On the Robustness of Self-Adaptive Levy-Flight

Tholen, Christoph; El-Mihoub, Tarek A.; Nolle, Lars

doi:10.1109/oceanskobe.2018.8559485

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…The next step in this research is to combine the cooperative search algorithms with inertia-levy flight [14]. The impact of the localisation, navigation and communication errors on the performance of this combination will be investigated.…”

Section: Discussionmentioning

confidence: 99%

“…The maximum difference between the average temperature and the temperature of the centres of the SGDs were set to 6 • C and 4 • C [1]. The radiuses of the basin of SGDs were selected randomly in the range from 10 to 20 m. These radiuses are smaller than the maximum distance between the path of preplanned algorithm and the SGD [14].…”

Section: Test Environmentmentioning

confidence: 99%

“…Each AUV uses a temperature sensor with an accuracy of 0.01 • C to trace the SGD's location. Each AUV has a power supply that is sufficient for travelling a distance of 4500 meter with an average speed of 1 m/s [14]. Changing the direction of the AUV costs energy and a change by an angle of 180 • is assumed to be equivalent to travelling 4 meters [3].…”

Section: Simulation Of Auvs As Search Agentsmentioning

confidence: 99%

“…A small swarm of Autonomous Underwater Vehicles (AUVs) can be used for developing a flexible and low cost environmental marine observatory [14]. AUVs equipped with suitable sensors for collecting environmental data can explore intermediate size areas to locate phenomena of interest, like hydrothermal vents, hazardous substances and organisms [20].…”

Section: Introductionmentioning

confidence: 99%

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Towards Reducing the Impact of Localisation Errors on the Behaviour of a Swarm of Autonomous Underwater Vehicles

El-Mihoub

Tholen

Nolle

2020

mendel

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Localisation errors have a great impact on Autonomous Underwater Vehicles (AUVs) as search agents. Different approaches for solving the localisation problem can be used and combined together for greater accuracy in estimating AUVs’ locations. The effect of localisation errors on locating a target can be lightened by designing a search algorithm that avoids extensive use of exact lo-cation information. In this paper, two cooperative search algorithms are proposed and evaluated. In these algorithms, a high-level mechanism is employed for building a global view of the search space using minimum possible search information. These algorithms rely on low-level search algorithms with exploring roles. Particle Swarm Optimisation (PSO) and all-to-one Self-Organising Migrating Algorithm (SOMA) are selected as high-level mechanisms. The conducted experiments demonstrate that both algorithms show a robust behaviour within a range of localisation errors.

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Section: Discussionmentioning

confidence: 99%

Section: Test Environmentmentioning

confidence: 99%

Section: Simulation Of Auvs As Search Agentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards Reducing the Impact of Localisation Errors on the Behaviour of a Swarm of Autonomous Underwater Vehicles

El-Mihoub

Tholen

Nolle

2020

mendel

Self Cite

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“…The path of Levy flight is a random walk that alternates between high-frequency short steps and low-frequency long steps during flight [40]. With regard to researching spatially global solution space, the cuckoo search algorithm shows a strong jump stochasticity, which improves its global optimization functionality.…”

Section: The Weight and Threshold Optimization Of Tmcs-enn Modelmentioning

confidence: 99%

TMCS-ENN: A Temporal Feature-Correlation Cuckoo Search-Elman Neural Network for Sugar Raw Materials Demands Prediction

et al. 2022

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The prediction of the demand for raw materials is of vital importance to modern industries. Most studies are based on traditional regression, linear programming, and other methods. Previous studies have often overlooked the characteristics of the sugar raw materials business and the influence of time factors on raw material demand, resulting in limited prediction accuracy. How to accurately predict the demand for sugar raw materials is one of the key issues for intelligent management. In view of the above problems, combined with the characteristics of the supply and demand cycle of sugar raw materials, this paper aims to predict the demand for raw materials based on their supply and demand in a real sugar company by optimizing the Elman neural network through the modified cuckoo search (MCS) algorithm with temporal features. This study proposes a temporal feature-correlation cuckoo search–Elman neural network (TMCS-ENN) for predicting the demand for sugar raw materials. The experimental results show that the accuracy of the TMCS-ENN model reaches 93.89%, a better performance than that achieved by existing models. Therefore, the study model effectively improves the accuracy of the demand forecast of sugar raw materials for companies. This output will be helpful for improving the production efficiency and automation level, as well as reducing costs.

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