Journal of Heuristics Impact Factor & Information

Publisher: Springer Verlag

Journal description

The field of heuristics within the area of optimization has become extremely important as researchers and practitioners attempt to solve larger and more complex problems. With the integration of artificial intelligence principles in the area of optimization the field of heuristics has experienced a rapid growth. The Journal of Heuristics considers the importance of theoretical empirical and experimental work related to the development of heuristics providing a forum for advancing the state-of-the-art in the theory and practical application of techniques for solving problems approximately that cannot be solved exactly. The journal fosters the development understanding and practical use of heuristic solution techniques for solving business engineering and societal problems. The following areas are of interest to the journal: Practical Applications showing the benefits achieved by the particular application (i.e. reduced costs increased profits etc.). These papers can deal with new application areas for which no previous solution methods exist or with areas where previous methods have proved unsatisfactory. Theoretical Developments dealing with theoretical work on existing or new heuristics. These papers generally present mathematical work such as probabilistic analysis convergence results worst-case analysis combinatorial leverage results and theorems about special structures. Decision Analysis models that consider issues of rational decision making with limited information. Of special interest is the interface between decision analysis and the search principles used in artificial intelligence and the integration of processes in which information becomes available in stages or where data and inputs are subject to uncertainties. Artificial Intelligence -based heuristics applied to a wide variety of problems in optimization classification statistics recognition planning design and so forth. Typical approaches include genetic algorithms neural networks simulated annealing and tabu search. Learning paradigms with implications for heuristic problem solving are also encouraged. Computational Experimentation focuses on computational comparisons of heuristic methods. The Journal of Heuristics provides strict guidelines under which the experiments are to be conducted. The Journal of Heuristics also provides access to all problem instances used for computation experiments published in this area.

Current impact factor: 1.36

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 1.359
2012 Impact Factor 1.471
2011 Impact Factor 1.262
2010 Impact Factor 1.623
2009 Impact Factor 1.264
2008 Impact Factor 1.064
2007 Impact Factor 0.644
2006 Impact Factor 0.74
2005 Impact Factor 0.551
2004 Impact Factor 1.113
2003 Impact Factor 0.633
2002 Impact Factor 0.655
2001 Impact Factor 0.404
2000 Impact Factor 0.65

Impact factor over time

Impact factor
Year

Additional details

5-year impact 1.80
Cited half-life 9.00
Immediacy index 0.17
Eigenfactor 0.00
Article influence 0.90
Website Journal of Heuristics website
Other titles Journal of heuristics (Online)
ISSN 1381-1231
OCLC 41570324
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Springer Verlag

