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International Journal of Metaheuristics (2 papers in press)
A Tabu Search Approach for a Virtual Networks Splitting Strategy Across Multiple Cloud Providers 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 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.