Improved hybrid node localization using the wild horse optimization in the underwater environment

Saha, Souvik; Arya, Rajeev

doi:10.1007/s13198-021-01388-1

Cited by 3 publications

(1 citation statement)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The wild horse optimizer (WHO) [42] is a new meta-heuristic algorithm that has been employed successfully for the optimization of practical problems due to its limited parameters, great optimum capability and relatively low time complexity. For example, it has been employed in extracting model parameters in photovoltaic systems [43], solving nonlinear multi-objective optimization problems in energy management [44] and solving link failure problems in underwater channels [45]. Although WHO can achieve satisfactory results on some practical issues, there are still some problems, such as a limited exploitation capability and stagnation of locally optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Wild Horse Optimizer with Cauchy Mutation and Dynamic Random Search for Hyperspectral Image Band Selection

Chen,

Sun,

Chen

et al. 2024

Electronics

View full text Add to dashboard Cite

The high dimensionality of hyperspectral images (HSIs) brings significant redundancy to data processing. Band selection (BS) is one of the most commonly used dimensionality reduction (DR) techniques, which eliminates redundant information between bands while retaining a subset of bands with a high information content and low noise. The wild horse optimizer (WHO) is a novel metaheuristic algorithm widely used for its efficient search performance, yet it tends to become trapped in local optima during later iterations. To address these issues, an enhanced wild horse optimizer (IBSWHO) is proposed for HSI band selection in this paper. IBSWHO utilizes Sobol sequences to initialize the population, thereby increasing population diversity. It incorporates Cauchy mutation to perturb the population with a certain probability, enhancing the global search capability and avoiding local optima. Additionally, dynamic random search techniques are introduced to improve the algorithm search efficiency and expand the search space. The convergence of IBSWHO is verified on commonly used nonlinear test functions and compared with state-of-the-art optimization algorithms. Finally, experiments on three classic HSI datasets are conducted for HSI classification. The experimental results demonstrate that the band subset selected by IBSWHO achieves the best classification accuracy compared to conventional and state-of-the-art band selection methods, confirming the superiority of the proposed BS method.

show abstract