An improved configuration checking-based algorithm for the unicost set covering problem

Wang, Yiyuan; Pan, Shiwei; Al‐Shihabi, Sameh; Zhou, Junping; Yang, Nan; Yin, Minghao

doi:10.1016/j.ejor.2021.02.015

Cited by 9 publications

(4 citation statements)

References 54 publications

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“…To calculate the score value, denoted as sc(v), we consider the frequency of all the rows in I G , referring to the idea in [28]. Let R(I G ) = {r 1 , r 2 , .…”

Section: Scoring Functionmentioning

confidence: 99%

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A Population-Based Local Search Algorithm for the Identifying Code Problem

Lara-Caballero,

González-Moreno

2023

Mathematics

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The identifying code problem for a given graph involves finding a minimum subset of vertices such that each vertex of the graph is uniquely specified by its nonempty neighborhood within the identifying code. The combinatorial optimization problem has a wide variety of applications in location and detection schemes. Finding an identifying code of minimum possible size is a difficult task. In fact, it has been proven to be computationally intractable (NP-complete). Therefore, the use of heuristics to provide good approximations in a reasonable amount of time is justified. In this work, we present a new population-based local search algorithm for finding identifying codes of minimum cost. Computational experiments show that the proposed approach was found to be more effective than other state-of-the-art algorithms at generating high-quality solutions in different types of graphs with varying numbers of vertices.

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“…To calculate the score value, denoted as sc(v), we consider the frequency of all the rows in I G , referring to the idea in [28]. Let R(I G ) = {r 1 , r 2 , .…”

Section: Scoring Functionmentioning

confidence: 99%

“…In this work, we used the improved CC strategy, ES-CC, proposed by Wang et al [28] to update the con f array after each iteration. As opposed to the traditional CC strategy, ES-CC considers the element or row states in addition to the neighboring states.…”

Section: Configuration Checking Strategymentioning

confidence: 99%

A Population-Based Local Search Algorithm for the Identifying Code Problem

Lara-Caballero,

González-Moreno

2023

Mathematics

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“…The simulated annealing algorithm for the CPP, though it can perform the non-improving moves to escape a local space, still suffers from the cycling issues. The configuration checking strategy can release local search algorithms from heavily cycling issues, and has been successfully applied in a number of NP-hard problems from the satisfiability problem to graph problems (Luo et al, 2013(Luo et al, , 2015Chu et al, 2017;Abramé et al, 2017;Chen et al, 2020;Wang et al, 2021). However, the existing configuration checking strategy does not work effectively to tackle complete graphs.…”

Section: Contributionsmentioning

confidence: 99%

Improving Simulated Annealing for Clique Partitioning Problems

Gao¹,

Lv²,

Liu³

et al. 2022

jair

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The Clique Partitioning Problem (CPP) is essential in graph theory with a number of important applications. Due to its NP-hardness, efficient algorithms for solving this problem are very crucial for practical purposes, and simulated annealing is proved to be effective in state-of-the-art CPP algorithms. However, to make simulated annealing more efficient to solve large-scale CPPs, in this paper, we propose a new iterated simulated annealing algorithm. Several methods are proposed in our algorithm to improve simulated annealing. First, a new configuration checking strategy based on timestamp is presented and incorporated into simulated annealing to avoid search cycles. Afterwards, to enhance the local search ability of simulated annealing and speed up convergence, we combine our simulated annealing with a descent search method to solve the CPP. This method further improves solutions found by simulated annealing, and thus compensates for the local search effect. To further accelerate the convergence speed, we introduce a shrinking factor to decline initial temperature and then propose an iterated local search algorithm based on simulated annealing. Additionally, a restart strategy is adopted when the search procedure converges. Extensive experiments on benchmark instances of the CPP were carried out, and the results suggest that the proposed simulated annealing algorithm outperforms all the existing heuristic algorithms, including five state-of-the-art algorithms. Thus the best-known solutions for 34 instances out of 94 are updated. We also conduct comparative analyses of the proposed strategies and show their effectiveness.

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“…This algorithm applied a process that began from an initial seed solution to repetitively create sub-problems to be resolved more efficiently. A configuration checkingbased algorithm for SCP [31] was developed to not only consider subset states but also take into account the element states, leading to cutting down the unnecessary search space. Sadeghi et al [32] developed a simple Lagrangian heuristic algorithm for SCP.…”

Section: Literaturementioning

confidence: 99%

A New Direct Coefficient-Based Heuristic Algorithm for Set Covering Problems

Hashemi¹,

Gholami²,

Venkatadri³

et al. 2021

Int. J. Fuzzy Syst.

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The set covering problem is a fundamental model which comprises a wide range of important applications such as crew scheduling problems that need to cover a set of trips. It is one of the most common issues in the facility location problem, which requires further investigations, particularly in emergency and service facilities. As such, the objective of this study is to propose a new coefficient-based heuristic algorithm for the set covering problems. This paper has accordingly presented the algorithm that evaluates the qualification of subsets by directly applying a fitness function. This fitness function is formulated based on sets and subsets coefficients in a way that the subsets of selected sets have the lowest probability to be selected in the next iteration. The algorithm is not only capable of constructing an answer within polynomial time, but can solve complex set covering problems without conventional restrictions. The performance of this algorithm is evaluated on benchmark instances including a set of reproduced and selected OR-library problems within different sizes. Computational results indicate that the proposed heuristic algorithm produces better solutions, especially in large-scale problems comparing simulated annealing in terms of quality and time.

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An improved configuration checking-based algorithm for the unicost set covering problem

Cited by 9 publications

References 54 publications

A Population-Based Local Search Algorithm for the Identifying Code Problem

A Population-Based Local Search Algorithm for the Identifying Code Problem

Improving Simulated Annealing for Clique Partitioning Problems

A New Direct Coefficient-Based Heuristic Algorithm for Set Covering Problems

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