Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Service Networks Monitoring for better Quality of Service

6 figures, 2tables, ICIST2015. 5th International Conference on Information Society and Techology, ICIST 2015International audienceToday, the deployment of Web services in many enterprise applications has gained much attention. Service network inhibits certain common properties as they arise spontaneously and are subject to high fluctuation. The objective of consumer is to compose services for stable business processes in coherence with their legacy system capabilities and with better quality of services. For this purpose we have proposed a dynamic decision model that integrates several performance metrics and attributes to monitor the performance of service oriented systems in order to ensure their sustainability. Based on the available metrics, we have identified performance metrics criteria and classified into categories like time based QoS, size based QoS, combined QoS and estimated attributes. Then we have designed service network monitoring ontology (SNM). Our decision model will take user query and SNM as input, measures the performance capabilities and suggests some new performance configurations like selected service is not available, physical resource is not available and no maintenance will be available for the selected service for composition

Service Networks Monitoring for better Quality of Service

International audienceToday, the deployment of Web services in many enterprise applications has gained much attention. Service network inhibits certain common properties as they arise spontaneously and are subject to high fluctuation. The objective of consumer is to compose services for stable business processes in coherence with their legacy system capabilities and with better quality of services. For this purpose we have proposed a dynamic decision model that integrates several performance metrics and attributes to monitor the performance of service oriented systems in order to ensure their sustainability. Based on the available metrics, we have identified performance metrics criteria and classified into categories like time based QoS, size based QoS, combined QoS and estimated attributes. Then we have designed service network monitoring ontology (SNM). Our decision model will take user query and SNM as input, measures the performance capabilities and suggests some new performance configurations like selected service is not available, physical resource is not available and no maintenance will be available for the selected service for composition