Consistent dynamic map labeling with fairness and importance

Zhang, Xiao; Poon, Sheung-Hung; Liu, Shengxin; Li, Minming; Lee, Victor C. S.

doi:10.1016/j.cagd.2020.101892

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Each employed bee is assigned to a specific food source, meaning that there are an equal number of employed bees and food sources. Neighborhood search can be performed according to Equation (5) to generate new solutions to find better food sources.…”

Section: Employed Bee Phasementioning

confidence: 99%

“…In the traditional artificial bee colony algorithm, employed bees and onlooker bees mainly generate new solutions by Equation (5), which is too single and not well suited for discrete problems. Therefore, we use Equation ( 9) for the employed bee for updating and two search operators for the onlooker bee with dynamic probability to better fit the discrete problem of PFLP.…”

Section: Analysis Of Neighborhood Solution Generationmentioning

confidence: 99%

“…Dealing with combinatorial optimization problems means choosing the optimal solution to maximize or minimize the objective function under the given conditions. There exist many practical application scenarios for this problem, such as graph coloring problems [1], workshop scheduling [2], traveling salesman problems [3,4], and label placement problems [5]. Solving such problems is significant in terms of productivity improvement and cost reduction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Discrete Artificial Bee Colony Algorithm Based on Label Similarity for Solving Point-Feature Label Placement Problem

Cao,

Xu,

Zhang

et al. 2023

IJGI

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The artificial bee colony algorithm (ABC) is a promising metaheuristic algorithm for continuous optimization problems, but it performs poorly in solving discrete problems. To address this issue, this paper proposes a hybrid discrete artificial bee colony (HDABC) algorithm based on label similarity for the point-feature label placement (PFLP) problem. Firstly, to better adapt to PFLP, we have modified the update mechanism for employed bees and onlooker bees. Employed bees learn the label position of the better individuals, while onlooker bees perform dynamic probability searches using two neighborhood operators. Additionally, the onlooker bees’ selection method selects the most promising solutions based on label similarity, which improves the algorithm’s search capabilities. Finally, the Metropolis acceptance strategy is replaced by the original greedy acceptance strategy to avoid the premature convergence problem. Systematic experiments are conducted to verify the effectiveness of the neighborhood solution generation method, the selection operation based on label similarity, and the Metropolis acceptance strategy in this paper. In addition, experimental comparisons were made at different instances and label densities. The experimental results show that the algorithm proposed in this paper is better or more competitive with the compared algorithm.

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Section: Employed Bee Phasementioning

confidence: 99%

Section: Analysis Of Neighborhood Solution Generationmentioning

confidence: 99%