Discrete invasive weed optimization algorithm: application to cooperative multiple task assignment of UAVs

Ramezani, Mohsen; Hajimirsadeghi, Hossein; Lucas, Caro

doi:10.1109/cdc.2009.5400938

Cited by 17 publications

(2 citation statements)

References 21 publications

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“…Until now, various studies used this novel algorithm for solving their problems, such as array antenna synthesis problems (Karimkashi & Kishk, 2010), timecost trade-off problems (Ramezani-Ghalenoei & Hajimirsadeghi, 2009), no wait-two-stage multiprocessor flow shop scheduling problems (Moradi-Nasab, Shafaei, Rabiee, & Mazinani, 2012) and multi-objective problems (Kundu, Suresh, Ghosh, Das, & Panigrahi, 2011). All the results show that IWO outperforms most of the oldest powerful meta-heuristics.…”

Section: Front2mentioning

confidence: 87%

Solving a multi-objective chance-constrained hub covering location problem by discrete invasive weed optimization

et al. 2014

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This paper presents a stochastic bi-objective model for a single-allocation hub covering problem (HCP) with the variable capacity and uncertainty parameters. Locating hubs can influence the performance of hub and spoke networks, as a strategic decision. The presented model optimizes two objectives minimizing the total transportation cost and the maximum transportation time from an origin to a destination simultaneously. Then, due to the NP-hardness of the multi-objective chance-constrained HCP, the presented model is solved by a well-known metaheuristic algorithm, namely multi-objective invasive weed optimization. Additionally, the associated results are compared with a well-known multi-objective evolutionary algorithm, namely non-dominated sorting genetic algorithm. Furthermore, the computational results of the foregoing algorithms are reported in terms four well-known metrics, namely quality, spacing, diversification, and mean ideal distance. Finally, the conclusion is reported.Nowadays, transportation and communication networks are of a vital role in modern societies. Thus, optimization of the network design for managing better the traffics and decreasing transport time and cost in acceptable service quality is more important. In this paper, we optimize a network structure for achieving two objectives, namely minimizing the total transport cost and maximum transport time in each allocation when a demand is uncertain. Because of the complexity of the problem, we propose a meta-heuristic algorithm that hss inspired the behavior of invasive weed colonization.

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Section: Front2mentioning

confidence: 87%

Solving a multi-objective chance-constrained hub covering location problem by discrete invasive weed optimization

et al. 2014

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“…It has shown successful results in many practical applications like optimization and tuning of a robust controller, developing a recommender system, design of encoding sequences for DNA computing, distributed identification and adaptive control of a surge tank,analysis of electricity markets dynamics, optimal positioning of piezoelectric actuators, and adaptive beam forming [6].…”

Section: Invasive Weed Optimization Algorithmmentioning

confidence: 99%

Automatic liver CT image clustering based on invasive weed optimization algorithm

Elmasry

Emary

Hassanien

2014

2014 International Conference on Engineering and Technology (ICET)

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In this paper, an automated liver CT image clustering approach based on evolutionary metaheuristic algorithm called invasive weed optimization is presented without any prior information about the number of naturally occurring groups in the images. The fitness function used in the genetic algorithm is kmeans objective function for searching of the smoothed compact cluster. The experimental results suggest that invasive weed optimization holds immense promise to appear as an efficient metaheuristic for multi-objective optimization in computer aided diagnosis applications.

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Comparison of Nature-Inspired Approaches for Path Planning Problem of Mobile Robots in MATLAB

Agarwal

Bharti

2022

Lecture Notes in Mechanical Engineering

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Discrete invasive weed optimization algorithm: application to cooperative multiple task assignment of UAVs

Cited by 17 publications

References 21 publications

Solving a multi-objective chance-constrained hub covering location problem by discrete invasive weed optimization

Solving a multi-objective chance-constrained hub covering location problem by discrete invasive weed optimization

Automatic liver CT image clustering based on invasive weed optimization algorithm

Comparison of Nature-Inspired Approaches for Path Planning Problem of Mobile Robots in MATLAB

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