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Differential Evolution (DE) is a population-based metaheuristic search algorithm that exhibits excellent performance. However, it is sensitive to mutation strategies and control parameters.To mitigate the impact of these factors, this paper proposes refining differential evolution with mutation rate and neighborhood weight local search(MRNLDE). The algorithm guides individuals in selecting an appropriate mutation mode through the mutation rate, effectively utilizing the evolutionary information inherent in the individuals themselves, thereby enhancing the search efficiency of the algorithm. Furthermore, the neighborhood individual information is utilized to re-explore the solution space, allowing for a comprehensive exploration of individual evolutionary potential and enhancing the algorithm's diversity. The performance of MRNLDE was validated under two sets of benchmark problems at the Institute of Electrical and Electronics Engineers (IEEE) Conference on Computing in Evolution (CEC), and the results show that MRNLDE performs well overall. [ABSTRACT FROM AUTHOR]

In this paper, the learning process of multilayer perceptron (MLP) neural network is boosted using hybrid metaheuristic optimization algorithms. Normally, the learning process in MLP requires suitable settings of its weight and bias parameters. In the original version of MLP, the gradient descent algorithm is used as a learner in MLP which suffers from two chronic problems: local minima and slow convergence. In this paper, six versions of memetic algorithms (MAs) are proposed to replace gradient descent learning mechanism of MLP where adaptive β -hill climbing (A β HC) as a local search algorithm is hybridized with six population-based metaheuristics which are hybrid flower pollination algorithm, hybrid salp swarm algorithm, hybrid crow search algorithm, hybrid grey wolf optimization (HGWO), hybrid particle swarm optimization, and hybrid JAYA algorithm. This is to show the effect of the proposed MA versions on the performance of MLP. To evaluate the proposed MA versions for MLP, 15 classification benchmark problems with different size and complexity are used. The A β HC algorithm is invoked in the improvement loop of any MA version with a probability of B r parameter, which is investigated to monitor its effect on the behavior of the proposed MA versions. The B r setting which obtains the most promising results is then used to set the hybrid MA. The results show that the proposed MA versions excel the original algorithms. Moreover, HGWO outperforms all other MA versions in almost all the datasets. In a nutshell, MAs are a good choice for training MLP to produce results with high accuracy. [ABSTRACT FROM AUTHOR]

The remarkable growth of cloud computing applications has caused many data centers to encounter unprecedented power consumption and heat generation. Cloud providers share their computational infrastructure through virtualization technology. The scheduler component decides which physical machine hosts the requested virtual machine. This process is virtual machine placement (VMP) which, affects the power distribution, and thereby the energy consumption of the data centers. Due to the heterogeneity and multidimensionality of resources, this task is not trivial, and many studies have tried to address this problem using different methods. However, the majority of such studies fail to consider the cooling energy, which accounts for almost 30% of the energy consumption in a data center. In this paper, we propose a metaheuristic approach based on the binary version of gravitational search algorithm to simultaneously minimize the computational and cooling energy in the VMP problem. In addition, we suggest a self-adaptive mechanism based on fuzzy logic to control the behavior of the algorithms in terms of exploitation and exploration. The simulation results illustrate that the proposed algorithm reduced energy consumption by 26% in the PlanetLab Dataset and 30% in the Google cluster dataset relative to the average of compared algorithms. The results also indicate that the proposed algorithm provides a much more thermally reliable operation. [ABSTRACT FROM AUTHOR]

Based on the algorithm structure, each metaheuristic algorithm may have its pros and cons, which may result in high performance in some problems and low functionality in some others. The idea is to hybridize two or more algorithms to cover each other's weaknesses. In this study, particle swarm optimization (PSO), simulated annealing (SA) and differential evolution (DE) are combined to develop a more powerful search algorithm. First, the temperature concept of SA is applied to balance the exploration/exploitation capability of the hybridized algorithm. Then, the DE's mutation operator is used to improve the exploration capability of the algorithm to escape the local minimums. Next, DE's mutation operator has been modified so that past experiences can be used for smarter mutations. Finally, the PSO particles' tendency to their local optimums or the global optimum, which balances the algorithm's random and greedy search, is affected by the temperature. The temperature influences the algorithm's behavior so that the random search is more significant at the beginning, and the greedy search becomes more important as the temperature is reduced. The results are compared with the basic PSO, SA, DE, cuckoo search (CS), and hybridized CS-PSO algorithm on 20 benchmark problems. The comparison reveals that, in most cases, the new algorithm outperforms others. [ABSTRACT FROM AUTHOR]

Sentiment analysis otherwise known as opinion mining is a result of the increased use of internet along with the sudden spurt of online review sites and social media. Sentiment or opinion mainly depends upon what general public think or comment and this generally includes products, services, policies and even politics where they opine either positively or negatively, and the opinion is shared by users of a particular product or service. The amount of data generated is huge and analysis of the same falls under the category of big data. In general, opinions are fuzzy in nature and extracting the true sentiment involves many challenges and requires effective methods in extracting and summarizing people's views. Fuzzy logic helps in classifying sentiments with correct strength designated to every opinion level which in turn increases the accuracy of classification. Shuffled frog leaping algorithm (SFLA) denotes a metaheuristic mechanism of optimization which imitates the mimetic evolutionary activity of frogs searching for a place which possesses most quantity of food. In the current work a hybrid SFLA is utilized for sentiment analysis alongside 2-OPT local search algorithm, for the purpose of reviewing books. The proposed method is deployed in cloud using MapReduce. Outcomes from experiments show the efficacy of the suggested technique. [ABSTRACT FROM AUTHOR]