A hybrid local search algorithm for minimum dominating set problems

Abed, Saad Adnan; Rais, Helmi Md; Watada, Junzo; Sabar, Nasser R.

doi:10.1016/j.engappai.2022.105053

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…However, the solution must be better than the one obtained in previous iterations. This helps the algorithm to escape local optima, which has been proved in recent studies [28]. The LAHC begins with a random initial solution and accepts or rejects new solutions until the stopping condition is satisfied.…”

Section: The Late Acceptance Hill-climbing (Lahc)mentioning

confidence: 92%

Improved Artificial Bee Colony Algorithm-based Path Planning of Unmanned Aerial Vehicle Using Late Acceptance Hill Climbing

2022

IJIES

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The unmanned aerial vehicle's primary goal (UAV) in route planning is to create a flight path from starting point to the ending point in space to avoid obstacles. The UAV path planning needs a real-time adaption and rapid reaction to the dynamic nature of the operational area since the path computing time, and average path length is very important factors over some cost function that reflects its effectiveness, such as power consumption or average trip time. Creating an ideal path must embrace a particular standard to reduce the distance the drone must travel. The artificial bee colony (ABC) represents one of the most important global search algorithms. The main problem with ABC is that it neglects the local search factor and uses the total search to find the optimal path to the target point since it searches the path to the target in remote areas rather than dealing with the nearby or neighboring areas, and this requires more time to find the path to reach the goal. On the other hand, late acceptance hill climbing (LAHC) algorithm uses the local search operator, which searches for the target point in the areas close to the starting point, thus providing a shorter path to reach a goal. This paper proposes and evaluates a new drone path planning algorithm, hybrid artificial bee colony (HABC). HABC algorithm design is based on modifying the ABC algorithm by cross-layer design between ABC and LAHC algorithms. The HABC is different from the original ABC algorithm in that it modifies the original one to reduce the total search by 3% and pushes ABC's search agent to use local search by 3% to start the process of rediscovering a new path to the target. The evaluation and analysis were performed for several performance metrics under different static evaluation scenarios. It has been observed from the results that the HABC outperforms the original ABC concerning average path length and standard deviation by a reduction reach to 25%, which leads to improved path planning.

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Section: The Late Acceptance Hill-climbing (Lahc)mentioning

confidence: 92%

Improved Artificial Bee Colony Algorithm-based Path Planning of Unmanned Aerial Vehicle Using Late Acceptance Hill Climbing

2022

IJIES

View full text Add to dashboard Cite

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“…Second, a high-quality initial solution can accelerate the convergence speed of local search algorithms. The common methods for generating an initial DS are greedy-based strategies [14] and random-based strategies [30], because these strategies are simple and efficient. However, the quality of solutions generated by these strategies is often poor and the time complexity would be increased significantly when adding complicated strategies to improve solutions' quality.…”

Section: B Motivation and Our Contributionsmentioning

confidence: 99%

“…UDG 1 : This benchmark is widely used in the literature on the MinDS problem [14], [21], [22], [24], [30], [33]. Graphs in this benchmark simulate wireless sensor networks, in which each vertex is a center of a circle.…”

Section: A Testing Benchmarksmentioning

confidence: 99%

Towards efficient local search for the minimum total dominating set problem

Liu

Wang

et al. 2021

Appl Intell

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Given a graph, the minimum dominating set (MinDS) problem is to identify a smallest set D of vertices such that every vertex not in D is adjacent to at least one vertex in D. The MinDS problem is a classic N P-hard problem and has been extensively studied because of its many disparate applications in network analysis. To solve this problem efficiently, many heuristic approaches have been proposed to obtain a good solution within an acceptable time limit. However, existing MinDS heuristic algorithms are always limited by various tie-breaking cases when selecting vertices, which slows down the effectiveness of the algorithms. In this paper, we design an efficient local search algorithm for the MinDS problem, named DmDS-a dual-mode local search framework that probabilistically chooses between two distinct vertex-swapping schemes. We further address limitations of other algorithms by introducing vertex selection criterion based on the frequency of vertices added to solutions to address tie-breaking cases, and a new strategy to improve the quality of the initial solution via a greedy-based strategy integrated with perturbation. We evaluate DmDS against the state-of-the-art algorithms on seven datasets, consisting of 346 instances (or families) with up to tens of millions of vertices. Experimental results show that DmDS obtains the best performance in accuracy for almost all instances and finds much better solutions than state-of-the-art MinDS algorithms on a broad range of large real-world graphs.

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