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International Journal of Metaheuristics (4 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.
Shuffled Teaching Learning Based Algorithm for solving Robot Path Planning Problem 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.
Special Issue on: Analysis and Implementation of Nature-Inspired Algorithms
Twitter Sentiment analysis using hybrid Grey Wolf Optimizer method by Rekha Kushwaha Abstract: In recent years, the use of social media has increased excessively. A large amount of data expressing the feelings of millions of people is available on social media. Sentiment Analysis (SA) is one of the mechanisms to analyze these feelings. It is an outstanding field of data mining which concerns the recognition and interpretation of sentiments available on social media. This paper is concerned about the extraction of sentiments from the well known social media website, Twitter. On Twitter, people can express their opinions in the term of tweets. Tweets can be either a review or a comment possibly, which can be +ve, -ve or neutral. Because of the subjective behaviour of tweets, sentiments analysis is considered as a complex problem that is very difficult to deal with the available conventional strategies. In this paper, a hybrid mechanism is introduced, namely Hybrid Grey-Wolf-Optimizer with K-means clustering (GWOK) to find the optimal heads of the clusters of the available dataset. The accuracy and efficiency of the proposed mechanism are analyzed on two datasets: sander2 and twitter dataset. The obtained outcomes of the proposed mechanism are compared with some state-of-art approaches of Nature-Inspired algorithms such as Particle-Swarm algorithm, Genetic-Algorithm, Cuckoo-Search, and Differential Evolution. These outcomes prove the efficiency and accuracy of the proposed mechanism to solve the Twitter sentiment analysis problem. Keywords: Sentiment-Analysis (SA); Twitter; Nature-Inspired-Algorithm; Machine learning techniques; Optimization; K-means clustering; Grey-Wolf-Optimizer;.