Forthcoming and Online First Articles

International Journal of Metaheuristics

International Journal of Metaheuristics (IJMHeur)

Forthcoming articles have been peer-reviewed and accepted for publication 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 Articles will not be published until the final proofs are validated by their authors.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 (2 papers in press)

Regular Issues

  • Testing initial solutions in a variable neighbourhood search for binary integer programming problems   Order a copy of this article
    by Håkon Bentsen, Alexandr Reznik, Lars Magnus Hvattum 
    Abstract: Local-search-based heuristics modify a current solution by making small changes. Hence, especially in early phases of the search, the initial solution can play an important role. This is tested in the context of a general variable neighbourhood search for solving binary integer programming problems. A basic version of the search starts from a randomly selected solution. For comparison, two simple construction heuristics are developed. Both select variables greedily, but one is adaptive, meaning that the metric is recalculated after every assignment, whereas the other is static. In addition, a recently developed feasibility jump heuristic is used. Results indicate that the initial solution has an effect on the quality of the solutions obtained early in the search, but as runs become longer, the effect of the initial solutions diminishes and eventually disappears. However, this only holds for instances where it is not too difficult to find feasible solutions.
    Keywords: 0-1 integer programming; construction heuristic; greedy; feasibility jump; local search.
    DOI: 10.1504/IJMHEUR.2024.10066097
     
  • A multi-objective model search algorithm for linear regression   Order a copy of this article
    by Anas Mifrani, Philippe Saint-Pierre, Nicolas Savy 
    Abstract: Inherent in model selection is the problem of simultaneously optimising multiple performance metrics. Some of these metrics express potentially conflicting criteria, like accuracy and simplicity. Pareto optimisation is a branch of mathematical optimisation dealing with problems involving conflicting objectives. In this article, an algorithm was developed that searches for Pareto optimal linear regression models given a dataset and a set of performance metrics. The optimisation task was framed as one of sequential variable selection on a graph. A search strategy was proposed that draws on ant colony optimisation. Experiments were run in which the metrics to be minimised were the root-mean-square error, expressing accuracy, and the number of coefficients, expressing simplicity. Cases were presented in which the algorithm outperformed Akaike information criterion-based stepwise regression. Results suggested that our algorithm copes well with small datasets and correlated predictors. Key properties of our algorithm were discussed and areas of improvement highlighted.
    Keywords: model selection; linear regression; parsimony; prediction error; multi-objective optimisation; ant colony optimisation; Pareto front; trade-off; Akaike information criterion; AIC; stepwise regression.
    DOI: 10.1504/IJMHEUR.2025.10071205
     

 

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