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

  • Journal of Heuristics 08/2015; DOI:10.1007/s10732-015-9298-x
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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 08/2015; 21(4). DOI:10.1007/s10732-015-9288-z
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    ABSTRACT: This paper presents an interactive planning system for forest road location. This decision support system is based on an interactive heuristic approach referred to as Interac- tive Large Neighborhood Search, within which the user contributes in a cooperative manner to the optimization process. The objective of this cooperative optimization process is to exploit the problem-domain expertise of the user in order to, on the one hand, guide the search for a solution towards intuitively interesting parts of the solution space, and, on the other hand, generate more practical solutions that integrate aspects of the decision problem that are not captured by the heuristic objective function. This paper more specifically pre- sents the user interface, the interaction mechanisms and the heuristic developed to support the cooperation between the computer and the user. We also present experimental results based on real problem instances, with an expert user. A comparison shows advantages for using the proposed interactive approach over a pure manual or pure automated approach.
    Journal of Heuristics 08/2015;
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    ABSTRACT: The Cableway Location Problem (CLP) is a facility location problem usually studied as a part of a hierarchical approach for large cable yarding systems outside of Europe. Small adaptable cable yarding systems are used in Europe. This increases the number of possible landing sites and makes the layout problem hard to solve to optimality. Here, two approaches are presented that solve the novel European CLP (E-CLP). The methods are tested on several generated cases and one real world case. The lateral yarding distance is introduced in the cost calculations to improve the quality of the solutions.
    Journal of Heuristics 06/2015; DOI:10.1007/s10732-015-9294-1
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    ABSTRACT: In this article we propose a hybrid genetic algorithm for the discrete \((r|p)\) -centroid problem. We consider the competitive facility location problem where two non-cooperating companies enter a market sequentially and compete for market share. The first decision maker, called the leader, wants to maximize his market share knowing that a follower will enter the same market. Thus, for evaluating a leader’s candidate solution, a corresponding follower’s subproblem needs to be solved, and the overall problem therefore is a bi-level optimization problem. This problem is \(\Sigma _2^P\) -hard, i.e., harder than any problem in NP (if \(\hbox {P}\not =\hbox {NP}\) ). A heuristic approach is employed which is based on a genetic algorithm with tabu search as local improvement procedure and a complete solution archive. The archive is used to store and convert already visited solutions in order to avoid costly unnecessary re-evaluations. Different solution evaluation methods are combined into an effective multi-level evaluation scheme. The algorithm is tested on well-known benchmark sets of both Euclidean and non-Euclidean instances as well as on larger newly created instances. Especially on the Euclidean instances our algorithm is able to exceed previous state-of-the-art heuristic approaches in solution quality and running time in most cases.
    Journal of Heuristics 06/2015; 21(3). DOI:10.1007/s10732-015-9282-5
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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 02/2015; DOI:10.1007/s10732-015-9283-4
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    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; 21(3). DOI:10.1007/s10732-015-9284-3
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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
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    ABSTRACT: In this paper we consider a job shop scheduling problem with blocking (BJSS) constraints. Blocking constraints model the absence of buffers (zero buffer), whereas in the traditional job shop scheduling model buffers have infinite capacity. There are two known variants of this problem, namely the blocking job shop scheduling with swap allowed (BWS) and the one with no swap allowed (BNS). This scheduling problem is receiving an increasing interest in the recent literature, and we propose an Iterated Greedy (IG) algorithm to solve both variants of the problem. IG is a metaheuristic based on the repetition of a destruction phase, which removes part of the solution, and a construction phase, in which a new solution is obtained by applying an underlying greedy algorithm starting from the partial solution. A comparison with recent published results shows that the iterated greedy algorithm outperforms other state-of-the-art algorithms on benchmark instances. Moreover it is conceptually easy to implement and has a broad applicability to other constrained scheduling problems.
    Journal of Heuristics 01/2015; DOI:10.1007/s10732-014-9279-5
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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: Given a graph, the critical node detection problem can be broadly defined as identifying the minimum subset of nodes such that, if these nodes were removed, some metric of graph connectivity is minimised. In this paper, two variants of the critical node detection problem are addressed. Firstly, the basic critical node detection problem where, given the maximum number of nodes that can be removed, the objective is to minimise the total number of connected nodes in the graph. Secondly, the cardinality constrained critical node detection problem where, given the maximum allowed connected graph component size, the objective is to minimise the number of nodes required to be removed to achieve this. Extensive computational experiments, using a range of sparse real-world graphs, and a comparison with previous exact results demonstrate the effectiveness of the proposed algorithms.
    Journal of Heuristics 01/2015; DOI:10.1007/s10732-015-9290-5
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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
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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: This paper investigates perturbation operators for variable neighborhood search (VNS) approaches for two related problems, namely the pickup and delivery traveling salesman problem with LIFO loading (TSPPDL) and FIFO loading (TSPPDF). Our study is motivated by the fact that previously published results on VNS approaches on the TSPPDL suggest that the perturbation operation has the most significant effect on solution quality. We propose a new perturbation operator for the TSPPDL that achieves better results on average than the existing best approach. We also devise new perturbation operators for the TSPPDF that combine request removal and request insertion operations, and investigate which combination of request removal and request insertion operations produces the best results. Our resultant VNS that employs our best perturbation operator outperforms the best existing TSPPDF approach on benchmark test data.
    Journal of Heuristics 01/2015; DOI:10.1007/s10732-015-9293-2
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    ABSTRACT: This paper presents a detailed analysis of the scalability and parallelization of Local Search algorithms for constraint-based and SAT (Boolean satisfiability) solvers. We propose a framework to estimate the parallel performance of a given algorithm by analyzing the runtime behavior of its sequential version. Indeed, by approximating the runtime distribution of the sequential process with statistical methods, the runtime behavior of the parallel process can be predicted by a model based on order statistics. We apply this approach to study the parallel performance of a constraint-based Local Search solver (Adaptive Search), two SAT Local Search solvers (namely Sparrow and CCASAT), and a propagation-based constraint solver (Gecode, with a random labeling heuristic). We compare the performance predicted by our model to actual parallel implementations of those methods using up to 384 processes. We show that the model is accurate and predicts performance close to the empirical data. Moreover, as we study different types of problems, we observe that the experimented solvers exhibit different behaviors and that their runtime distributions can be approximated by two types of distributions: exponential (shifted and non-shifted) and lognormal. Our results show that the proposed framework estimates the runtime of the parallel algorithm with an average discrepancy of 21 % w.r.t. the empirical data across all the experiments with the maximum allowed number of processors for each technique.
    Journal of Heuristics 01/2015; DOI:10.1007/s10732-015-9292-3
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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