11,158 research outputs found
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
In this paper we propose a crossover operator for evolutionary algorithms
with real values that is based on the statistical theory of population
distributions. The operator is based on the theoretical distribution of the
values of the genes of the best individuals in the population. The proposed
operator takes into account the localization and dispersion features of the
best individuals of the population with the objective that these features would
be inherited by the offspring. Our aim is the optimization of the balance
between exploration and exploitation in the search process. In order to test
the efficiency and robustness of this crossover, we have used a set of
functions to be optimized with regard to different criteria, such as,
multimodality, separability, regularity and epistasis. With this set of
functions we can extract conclusions in function of the problem at hand. We
analyze the results using ANOVA and multiple comparison statistical tests. As
an example of how our crossover can be used to solve artificial intelligence
problems, we have applied the proposed model to the problem of obtaining the
weight of each network in a ensemble of neural networks. The results obtained
are above the performance of standard methods
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
On the Benefits of Inoculation, an Example in Train Scheduling
The local reconstruction of a railway schedule following a small perturbation
of the traffic, seeking minimization of the total accumulated delay, is a very
difficult and tightly constrained combinatorial problem. Notoriously enough,
the railway company's public image degrades proportionally to the amount of
daily delays, and the same goes for its profit! This paper describes an
inoculation procedure which greatly enhances an evolutionary algorithm for
train re-scheduling. The procedure consists in building the initial population
around a pre-computed solution based on problem-related information available
beforehand. The optimization is performed by adapting times of departure and
arrival, as well as allocation of tracks, for each train at each station. This
is achieved by a permutation-based evolutionary algorithm that relies on a
semi-greedy heuristic scheduler to gradually reconstruct the schedule by
inserting trains one after another. Experimental results are presented on
various instances of a large real-world case involving around 500 trains and
more than 1 million constraints. In terms of competition with commercial math
ematical programming tool ILOG CPLEX, it appears that within a large class of
instances, excluding trivial instances as well as too difficult ones, and with
very few exceptions, a clever initialization turns an encouraging failure into
a clear-cut success auguring of substantial financial savings
A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization
In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is
proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization
problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy
with a satisfactory trade-off between exploration and exploitation capabilities was added to the
model predictive control. The proposed strategy was evaluated using a representative microgrid that
includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage
system. The achieved results demonstrate the validity of the proposed approach, outperforming
a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost.
In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409
Fuzzy heterogeneous neurons for imprecise classification problems
In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing missing information and uncertainty. In this paper, a general class of neuron models accepting heterogeneous inputs in the form of mixtures of continuous (crisp and/or fuzzy) and discrete quantities admitting missing data is presented. From these, several particular models can be derived as instances and different neural architectures constructed with them. Such models deal in a natural way with problems for which information is imprecise or even missing. Their possibilities in classification and diagnostic problems are here illustrated by experiments with data from a real-world domain in the field of environmental studies. These experiments show that such neurons can both learn and classify complex data very effectively in the presence of uncertain information.Peer ReviewedPostprint (author's final draft
A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels
In this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ‘real-life’ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals
Radial Distribution Network Topology Optimization Using Genetic Algorithms Considering Uncertain Load and Distributed Generation
This paper aims to study distribution network topology optimization considering uncertain load and distributed generation. Gradual increase of distributed generation in distribution network leads the network operator companies to concern more about having the best network topology, so their costs can be the lowest. MATLABTM genetic algorithms function is used to model this mathematical problem in its basic definition. A stochastic multi-objective programming algorithm is implemented and a decision maker applied to choose the best solution of non-dominated solutions set found.info:eu-repo/semantics/publishedVersio
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