4,576 research outputs found
Triggered memory-based swarm optimization in dynamic environments
This is a post-print version of this article - Copyright @ 2007 Springer-VerlagIn recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a triggered memory generator. Experimental study over a benchmark dynamic problem shows that the triggered memory-based particle swarm optimization algorithm has stronger robustness and adaptability than traditional particle swarm optimization algorithms, both with and without traditional memory scheme, for dynamic optimization problems
Smart Microgrids: Overview and Outlook
The idea of changing our energy system from a hierarchical design into a set
of nearly independent microgrids becomes feasible with the availability of
small renewable energy generators. The smart microgrid concept comes with
several challenges in research and engineering targeting load balancing,
pricing, consumer integration and home automation. In this paper we first
provide an overview on these challenges and present approaches that target the
problems identified. While there exist promising algorithms for the particular
field, we see a missing integration which specifically targets smart
microgrids. Therefore, we propose an architecture that integrates the presented
approaches and defines interfaces between the identified components such as
generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid
Worksho
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Multi-population methods with adaptive mutation for multi-modal optimization problems
open access journalThis paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity, the multi-population technique can be applied to maintain the diversity in the population and the convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive mutation operator, which determines two different mutation probabilities for different sites of the solutions. The probabilities are updated by the fitness and distribution of solutions in the search space during the evolution process. The experimental results demonstrate the performance of the proposed algorithm based on a set of benchmark problems in comparison with relevant algorithms
Comparison of Evolutionary Optimization Algorithms for FM-TV Broadcasting Antenna Array Null Filling
Broadcasting antenna array null filling is a very
challenging problem for antenna design optimization. This paper
compares five antenna design optimization algorithms (Differential
Evolution, Particle Swarm, Taguchi, Invasive Weed, Adaptive
Invasive Weed) as solutions to the antenna array null filling
problem. The algorithms compared are evolutionary algorithms
which use mechanisms inspired by biological evolution, such as
reproduction, mutation, recombination, and selection. The focus of
the comparison is given to the algorithm with the best results,
nevertheless, it becomes obvious that the algorithm which produces
the best fitness (Invasive Weed Optimization) requires very
substantial computational resources due to its random search
nature
Parameter Optimisation of a Virtual Synchronous Machine in a Microgrid
Parameters of a virtual synchronous machine in a small microgrid are
optimised. The dynamical behaviour of the system is simulated after a
perturbation, where the system needs to return to its steady state. The cost
functional evaluates the system behaviour for different parameters. This
functional is minimised by Parallel Tempering. Two perturbation scenarios are
investigated and the resulting optimal parameters agree with analytical
predictions. Dependent on the focus of the optimisation different optima are
obtained for each perturbation scenario. During the transient the system leaves
the allowed voltage and frequency bands only for a short time if the
perturbation is within a certain range.Comment: 17 pages, 5 figure
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