Population-Based Incremental Learning Algorithm for a Serial Colored Traveling Salesman Problem

Meng, Xianghu; Li, Jun; Zhou, MengChu; Dai, Xianzhong; Dou, Jun

doi:10.1109/tsmc.2016.2591267

Cited by 74 publications

(22 citation statements)

References 39 publications

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“…It is worth noting that our framework is applicable to those small-scale but complicated sequencing problems, while it would be more suitable to apply other exact or heuristic approaches to handle large-scale problems. For the future research, we can apply the proposed enhanced B&B to find the optimal solutions of other practical sequencing problems, e.g., disassembly sequencing problem [16], [20], [39], heterogeneous traveling salesman problem [21], [26], [30], and block relocation problem [38], [46]. In addition, more advanced searching techniques, such as different branching rules [1], problem-specific dominance checkers, incremental bounding [25], and other caching methods, can be studied for further improving the performance of the proposed framework.…”

Section: Discussionmentioning

confidence: 99%

Enhanced Branch-and-Bound Framework for a Class of Sequencing Problems

Zhang

Teng

Zhou

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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In this paper, we propose an enhanced branch-andbound (B&B) framework for a class of sequencing problems, which aim to find a permutation of all involved elements to minimize a given objective function. We require that the sequencing problems satisfy three conditions: 1) incrementally computable; 2) monotonic; and 3) overlapping subproblems. Our enhanced B&B framework is built on the classical B&B process by introducing two techniques, i.e., dominance rules and caching search states. Following the enhanced B&B framework, we conduct empirical studies on three typical and challenging sequencing problems, i.e., quadratic traveling salesman problem, traveling repairman problem, and talent scheduling problem. The computational results demonstrate the effectiveness of our enhanced B&B framework when compared to classical B&B and some exact approaches, such as dynamic programming and constraint programming. Additional experiments are carried out to analyze different configurations of the algorithm. Index Terms-Branch-and-bound (B&B), caching states, dominance rules, dynamic programming (DP), talent scheduling problem, traveling salesman/repairman problem. I. INTRODUCTION T HE SEARCH techniques for solving combinatorial optimization problems (COPs) have been widely studied in the communities of artificial intelligence, industrial engineering, and operations research during the last century. The simplest and standard framework for solving COPs is to use branch-and-bound (B&B) algorithms [3], [8], [14]. Its basic idea is to systematically and implicitly enumerate all possible candidate solutions, where large subsets of fruitless candidates Manuscript

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Section: Discussionmentioning

confidence: 99%

Enhanced Branch-and-Bound Framework for a Class of Sequencing Problems

Zhang

Teng

Zhou

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

Self Cite

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“…Liu et al studied the path optimization problem of intelligent driving vehicles and combined the prior reinforcement learning technology with the A * algorithm to search for the shortest path [4]. Meng et al studied the serial color traveling salesman path planning problem (SCTSP) and proposed a population-based incremental learning algorithm with a 2opt local search [22]. Xu et al studied the path optimization problem of the general colored traveling salesman problem (GCTSP) and proposed a Delaunay-triangulation-based variable neighborhood search algorithm to solve the problem [23].…”

Section: Related Workmentioning

confidence: 99%

The Path Optimization Algorithm of Car Navigation System considering Node Attributes under Time-Invariant Network

Zhu

Sun

2021

Mobile Information Systems

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Vehicle path planning plays a key role in the car navigation system. In actual urban traffic, the time spent at intersections accounts for a large proportion of the total time and cannot be ignored. Therefore, studying the shortest path planning problem considering node attributes has important practical significance. In this article, we study the vehicle path planning problem in time-invariant networks, with the minimum travel time from the starting node to the destination node as the optimization goal (including node time cost). Based on the characteristics of the problem, we construct the mathematical model. We propose a Reverse Order Labeling Algorithm (ROLA) based on the traditional Dijkstra algorithm to solve the problem; the correctness of the proposed algorithm is proved theoretically, and we analyse and give the time complexity of the ROLA and design a calculation example to verify the effectiveness of the algorithm. Finally, through extensive simulation experiments, we compare the performance of the proposed ROLA with several other existing algorithms. The experimental results show that the proposed algorithm has good stability and high efficiency.

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“…Many metaheuristic algorithms have been successfully used to solve NP-hard problems such as the travelling salesman problems and vehicle routing problems. These new optimization algorithms include ant colony optimization (ACO) (Dorigo & Caro, 1999), particle swarm optimization (PSO) (Huang et al, 2003;Shia et al, 2007), genetic algorithm (GA) (Chatterjee et al, 1996;Marinakis et al, 2007), firefly algorithm (FA) (Osaba et al, 2017), bat algorithm (BA) (Osaba et al, 2016) and other swarm intelligence based algorithms (Meng et al, 2016;Li et al, 2015). These algorithms can obtain surprisingly good solutions with sufficient accuracy.…”

Section: Recent Developmentsmentioning

confidence: 99%

Neighborhood information-based probabilistic algorithm for network disintegration

Liu

Yang

2020

Expert Systems with Applications

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Many real-world applications can be modelled as complex networks, and such networks include the Internet, epidemic disease networks, transport networks, power grids, protein-folding structures and others. Network integrity and robustness are important to ensure that crucial networks are protected and undesired harmful networks can be dismantled. Network structure and integrity can be controlled by a set of key nodes, and to find the optimal combination of nodes in a network to ensure network structure and integrity can be an NP-complete problem. Despite extensive studies, existing methods have many limitations and there are still many unresolved problems. This paper presents a probabilistic approach based on neighborhood information and node importance, namely, neighborhood information-based probabilistic algorithm (NIPA). We also define a new centrality-based importance measure (IM), which combines the contribution ratios of the neighbor nodes of each target node and two-hop node information.Our proposed NIPA has been tested for different network benchmarks and compared with three other methods: optimal attack strategy (OAS), high betweenness first (HBF) and high degree first (HDF). Experiments suggest that the proposed NIPA is most effective among all four methods. In general, NIPA can identify the most crucial node combination with higher effectiveness, and the set of optimal key nodes found by our proposed NIPA is much smaller than that by heuristic centrality prediction. In addition, many previously neglected weakly connected nodes are identified, which become a crucial part of the newly identified optimal nodes. Thus, revised strategies for protection are recommended to ensure the safeguard of network integrity. Further key issues and future research topics are also discussed. Citation Details:Qian Li, San-Yang Liu, Xin-She Yang, Neighborhood information-based probabilistic algorithm for network disintegration, Network structures and characteristics appear naturally in many systems and applications. Examples are power grid networks, communication networks, transport networks, the Internet and others. Such networks form a crucial part of modern infrastructure, and the robustness and integrity of such networks can have a huge impact on the quality of life and society (Buldyrev et al.,

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Population-Based Incremental Learning Algorithm for a Serial Colored Traveling Salesman Problem

Cited by 74 publications

References 39 publications

Enhanced Branch-and-Bound Framework for a Class of Sequencing Problems

Enhanced Branch-and-Bound Framework for a Class of Sequencing Problems

The Path Optimization Algorithm of Car Navigation System considering Node Attributes under Time-Invariant Network

Neighborhood information-based probabilistic algorithm for network disintegration

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