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on pre-print servers such as arXiv.org
    • Author's post-print on author's personal website immediately
    • Author's post-print on any open access repository after 12 months after publication
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany link to published version (see policy)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Stochastic local search (SLS) is an appealing method for solving the maximum satisfiability (Max-SAT) problem. This paper proposes a new variable selection heuristic for Max-SAT local search algorithms, which works particularly well for weighted Max-2-SAT instances. Evolving from the recent configuration checking strategy, this new heuristic works in three levels and is called CCTriplex. According to the CCTriplex heuristic, a variable that is both decreasing and configuration changed has the higher priority to be flipped than a decreasing variable, which in turn has the higher priority than a configuration changed variable. The CCTriplex heuristic is used to develop a new SLS algorithm for weighted Max-2-SAT called CCMaxSAT. We evaluate CCMaxSAT on random benchmarks with different densities, and the hand crafted Frb benchmark, as well as weighted Max-2-SAT instances encoded from MaxCut, MaxClique and sports scheduling problems. Compared with the state-of-the-art SLS solver for weighted Max-2-SAT called ITS and the best SLS solver in Max-SAT Evaluation 2012 namely ubcsat-IRoTS, as well as the famous complete solver wMaxSATz, our algorithm CCMaxSAT shows rather good performance on all the benchmarks.
    Journal of Heuristics 02/2015; DOI:10.1007/s10732-015-9284-3
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    ABSTRACT: Large-scale unconditional and conditional vertex \(p\) -centre problems are solved using two meta-heuristics. One is based on a three-stage approach whereas the other relies on a guided multi-start principle. Both methods incorporate Variable Neighbourhood Search, exact method, and aggregation techniques. The methods are assessed on the TSP dataset which consist of up to 71,009 demand points with \(p\) varying from 5 to 100. To the best of our knowledge, these are the largest instances solved for unconditional and conditional vertex \(p\) -centre problems. The two proposed meta-heuristics yield competitive results for both classes of problems.
    Journal of Heuristics 01/2015; DOI:10.1007/s10732-014-9277-7
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    ABSTRACT: Developers of high-performance algorithms for hard computational problems increasingly take advantage of automated parameter tuning and algorithm configuration tools, and consequently often create solvers with many parameters and vast configuration spaces. However, there has been very little work to help these algorithm developers answer questions about the high-quality configurations produced by these tools, specifically about which parameter changes contribute most to improved performance. In this work, we present an automated technique for answering such questions by performing ablation analysis between two algorithm configurations. We perform an extensive empirical analysis of our technique on five scenarios from propositional satisfiability, mixed-integer programming and AI planning, and show that in all of these scenarios more than 95 % of the performance gains between default configurations and optimised configurations obtained from automated configuration tools can be explained by modifying the values of a small number of parameters (1–4 in the scenarios we studied). We also investigate the use of our ablation analysis procedure for producing configurations that generalise well to previously-unseen problem domains, as well as for analysing the structure of the algorithm parameter response surface near and between high-performance configurations.
    Journal of Heuristics 01/2015; DOI:10.1007/s10732-014-9275-9
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    ABSTRACT: A heuristic framework for turbine layout optimization in a wind farm is proposed that combines ad-hoc heuristics and mixed-integer linear programming. In our framework, large-scale mixed-integer programming models are used to iteratively refine the current best solution according to the recently-proposed proximity search paradigm. Computational results on very large scale instances involving up to 20,000 potential turbine sites prove the practical viability of the overall approach.
    Journal of Heuristics 01/2015; DOI:10.1007/s10732-015-9283-4
  • Journal of Heuristics 01/2015; DOI:10.1007/s10732-014-9279-5
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    ABSTRACT: We describe VarClust, a gossip-based decentralized clustering algorithm designed to support multi-mean decentralized aggregation in energy-constrained wireless sensor networks. We empirically demonstrate that VarClust is at least as accurate as, and requires less node-to-node communication (and hence consumes less energy) than, a state-of-the-art aggregation approach, affinity propagation. This superiority holds for both the clustering and aggregation phases of inference, and is demonstrated over a range of noise levels and for a range of random and small-world graph topologies.
    Journal of Heuristics 12/2014; DOI:10.1007/s10732-014-9259-9
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    ABSTRACT: This article describes a heuristic for scheduling so-called ‘modular’ projects. Exact solutions to this NP-hard problem can be obtained with existing branch-and-bound and dynamic-programming algorithms, but only for small to medium-size instances. The proposed heuristic, by contrast, can be used even for large instances, or when instances are particularly difficult because of their characteristics, such as a low network density. The proposed heuristic draws from existing results in the literature on sequential testing, which will be briefly reviewed. The performance of the heuristic is assessed over a dataset of 360 instances.
    Journal of Heuristics 12/2014; 21(1). DOI:10.2139/ssrn.2244892
  • Journal of Heuristics 12/2014; 20(6):643-676. DOI:10.1007/s10732-014-9260-3
  • Journal of Heuristics 12/2014; 20(6):617-641. DOI:10.1007/s10732-014-9253-2
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    ABSTRACT: Protecting communication networks against failures is becoming increasingly important as they have become an integrated part of our society. Cable failures are fairly common, but it is unacceptable for a single cable failure to disconnect communication for more than a few seconds—hence protection schemes are employed. In contrast to manual intervention, automatic protection schemes such as shared backup path protection (SBPP) can recover from failure quickly and efficiently. SBPP is a simple but efficient protection scheme that can be implemented in backbone networks with technology available today. In SBPP backup paths are planned in advance for every failure scenario in order to recover from failures quickly and efficiently. Planning SBPP is an NP-hard optimization problem, and previous work confirms that it is time-consuming to solve the problem in practice using exact methods. We present heuristic algorithms and lower bound methods for the SBPP planning problem. Experimental results show that the heuristic algorithms are able to find good quality solutions in minutes. A solution gap of less than \(3.5~\%\) was achieved for 5 of 7 benchmark instances (and a gap of less than \(11~\%\) for the remaining instances.)
