Forthcoming articles

International Journal of Metaheuristics

International Journal of Metaheuristics (IJMHeur)

These 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 Articles will not be published until the final proofs are validated by their authors.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 (3 papers in press)

Regular Issues

  • 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.

  • Solving high dimensional multimodal continuous optimization problems using hybridization between particle swarm optimization variants   Order a copy of this article
    by Hugo Deschenes, Caroline Gagne 
    Abstract: This paper presents a comparison between three new hybridizations using three Particle Swarm Optimization (PSO) variants: The Barebones PSO (BPSO), the Comprehensive Learning PSO (CLPSO) and the Cooperative Learning PSO (CoLPSO). The goal of these hybridizations is to improve the exploration and the exploitation of the search space from these three variants and contributes to PSO on high scale continuous optimization problems. The performance of these three new hybrids, named HCLBPSO-Half, HBPSO+CL and HCoCLPSO, are compared with the original methods on which they are based. The comparison is done using 6 classical continuous optimization functions with dimensions set to 50, 100 and 200, and all 15 continuous optimization functions from the CEC15 benchmark with dimensions set to 10, 30, 50 and 100. The results are compared using the mean and median of executions.
    Keywords: metaheuristics; continuous optimization; particle swarm optimization; hybridization; variants; high dimensional problems.

  • Shuffled Teaching Learning Based Algorithm for solving Robot Path Planning Problem   Order a copy of this article
    by Geetanjali Singh, Nirmala Sharma, Harish Sharma 
    Abstract: To evade the big and destructive obstacles in the real world scenario, such as bomb blast, nuclear activities, and fire breakdowns, robots are necessary. Robot Path Planning (RPP) problem (finding the optimal traveling path between source to destination) is one of the interesting NP-hard problems in the world of robotics. The RPP problem can be dealt with, using swarm intelligence (SI) based optimization algorithms. Teaching Learning Based Optimization (TLBO) algorithm is a very efficient and reliable swarm intelligence based algorithm in the history of optimization. This paper proposed a hybridized version of TLBO with shuffled frog leaping algorithm (SFLA) to improve the efficiency in terms of exploitation and to overcome the slow convergence rate. The proposed variant is named as Shuffled Teaching Learning Based Optimization (STLBO) algorithm. For checking the efficiency and accuracy of the proposed STLBO, it is applied to 12 continuous benchmark functions and compared with different nature-inspired algorithms (NIA), namely TLBO, SFLA, particle swarm optimization (PSO), gravitational search algorithm (GSA), covariance matrix adaptation evolution strategy (CMAES), and biogeography based optimization (BBO). To check the robustness of the propounded STLBO, it is implemented to solve the problem of path planning of the robots starting from the source node to the destination node. Through simulation results and statistical analyses, the effectiveness of the proposed STLBO is proved in the field of SI based algorithms.
    Keywords: Teaching learning based optimization; Shuffled frog leaping algorithm; Robot path planning; Swarm intelligence based algorithm; Optimization.