Near-Optimal Covering Solution for USV Coastal Monitoring using PAES

Ouelmokhtar, Hand; Benmoussa, Yahia; Diguet, Jean-Philippe; Benazzouz, Djamel; Lemarchand, Laurent

doi:10.1007/s10846-022-01717-x

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Evolutionary algorithms are usually used as an important tool for solving multiobjective problems. Because of the inherent parallelism of population iteration in multiobjective evolutionary algorithms, a near-optimal solution set covering the entire Pareto front can be generated in one iteration [44]. Deb et al proposed the NSGA-II algorithm using a fast non-dominated sorting strategy [45].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Multi-Objective Automatic Clustering Algorithm Based on Evolutionary Multi-Tasking Optimization

Wang,

Dang,

Yang

et al. 2024

Electronics

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Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has obvious advantages compared with the traditional single-objective method. In order to further improve the performance of evolutionary multi-objective clustering algorithms, this paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization. Based on the multi-objective clustering algorithm that automatically determines the value of k, evolutionary multi-task optimization is introduced to deal with multiple clustering tasks simultaneously. A set of non-dominated solutions for clustering results is obtained by concurrently optimizing the overall deviation and connectivity index. Multi-task adjacency coding based on a locus adjacency graph was designed to encode the clustered data. Additionally, an evolutionary operator based on relevance learning was designed to facilitate the evolution of individuals within the population. It also facilitates information transfer between individuals with different tasks, effectively avoiding negative transfer. Finally, the proposed algorithm was applied to both artificial datasets and UCI datasets for testing. It was then compared with traditional clustering algorithms and other multi-objective clustering algorithms. The results verify the advantages of the proposed algorithm in clustering accuracy and algorithm convergence.

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Section: Multi-objective Optimizationmentioning

confidence: 99%

Multi-Objective Automatic Clustering Algorithm Based on Evolutionary Multi-Tasking Optimization

Wang,

Dang,

Yang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Numerous studies have explored Unmanned Surface Vehicle (USV) path planning using various metaheuristic approaches such as Ant Colony Optimization [1], Genetic Algorithms as shown in [8] and [7]. Our focus lies in addressing a multi-objective port surveillance problem.…”

Section: Related Workmentioning

confidence: 99%