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Showing 1 to 20 of 37 for “"Particle swarm optimisation"”.
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Multi-objective particle swarm optimisation: methods and applications.
Solving real life optimisation problems is a challenging engineering venture. Since the early days of research on optimisation it was realised that many problems do not simply have one optimisation objective. This led to the development of multi-objective optimizers that try to look at the …
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Set-based Particle Swarm Optimisation for Dynamic Optimisation Problems
Many real-world optimisation problems are inherently dynamic, defined by changes in their underlying properties over time. Real-world problems also frequently require optimisation over discrete-valued decision variables. However, the solution of problems that are simultaneously dynamic and …
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Self-limitation, dynamic and flexible approaches for particle swarm optimisation
Swarm Intelligence (SI) is one of the prominent techniques employed to solve optimisation problems. It has been applied to problems pertaining to engineering, schedule, planning, networking and design. However, this technique has two main limitations. First, the SI technique may not be suitable for …
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An Evaluation of Performance Enhancements to Particle Swarm Optimisation on Real-World Data
Swarm Computation is a relatively new optimisation paradigm. The basic premise is to model the collective behaviour of self-organised natural phenomena such as swarms, flocks and shoals, in order to solve optimisation problems. Particle Swarm Optimisation (PSO) is a type of swarm computation …
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The use of prospect theory framework in constrained multi-objective particle swarm optimisation
… as constraintsingle or multiple objective optimisation problems with constraints. Particle swarmoptimisation (PSO) is a population-based stochastic algorithm has been shown to be aneffective optimisation method for solving these types of problems since it is capable ofgenerating random …
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Short term load forecasting based on hybrid artificial neural networks and particle swarm optimisation
… Fuzzy Logic (FL), Expert Systems (ES), and Particle Swarm Optimisation (PSO). Hybrid versions of these methods, where two or more CI methods are amalgamated in a process to forecast future load, have also been used. iv In this research, a traditional forecasting technique, Multiple Linear …
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Exergy Based SI Engine Model Optimisation. Exergy Based Simulation and Modelling of Bi-fuel SI Engine for Optimisation of Equivalence Ratio and Ignition Time Using Artificial Neural Network (ANN) Emulation and Particle Swarm Optimisation (PSO).
In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the …
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Markerless multiple-view human motion analysis using swarm optimisation and subspace learning
… multiple-view studio-based video sequences using particle swarm optimisation and charting, a subspace learning technique.In our first framework, we formulate, and perform, human motion tracking as a multi-dimensional non-linear optimisation problem, solved using particle swarm optimisation (PSO), …
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Energy-efficient data gathering and aggregation for wireless sensor networks.
… consisting of the setup phase, the routing tree optimisation phase and the data gathering phase. The setup phase is to build initial routing trees by the ant colony optimisation algorithm which is executed between the base station and all sensor nodes. A key to our routing scheme is the routing …
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A modified flower pollination algorithm and carnivorous plant algorithm for solving engineering optimization problem
… pollination algorithm (FPA) is a biomimicry optimisation algorithm inspired by natural pollination. Although FPA has shown better convergence than particle swarm optimisation and genetic algorithm in the pioneering study, improving the convergence characteristic of FPA still needs more work. …
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Multi-objective optimisation using sharing in swarm optimisation algorithms
… with the help of evolutionary computation. Particle Swarm Optimisation (PSO) is a relatively new heuristic that shares some similarities with evolutionary computation techniques, and that recently has been successfully modified to solve multi-objective optimisation problems. In this thesis …
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Stand-alone solar-pv hydrogen energy systems incorporating reverse osmosis
… between applying (simplistic) predictive/optimisation techniques compared to intelligent tools in renewable energy systems. This is achieved through applying intelligent tools such as Neural Networks and Particle Swarm Optimisation for different aspects that govern system design and …
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Design and modelling of beam steering antenna array for mobile and wireless applications using optimisation algorithms. Simulation and measrement of switch and phase shifter for beam steering antenna array by applying reactive loading and time modulated switching techniques, optimised using genetic algorithms and particle swarm methods.
… using the genetic algorithm (GA) and particle swarm optimisation (PSO) techniques as optimisation design tools. Several antenna designs were implemented and tested: initially, a printed dipole antenna integrated with a duplex RF switch used for mobile base station antenna beam steering …
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Advanced computational techniques for pipe burst detection and localisation in water distribution networks
… a comparative analysis of hyperparameter optimisation techniques, Particle Swarm Optimisation (PSO) and Population-Based Training (PBT), for FL-DL architectures. This investigation addresses the limitations of traditional detection methods, which are often costly, labour-intensive, and …
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Characteristics of Multilayer Mirrors in the X-ray and Extreme Ultraviolet Radiation Ranges
… beamsplitters and order-selecting mirror. The particle swarm optimisation method was successfully applied in a global search of materials for the structure of mirrors. The robust designs made possible are considered to alleviate fabrication difficulties, such as control of layer thickness …
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An intelligent manufacturing system for heat treatment scheduling
… scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) …
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Reinforcement learning based control for arrays of point absorber wave energy converters
… termed hyperparameters. It was shown that particle swarm optimisation could be used for the selection of hyperparameters. Point absorbers can be deployed in arrays. Array models were developed to evaluate Q-learning for the control of multiple point absorbers. A characteristic of the array …
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Baldwinian-based meta-heuristic for robust engineering optimisation
… research was to identify problems in engineering optimisation and then to develop novel solutions to the identified problems. First, principles of computational optimisation were studied and a literature review was conducted. It emerged that the latest research in the area of automated engineering …
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State switching in multi-stable systems: control and optimisation.
… simulation on the test systems. Moreover, optimisation of the controller (based on multiple performance objectives) can further improve system performance. Two performance objectives - maximum peak of control input and switching duration - are adopted in optimising the proposed PD-like …
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Network-level maintenance decisions for flexible pavement using a soft computing-based framework
… classification; performance prediction; and optimisation for decision-making. For section classification, this research presents a fuzzy inference system (FIS), with appropriate membership functions for section classifications and for calculating the pavement condition index (PCI). The …
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