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

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
    • 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: 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: Community detection is one of the most important problems in the field of complex networks in recent years. The majority of present algorithms only find disjoint communities, however, community often overlap to some extent in many real-world networks. In this paper, an improved multi-objective quantum-behaved particle swarm optimization (IMOQPSO) based on spectral-clustering is proposed to detect the overlapping community structure in complex networks. Firstly, the line graph of the graph modeling the network is formed, and a spectral method is employed to extract the spectral information of the line graph. Secondly, IMOQPSO is employed to solve the multi-objective optimization problem so as to resolve the separated community structure in the line graph which corresponding to the overlapping community structure in the graph presenting the network. Finally, a fine-tuning strategy is adopted to improve the accuracy of community detection. The experiments on both synthetic and real-world networks demonstrate our method achieves cover results which fit the real situation in an even better fashion.
    Journal of Heuristics 01/2015; DOI:10.1007/s10732-015-9289-y
  • Journal of Heuristics 01/2015; DOI:10.1007/s10732-015-9282-5
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    ABSTRACT: The b-chromatic number of a graph \(G\) is a maximum integer \(\varphi (G)\) for which there exists a proper \(\varphi (G)\) -coloring with the additional property that each color class contains a vertex that is adjacent to one of the vertices within each color class. In contrast to many theoretical results discovered over the last decade and a half there are no computer running experiments on \(\varphi (G)\) in the literature. This work presents a hybrid evolutionary algorithm for graph b-coloring. Its performance has been tested on some instances of regular graphs where their b-chromatic number is theoretically known in advance, as well as by comparing with a brute force algorithm solving the regular graphs up to 12 vertices. In addition, the algorithm has been tested on some larger graphs taken from a DIMACS challenge benchmark that also proved to be challenging for the algorithms searching for the classical chromatic number \(\chi (G)\) .
    Journal of Heuristics 01/2015; DOI:10.1007/s10732-015-9288-z
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    ABSTRACT: Digital cameras are equipped with passive autofocus mechanisms where a lens is focused using only the camera’s optical system and an algorithm for controlling the lens. The speed and accuracy of the autofocus algorithm are crucial to user satisfaction. In this paper, we address the problems of identifying the global optimum and significant local optima (or peaks) when focusing an image. We show that supervised machine learning techniques can be used to construct a passive autofocus heuristic for these problems that out-performs an existing hand-crafted heuristic and other baseline methods. In our approach, training and test data were produced using an offline simulation on a suite of 25 benchmarks and correctly labeled in a semi-automated manner. A decision tree learning algorithm was then used to induce an autofocus heuristic from the data. The automatically constructed machine-learning-based (ml-based) heuristic was compared against a previously proposed hand-crafted heuristic for autofocusing and other baseline methods. In our experiments, the ml-based heuristic had improved speed—reducing the number of iterations needed to focus by 37.9 % on average in common photography settings and 22.9 % on average in a more difficult focus stacking setting—while maintaining accuracy.
    Journal of Heuristics 01/2015; DOI:10.1007/s10732-015-9291-4
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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
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    ABSTRACT: The vehicle routing problem with stochastic demands (VRPSD) consists in designing optimal routes to serve a set of customers with random demands following known probability distributions. Because of demand uncertainty, a vehicle may arrive at a customer without enough capacity to satisfy its demand and may need to apply a recourse to recover the route’s feasibility. Although travel times are assumed to be deterministic, because of eventual recourses the total duration of a route is a random variable. We present two strategies to deal with route-duration constraints in the VRPSD. In the first, the duration constraints are handled as chance constraints, meaning that for each route, the probability of exceeding the maximum duration must be lower than a given threshold. In the second, violations to the duration constraint are penalized in the objective function. To solve the resulting problem, we propose a greedy randomized adaptive search procedure (GRASP) enhanced with heuristic concentration (HC). The GRASP component uses a set of randomized route-first, cluster-second heuristics to generate starting solutions and a variable-neighborhood descent procedure for the local search phase. The HC component assembles the final solution from the set of all routes found in the local optima reached by the GRASP. For each strategy, we discuss extensive computational experiments that analyze the impact of route-duration constraints on the VRPSD. In addition, we report state-of-the-art solutions for a established set of benchmarks for the classical VRPSD.
    Journal of Heuristics 01/2015; DOI:10.1007/s10732-015-9281-6
  • 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; 21(2). 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: 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