Probability learning based tabu search for the budgeted maximum coverage problem

Liu, Liwen; Wei, Zequn; Hao, Jin‐Kao; He, Kun

doi:10.1016/j.eswa.2021.115310

Cited by 12 publications

(31 citation statements)

References 18 publications

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“…Chauhan et al [32] proposed a robust three-stage heuristic to solve the coverage facility location problem with drones considering uncertainties such as battery availability and consumption. A recent study conducted by Li et al [33] solved a new variant named budgeted maximum coverage problem based on an algorithm that combines reinforcement learning with tabu search. For solving VRP and its variants, efficient algorithms such as tabu search [34,35], variable neighborhood search [36], large neighborhood search [37], genetic algorithms [38], iterated local search algorithms [39,40], and hybrid algorithms [41,42] have been adopted in existing studies.…”

Section: Related Workmentioning

confidence: 99%

An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship

Chen

Luo

2022

Sensors

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Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales.

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Section: Related Workmentioning

confidence: 99%

An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship

Chen

Luo

2022

Sensors

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“…This greedy algorithm outputs the best among two candidate solutions (lines [11][12][13][14]. The first candidate is generated by a greedy heuristic (lines 1-9) that iteratively adds an item i ∈ I \ S out into S out that satisfies the total cost of all items in S out ∪ {i} not exceeding the budget L and the increased weight Algorithm 2 Greedy() Output: a solution S out 1: S out := ∅ 2: while TRUE do 3:…”

Section: The Greedy Constructive Algorithmmentioning

confidence: 99%

“…Moreover, the BMCP is highly related to the Set-Union Knapsack Problem (SUKP) [13], wherein each item has a nonnegative profit and each element has a nonnegative weight. The goal of the SUKP is to select a subset S ⊆ I that maximizes the total profits of the items in S, with the constraint that the sum of the weights of all the elements covered by the items in S cannot exceed a given knapsack capacity C. In a word, the BMCP is a variant problem of the SUKP, and can be transferred to the SUKP by swapping the attributes of the items and elements [14]. The SUKP has received much attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Later on, some approximation algorithms [9,12,22] and two greedy constructive algorithms [8] were proposed for various extensions or variants of the BMCP. Recently in 2021, Li et al [14] proposed a meta-heuristic algorithm based on tabu search and reinforcement learning, called probability learning based tabu search (PLTS), for the standard BMCP problem. To the best of our knowledge, PLTS is the only practical algorithm proposed for solving the standard BMCP.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [14] also proposed the first set of BMCP benchmarks to facilitate the evaluation of algorithms. However, the structure of this set of BMCP benchmarks is simple and their problem scale is small in 585 to 1000 for the number of items/elements.…”

Section: Introductionmentioning

confidence: 99%

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Effective Variable Depth Local Search for the Budgeted Maximum Coverage Problem

Zhou¹,

Zheng²,

He³

2022

Preprint

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We address the Budgeted Maximum Coverage Problem (BMCP), which is a natural and more practical extension of the standard 0-1 knapsack problem and the set cover problem. Given m elements with nonnegative weights, n subsets of elements with nonnegative costs, and a total budget, BMCP aims to select some subsets such that the total cost of selected subsets does not exceed the budget, and the total weight of associated elements is maximized. In this paper, we propose a variable depth local search algorithm (VDLS) for the BMCP. VDLS first generates an initial solution by a greedy algorithm, then iteratively improves the solution through a partial depth-first search method, that can improve the solution by simultaneously changing the states (selected or not) of multiple subsets. Such method allows VDLS to explore the solution space widely and deeply, and to yield high-quality solutions. We further propose a neighbour structure to boost the algorithm performance, that is, both subsets have a neighbour relation if they have at least one common associated element. By applying the neighbour structure, VDLS can adjust the selected subsets while losing as few covered elements as possible. Since the existing BMCP benchmarks only have simple structures and small scales, we design 60 new instances with relatively large scales and complex structures to enrich the diversity of the BMCP instances. Experimental results on 30 public instances and 60 new instances we designed demonstrate that VDLS significantly outperforms the existing heuristic and the general CPLEX exact solver, for the BMCP.

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Hybrid Evolutionary Algorithm for the Overlap Constrained Resource Allocation Problem in Wireless Networks

Wang,

Li,

Wei

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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Probability learning based tabu search for the budgeted maximum coverage problem

Cited by 12 publications

References 18 publications

An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship

An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship

Effective Variable Depth Local Search for the Budgeted Maximum Coverage Problem

Hybrid Evolutionary Algorithm for the Overlap Constrained Resource Allocation Problem in Wireless Networks

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