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Showing 1 to 20 of 210 for “"Particle swarm"”.
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Dual Satellite Coverage using Particle Swarm Optimization
… analysis of the optimal solutions generated by a Particle Swarm Optimization method is completed using a cost function with different weights for the time, fuel, and angle terms. Three different scenarios are presented: a single burn case, a double burn case, and a four burn case. The results are …
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Multi-objective particle swarm optimisation: methods and applications.
… in the eld of multi-objective optimisation and particle swarm optimisation raising several challenges. This is tackled from di erent aspects including the proposal of new archiving techniques to developing new methods and quality measures. Smart Multi-objective Particle Swarm Optimisation based …
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Experimental analysis on the operation of Particle Swarm Optimization
In Particle Swarm Optimization, it has been observed that swarms often stall as opposed to converge. A stall occurs when all of the forward progress that could occur is instead rejected as Failed Exploration. Since the swarms particles are in good regions of the search space with the potential to …
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Set-based Particle Swarm Optimisation for Dynamic Optimisation Problems
… thesis investigates the application of set-based particle swarm optimisation (SBPSO) to dynamic combinatorial optimisation problems and dynamic multivariate regression problems, which involve bilevel optimisation over both continuous and discrete domains. After a review of relevant optimisation …
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Intelligent Processing in Wireless Communications Using Particle Swarm Based Methods
… either. This dissertation presents a series of Particle Swarm Optimization (PSO) based search and optimization algorithms that solve open research and design problems in wireless communications. These problems are either avoided or solved approximately before.</p> <p>PSO is a bottom-up approach …
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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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Particle swarm optimization applications to mpeg-4 transmission over ZigBee
… an artificial intelligence technique known as Particle Swann Optimization (PSO) has becoming very popular. PSO is a population-based stochastic optimization technique, inspired by the social behavior of flocks of birds, and colonies of ants and bees. Such intelligence is decentralized, …
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Application of particle swarm optimization in adaptive self-interference cancellation
This thesis investigates the application of particle swarm optimization in self-interference cancellation. To achieve cancellation, the receiver has to differentiate between the transmit and receive signal. The transmit signal is already known at the transmitting side but it undergoes some …
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An improved data classification framework based on fractional particle swarm optimization
Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high …
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Dynamic electronic asset allocation comparing genetic algorithm with particle swarm optimization
… 2) Comparing performance of Genetic Algorithm to Particle Swarm Optimization (PSO) algorithm. This research problem implemented Genetic Algorithm in C++ and used QT Data Visualization for displaying three-dimensional space, pheromone, and Terrain. The Genetic algorithm implementation maintained …
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Optimization-based mechanism synthesis using multi-objective parallel asynchronous particle swarm optimization
A distributed variant of multi-objective particle swarm optimization (MOPSO) called multi-objective parallel asynchronous particle swarm optimization (MOPAPSO) is presented, and the effects of distribution of objective function calculations to slave processors on the results and performance are …
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Identifying near-earth asteroid targets for human exploration using particle swarm optimization
… to help identify these targets is the particle swarm optimization (PSO) technique, a metaheuristic swarming algorithm. The optimizer that was developed minimizes the total mission delta-V, given a particular epoch date, whilst optimizing four time parameters, namely, the ideal time to …
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Optimization of relative orbit transfers via particle swarm and primer vector theory
… within the cluster is a difficult task. The particle swarm evolutionary algorithm is used to find ways to minimize the amount of fuel required for impulsive transfers between periodic orbits in the relative motion problem. Relative orbit elements are used in order to create the solution …
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Stochastic Approximation Algorithms With Applications To Particle Swarm Optimization, Adaptive Optimization, And Consensus
… 2, we use stochastic approximation to analyze Particle Swarm Optimization (PSO) algorithm. We introduce four coefficients and rewrite the PSO procedure as a stochastic approximation type iterative algorithm. Then we analyze its convergence using weak convergence method. It is proved that a …
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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
… 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 multi-start points, it is simple to perform …
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Design Validation of RTL Circuits using Binary Particle Swarm Optimization and Symbolic Execution
Over the last two decades, chip design has been conducted at the register transfer (RT) Level using Hardware Descriptive Languages (HDL), such as VHDL and Verilog. The modeling at the behavioral level not only allows for better representation and understanding of the design, but also allows for …
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Efficient Merging and Decomposition Variants of Cooperative Particle Swarm Optimization for Large Scale Problems
… due to insufficient grouping strategies. Various particle swarm optimization (PSO) techniques have been proposed in order to address these LSOPs, either through the improvement of search operators or utilizing decomposition. However, there is a lack of comparison between them showing which PSO …
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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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Particle swarm optimization for dynamically changing environments with particular focus on scalability and switching cost
… All the proposed methods in this thesis use particle swarm optimization as the core optimizer embedded in a multi-population framework. The performance of the proposed methods are compared with state-of-the-art methods on a wide range of problem instances generated by the state-of-the-art and …
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