockchain decreased by 11.11% compared to other works.

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“…However, it still has significant limitations. Though, FA has been widely used for dimensionality reduction technique due to its effectiveness, simplicity, and eases of implementation but still suffers from premature convergence, an imbalance between exploitation and exploration, and a significant risk of becoming stuck in a local optimum, especially when applied to high-dimensional optimization problems like fusion [27]. Therefore, in order to further improve the performance of multimodal biometric authentication system, this research introduced a meta-heuristic optimization approach using a modified firefly algorithm for feature level fusion as an efficient feature selection algorithm to select optimal features, reduce redundant features in the feature space and speed up convergence rate for better classification and employed Support Vector Machine (SVM) as the classifier.…”

Section: Introductionmentioning

confidence: 99%

A framework for Modified Firefly Algorithm in Multimodal Biometric Authentication System

Bukola

2023

int. jour. eng. com. sci

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Many end users are turning to multimodal biometric systems as a result of the limitations of conventional authentication techniques and unimodal biometric systems for offering a high level of accurate authentication. When high accuracy and security are required, multimodal biometrics are the best choice because to the utilization of numerous identification modalities. It is difficult to identify the best features that contribute to the recognition rate/accuracy and have a high redundancy of features since different features are acquired at the feature level fusion from a variety of physiological or behavioral variables. At the feature selection level, the utilization of meta-heuristic algorithms will reduce the number of redundant features while keeping critical feature sets that are important to biometric performance, accuracy, and efficiency. The study demonstrated a multimodal biometric authentication system that used the features of the face and both irises. In order to avoid being stuck at the local optimum and hasten convergence, the Firefly Algorithm (FFA) was modified by including a chaotic sinusoidal map function and a roulette wheel selection mechanism as deterministic processes. The results of the study demonstrated that in terms of sensitivity, precision, recognition accuracy, and time, the proposed MFFA with multimodal outperformed the MFFA for unimodal, bi-modal, and bi-instance. In addition to being computationally faster, more accurate, and suitable for real-time applications, the modified method, known as MFFA, proved effective in integrating multimodal data sets.

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A novel metaheuristic optimization algorithm: the monarchy metaheuristic

Cited by 9 publications

References 7 publications

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

A framework for Modified Firefly Algorithm in Multimodal Biometric Authentication System

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