Multi-objective Optimization for Selecting and Scheduling Observations by Agile Earth Observing Satellites

Tangpattanakul, Panwadee; Jozefowiez, Nicolas; López, Pierre

doi:10.1007/978-3-642-32964-7_12

Cited by 35 publications

(17 citation statements)

References 7 publications

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“…We presented the biased random-key genetic algorithm (BRKGA) for solving this Earth observation scheduling problem in [27] [28]. Genetic algorithms are metaheuristic search methods, which can solve large-size problem instances and obtain satisfying solutions in an acceptable time [26].…”

Section: Biased Random-key Genetic Algorithmmentioning

confidence: 99%

“…It has different ways to select two parents for the crossover operation, compared with the original of random-key genetic algorithm (RKGA) [4]. In [27] [28], parameter values of BRKGA were experimentally tuned. The population size of BRKGA was set equal to the length of the random-key chromosome or twice the number of strips.…”

Section: Biased Random-key Genetic Algorithmmentioning

confidence: 99%

“…Moreover, BRKGA used encoding and decoding steps for managing the solution. For the decoding step, the basic decoding was implemented in [27]. Several decoding procedures have been proposed in [27,28].…”

Section: Biased Random-key Genetic Algorithmmentioning

confidence: 99%

“…For the decoding step, the basic decoding was implemented in [27]. Several decoding procedures have been proposed in [27,28]. We will test IBMOLS agains the best version of the algorithm.…”

Section: Biased Random-key Genetic Algorithmmentioning

confidence: 99%

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A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite

Tangpattanakul

Jozefowiez

López

2015

European Journal of Operational Research

Self Cite

125

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This paper presents an indicator-based multi-objective local search (IBMOLS) to solve a multi-objective optimization problem. The problem concerns the selection and scheduling of observations for an agile Earth observing satellite. The mission of an Earth observing satellite is to obtain photographs of the Earth surface to satisfy user requirements. Requests from several users have to be managed before transmitting an order, which is a sequence of selected acquisitions, to the satellite. The obtained sequence has to optimize two objectives under operation constraints. The objectives are to maximize the total profit of the selected acquisitions and simultaneously to ensure the fairness of resource sharing by minimizing the maximum profit difference between users. Experiments are conducted on realistic instances. Hypervolumes of the approximate Pareto fronts are computed and the results from IBMOLS are compared with the results from the biased random-key genetic algorithm (BRKGA).

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Section: Biased Random-key Genetic Algorithmmentioning

confidence: 99%

Section: Biased Random-key Genetic Algorithmmentioning

confidence: 99%

Section: Biased Random-key Genetic Algorithmmentioning

confidence: 99%

“…For the decoding step, the basic decoding was implemented in [27]. Several decoding procedures have been proposed in [27,28]. We will test IBMOLS agains the best version of the algorithm.…”

Section: Biased Random-key Genetic Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite

Tangpattanakul

Jozefowiez

López

2015

European Journal of Operational Research

Self Cite

125

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show abstract

“…2. In [12], we proposed a straightforward use of biased random-key genetic algorithm (BRKGA). Note that multiple users have also been considered in [3].…”

mentioning

confidence: 99%

Biased Random Key Genetic Algorithm for Multi-user Earth Observation Scheduling

Tangpattanakul¹,

Jozefowiez²,

López³

2014

Recent Advances in Computational Optimization

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This paper presents a biased random key genetic algorithm, or BRKGA, for solving a multi-user observation scheduling problem. BRKGA is an efficient method in the area of combinatorial optimization. It is usually applied to single objective problem. It needs to be adapted for multi-objective optimization. This paper considers two adaptations. The first one presents how to select the elite set, i.e., good solutions in the population. We borrow the elite selection methods from efficient multi-objective evolutionary algorithms. For the second adaptation, since the multi-objective optimization needs a set of solutions on the Pareto front, we investigate the idea to obtain several solutions from a single chromosome. Experiments are conducted on realistic instances, which concern the multi-user observation scheduling of an agile Earth observing satellite.

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Joint Probability Density Immune Algorithm for Multi-satellite Earth Observation Scheduling

Ren

Ning

Shi

et al. 2021

Computational and Experimental Simulations in Engineering

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Multi-objective Optimization for Selecting and Scheduling Observations by Agile Earth Observing Satellites

Cited by 35 publications

References 7 publications

A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite

A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite

Biased Random Key Genetic Algorithm for Multi-user Earth Observation Scheduling

Joint Probability Density Immune Algorithm for Multi-satellite Earth Observation Scheduling

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