25 research outputs found
The Genetic Flock Algorithm
AbstractThe purpose of this paper is to describe and evaluate a new algorithm for optimization. The new algorithm is named the Genetic Flock Algorithm. This algorithm is a type of hybrid of a Genetic Algorithm and a Particle Swarm Optimization Algorithm. The paper discusses strengths and weaknesses of these two algorithms. It then explains how the Genetic Flock Algorithm combines features of both and gives details of the algorithm. All three algorithms are compared using eight standard optimization problems that are used in the literature. It is shown that the Genetic Flock Algorithm provides superior performance on 75% of the tested cases. In the remaining 25% of the cases it outperforms either the Genetic Algorithm or the Particle Swarm Optimization Algorithm; it is never worse than both. Possible future improvements to the Genetic Flock Algorithm are briefly described
Solving the extended vehicle scheduling problem with metaheuristics
Mestrado Integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201
Effect of Spatial Locality on an Evolutionary Algorithm for Multimodal Optimization
Abstract. To explore the effect of spatial locality, crowding differential evolu-tion is incorporated with spatial locality for multimodal optimization. Instead of random trial vector generations, it takes advantages of spatial locality to generate fitter trial vectors. Experiments were conducted to compare the proposed algo-rithm (CrowdingDE-L) with the state-of-the-art algorithms. Further experiments were also conducted on a real world problem. The experimental results indicate that CrowdingDE-L has a competitive edge over the other algorithms tested.
Analysing knowledge transfer in SHADE via complex network
In this research paper a hybridization of two computational intelligence fields, which are evolutionary computation techniques and complex networks (CNs), is presented. During the optimization run of the success-history based adaptive differential evolution (SHADE) a CN is built and its feature, node degree centrality, is extracted for each node. Nodes represent here the individual solutions from the SHADE population. Edges in the network mirror the knowledge transfer between individuals in SHADE's population, and therefore, the node degree centrality can be used to measure knowledge transfer capabilities of each individual. The correlation between individual's quality and its knowledge transfer capability is recorded and analyzed on the CEC2015 benchmark set in three different dimensionality settings-10D, 30D and 50D. Results of the analysis are discussed, and possible directions for future research are suggested.Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science & Technology), Improving Applicability of NatureInspired Optimisation by Joining Theory and Practice (ImAppNIO) [CA15140]; COST (European Cooperation in Science & Technology), HighPerformance Modelling and Simulation for Big Data Applications (cHiPSet) [IC1406]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089
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
Adaptive Search and Constraint Optimisation in Engineering Design
The dissertation presents the investigation and development of novel adaptive
computational techniques that provide a high level of performance when searching
complex high-dimensional design spaces characterised by heavy non-linear constraint
requirements. The objective is to develop a set of adaptive search engines that will allow
the successful negotiation of such spaces to provide the design engineer with feasible high
performance solutions.
Constraint optimisation currently presents a major problem to the engineering designer and
many attempts to utilise adaptive search techniques whilst overcoming these problems are
in evidence. The most widely used method (which is also the most general) is to
incorporate the constraints in the objective function and then use methods for
unconstrained search. The engineer must develop and adjust an appropriate penalty
function. There is no general solution to this problem neither in classical numerical
optimisation nor in evolutionary computation. Some recent theoretical evidence suggests
that the problem can only be solved by incorporating a priori knowledge into the search
engine.
Therefore, it becomes obvious that there is a need to classify constrained optimisation
problems according to the degree of available or utilised knowledge and to develop search
techniques applicable at each stage. The contribution of this thesis is to provide such a
view of constrained optimisation, starting from problems that handle the constraints on the
representation level, going through problems that have explicitly defined constraints (i.e.,
an easily computed closed form like a solvable equation), and ending with heavily
constrained problems with implicitly defined constraints (incorporated into a single
simulation model). At each stage we develop applicable adaptive search techniques that
optimally exploit the degree of available a priori knowledge thus providing excellent
quality of results and high performance. The proposed techniques are tested using both well
known test beds and real world engineering design problems provided by industry.British Aerospace,
Rolls Royce and Associate