    Journal of Heuristics 10/2014; 20(5). DOI:10.1007/s10732-014-9248-z
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    ABSTRACT: We investigate the integrated production and distribution scheduling problem in a supply chain. The manufacturer's production environment is modeled as a parallel machine system. A single capacitated vehicle is employed to deliver products in batches to multiple customers. The scheduling problem can also be viewed as either parallel machines with delivery considerations or a flexible flowshop. Different inventory holding costs, job sizes (volume or storage space required in the transportation unit), and job priorities are taken into account. Efficient mathematical modeling and near-optimal heuristic approaches are presented for minimizing total weighted completion time.
    Journal of Heuristics 10/2014; 20(5):511-537. DOI:10.1007/s10732-014-9249-y
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    ABSTRACT: What is the minimum tour visiting all lines in a subway network? In this paper we study the problem of constructing the shortest tour visiting all lines of a city railway system. This combinatorial optimization problem has links with the classic graph circuit problems and operations research. A broad set of fast algorithms is proposed and evaluated on simulated networks and example cities of the world. We analyze the trade-off between algorithm runtime and solution quality. Time evolution of the trade-off is also captured. Then, the algorithms are tested on a range of instances with diverse features. On the basis of the algorithm performance, measured with various quality indicators, we draw conclusions on the nature of the above combinatorial problem and the tacit assumptions made while designing the algorithms.
    Journal of Heuristics 10/2014; 20(5). DOI:10.1007/s10732-014-9252-3
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    ABSTRACT: Local search algorithms play an essential role in solving large-scale combinatorial optimization problems. Traditionally, the local search procedure is guided mainly by the objective function of the problem. Hence, the greedy improvement paradigm poses the potential threat of prematurely getting trapped in low quality attraction basins. In this study, we intend to utilize the information extracted from the relaxed problem, to enhance the performance of the local search process. Considering the Lin-Kernighan-based local search (LK-search) for the p-median problem as a case study, we propose the Lagrangian relaxation Assisted Neighborhood Search (LANS). In the proposed algorithm, two new mechanisms, namely the neighborhood reduction and the redundancy detection, are developed. The two mechanisms exploit the information gathered from the relaxed problem, to avoid the search from prematurely targeting low quality directions, and to cut off the non-promising searching procedure, respectively. Extensive numerical results over the benchmark instances demonstrate that LANS performs favorably to LK-search, which is among the state-of-the-art local search algorithms for the p-median problem. Furthermore, by embedding LANS into other heuristics, the best known upper bounds over several benchmark instances could be updated. Besides, run-time distribution analysis is also employed to investigate the reason why LANS works. The findings of this study confirm that the idea of improving local search by leveraging the information induced from relaxed problem is feasible and practical, and might be generalized to a broad class of combinatorial optimization problems.
    Journal of Heuristics 10/2014; 20(5). DOI:10.1007/s10732-014-9255-0
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    ABSTRACT: In this paper, we study the problem of collective decision-making over combinatorial domains, where the set of possible alternatives is a Cartesian product of (finite) domain values for each of a given set of variables, and these variables are not preferentially independent. Due to the large alternative space, most common rules for social choice cannot be directly applied to compute a winner. In this paper, we introduce a distributed protocol for collective decision-making in combinatorial domains, which enjoys the following desirable properties: (i) the final decision chosen is guaranteed to be a Smith member; (ii) it enables distributed decision-making and works under incomplete information settings, i.e., the agents are not required to reveal their preferences explicitly; (iii) it significantly reduces the amount of dominance testings (individual outcome comparisons) that each agent needs to conduct, as well as the number of pairwise comparisons; (iv) it is sufficiently general and does not restrict the choice of preference representation languages.
    Journal of Heuristics 08/2014; 20(4):453-481. DOI:10.1007/s10732-014-9246-1
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    ABSTRACT: An extension of the capacitated vehicle routing problem is studied in this paper. In this version the difference between the individual route lengths is minimized simultaneously with the total length. The drivers’ workload and perhaps, income, may be affected by the route lengths; so adding this objective makes the problem closer to real-life than the original, single-objective problem. A heuristic based on GRASP is used to obtain an approximation of the Pareto set. The proposed heuristic is tested on instances from the literature, obtaining good approximations of the Pareto set.
    Journal of Heuristics 08/2014; 20(4):361-382. DOI:10.1007/s10732-014-9251-4
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    ABSTRACT: We propose a new population-based hybrid meta-heuristic for the periodic vehicle routing problem with time windows. This meta-heuristic is a generational genetic algorithm that uses two neighborhood-based meta-heuristics to optimize offspring. Local search methods have previously been proposed to enhance the fitness of offspring generated by crossover operators. In the proposed method, neighborhood-based meta-heuristics are used for their capacity to escape local optima, and deliver optimized and diversified solutions to the population of the next generation. Furthermore, the search performed by the neighborhood-based meta-heuristics repairs most of the constraint violations that naturally occur after the application of the crossover operators. The genetic algorithm we propose introduces two new crossover operators addressing the periodic vehicle routing problem with time windows. The two crossover operators are seeking the diversification of the exploration in the solution space from solution recombination, while simultaneously aiming not to destroy information about routes in the population as computing routes is NP-hard. Extensive numerical experiments and comparisons with all methods proposed in the literature show that the proposed methodology is highly competitive, providing new best solutions for a number of large instances.
    Journal of Heuristics 08/2014; 20(4):383-416. DOI:10.1007/s10732-014-9244-3