Forthcoming articles


International Journal of Metaheuristics


These articles have been peer-reviewed and accepted for publication in IJMHeur, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.


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International Journal of Metaheuristics (3 papers in press)


Regular Issues


  • Solution attractor of local search in traveling salesman problem (part 2): computational study   Order a copy of this article
    by Weiqi Li, Xue Li 
    Abstract: This paper is the second part of our study. In the first part, we introduce the concept of solution attractor of local search system for the Traveling Salesman Problem (TSP), describe a procedure for constructing the solution attractor, and present an attractor-based search system to solve the dynamic multi-objective TSP. In this paper, we report the results of our recent empirical study on some important properties of the solution attractor of local search system for the TSP. These properties include the nature of convergence of local search trajectories, the size of the constructed solution attractor, the relationship between the size of the problem and the size of the constructed solution attractor, the best tour in the solution attractor, and computational complexity in the attractor-based search system.
    Keywords: traveling salesman problem; global optimization; analysis of heuristics; convergence of local search; solution attractor.

  • A Structural Taxonomy for Metaheuristic Optimization Search Methods   Order a copy of this article
    by Raymond R. Hill, Edward Pohl 
    Abstract: Metaheuristic search algorithms have become ubiquitous in the applied optimization world. Various works have appeared classifying and improving these algorithms and the particular processes embedded within the algorithms. Successful metaheuristic approaches have a common general structure to their search processes. To this end, we offer a structural taxonomy of metaheuristic search methods. This taxonomy serves as a framework for constructing and evaluating metaheuristic approaches from a general structural perspective as well as for conducting empirical research regarding the effectiveness of more detailed structural components. Implementation mechanisms of the detailed components within each structural component is left for future taxonomy research and development.
    Keywords: heuristic optimization; taxonomy; metaheuristics; intensification; diversification; adaptive memory.

Special Issue on: Randomised Heuristics for Communication Networks

  • A Tabu Search Approach for a Virtual Networks Splitting Strategy Across Multiple Cloud Providers   Order a copy of this article
    by Marieme Diallo, Alejandro Quintero, Samuel Pierre 
    Abstract: This paper addresses the problem of computational and networking resources embedding across multiple independent cloud providers (CPs). We focus on the splitting phase problem by proposing a virtual network requests (VNRs) splitting strategy, which aims at improving the performance and the quality of service (QoS) of resulting mapped VNR segments. We formalize our splitting strategy as a mathematical maximization problem with constraints by using an Integer Linear Program (ILP). Since the VNRs splitting process is classified as an NP-hard problem, we propose a metaheuristic approach based on the Tabu Search (TS), in order to find good feasible solutions in polynomial solving time. The simulations results obtained show the efficiency of the proposed algorithm, in comparison with the exact method and an other baseline approach. Solution costs are on average close to the upper bounds, with an average gap ranging from 0% to a maximum of 2.97%, performed in a highly reduced computing time.
    Keywords: Cloud computing; virtualized network infrastructures; resource splitting; optimization; metaheuristics; Tabu Search.