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Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective(More)
The proposed novel Genetic Bees Algorithm (GBA) is an enhancement to the swarm-based Bees Algorithm (BA). It is called the Genetic Bees Algorithm because it has genetic operators. The structure of the GBA compared to the basic BA has two extra components namely, a Reinforced Global Search and a Jumping Function. The main advantage of adding the genetic(More)
Smart scheduling of energy consuming devices in the domestic sector should factor in clean energy generation potential, electricity tariffs, and occupants’ behaviour (i.e. interactions with their appliances). The paper presents an ANN–GA (Artificial Neural Network / Genetic Algorithm) smart appliance scheduling approach for optimized energy management in(More)
A supply chain is a complex network which involves the products, services and information flows between suppliers and customers. A typical supply chain is composed of different levels, hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between the multi-objectives such as(More)
This paper addresses the endemic problem of the gap between predicted and actual energy performance in public buildings. A system engineering approach is used to characterize energy performance factoring in building intrinsic properties, occupancy patterns, environmental conditions, as well as available control variables and their respective ranges. Due to(More)
In this paper, an enhanced version of the Bees Algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of a given supply chain problem in order to minimise the total cost and the total lead-time. The new Bees Algorithm includes an adaptive neighbourhood size change and site abandonment (ANSSA) strategy(More)
Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating,(More)
Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects Baris Yuce, Ernesto Mastrocinque, Michael Sylvester Packianather, Duc Pham, Alfredo Lambiase & Fabio Fruggiero a Institute of Sustainable Engineering, Cardiff University, Newport Road, The Parade, Queen’s Building, Cardiff(More)
A HPC based cloud model for real-time energy optimisation Ioan Petri, Haijiang Li, Yacine Rezgui, Yang Chunfeng, Baris Yuce & Bejay Jayan To cite this article: Ioan Petri, Haijiang Li, Yacine Rezgui, Yang Chunfeng, Baris Yuce & Bejay Jayan (2016) A HPC based cloud model for real-time energy optimisation, Enterprise Information Systems, 10:1, 108-128, DOI:(More)
Power distribution network management must integrate with demand side management, alongside distributed energy resources, in order to meet sustainability, resilience, and economic challenges through a smart grid approach. This paper presents an implementation of the Universal Smart Energy Framework (USEF) through a multiagent system and a novel semantic web(More)