308 research outputs found
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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 inspired by bird flocks or swarms of bees by modelling their collective social influence as they search for optimal solutions.
In many real-world applications of PSO, the algorithm is used as a data pre-processor for a neural network or similar post processing system, and is often extensively modified to suit the application. The thesis introduces techniques that allow unmodified PSO to be applied successfully to a range of problems, specifically three extensions to the basic PSO algorithm: solving optimisation problems by training a hyperspatial matrix, using a hierarchy of swarms to coordinate optimisation on several data sets simultaneously, and dynamic neighbourhood selection in swarms.
Rather than working directly with candidate solutions to an optimisation problem, the PSO algorithm is adapted to train a matrix of weights, to produce a solution to the problem from the inputs. The search space is abstracted from the problem data.
A single PSO swarm optimises a single data set and has difficulties where the data set comprises disjoint parts (such as time series data for different days). To address this problem, we introduce a hierarchy of swarms, where each child swarm optimises one section of the data set whose gbest particle is a member of the swarm above in the hierarchy. The parent swarm(s) coordinate their children and encourage more exploration of the solution space. We show that hierarchical swarms of this type perform better than single swarm PSO optimisers on the disjoint data sets used.
PSO relies on interaction between particles within a neighbourhood to find good solutions. In many PSO variants, possible interactions are arbitrary and fixed on initialisation. Our third contribution is a dynamic neighbourhood selection: particles can modify their neighbourhood, based on the success of the candidate neighbour particle. As PSO is intended to reflect the social interaction of agents, this change significantly increases the ability of the swarm to find optimal solutions. Applied to real-world medical and cosmological data, this modification is and shows improvements over standard PSO approaches with fixed neighbourhoods
Mitigating Metaphors: A Comprehensible Guide to Recent Nature-Inspired Algorithms
In recent years, a plethora of new metaheuristic algorithms have explored
different sources of inspiration within the biological and natural worlds. This
nature-inspired approach to algorithm design has been widely criticised. A
notable issue is the tendency for authors to use terminology that is derived
from the domain of inspiration, rather than the broader domains of
metaheuristics and optimisation. This makes it difficult to both comprehend how
these algorithms work and understand their relationships to other
metaheuristics. This paper attempts to address this issue, at least to some
extent, by providing accessible descriptions of the most cited nature-inspired
algorithms published in the last twenty years. It also discusses commonalities
between these algorithms and more classical nature-inspired metaheuristics such
as evolutionary algorithms and particle swarm optimisation, and finishes with a
discussion of future directions for the field
Nature-inspired algorithms for solving some hard numerical problems
Optimisation is a branch of mathematics that was developed to find the optimal solutions,
among all the possible ones, for a given problem. Applications of optimisation techniques
are currently employed in engineering, computing, and industrial problems. Therefore, optimisation is a very active research area, leading to the publication of a large number of
methods to solve specific problems to its optimality.
This dissertation focuses on the adaptation of two nature inspired algorithms that, based
on optimisation techniques, are able to compute approximations for zeros of polynomials
and roots of non-linear equations and systems of non-linear equations.
Although many iterative methods for finding all the roots of a given function already
exist, they usually require: (a) repeated deflations, that can lead to very inaccurate results
due to the problem of accumulating rounding errors, (b) good initial approximations to the
roots for the algorithm converge, or (c) the computation of first or second order derivatives,
which besides being computationally intensive, it is not always possible.
The drawbacks previously mentioned served as motivation for the use of Particle Swarm
Optimisation (PSO) and Artificial Neural Networks (ANNs) for root-finding, since they are
known, respectively, for their ability to explore high-dimensional spaces (not requiring good
initial approximations) and for their capability to model complex problems. Besides that,
both methods do not need repeated deflations, nor derivative information.
The algorithms were described throughout this document and tested using a test suite of
hard numerical problems in science and engineering. Results, in turn, were compared with
several results available on the literature and with the well-known Durand–Kerner method,
depicting that both algorithms are effective to solve the numerical problems considered.A Optimização é um ramo da matemática desenvolvido para encontrar as soluções óptimas, de entre todas as possíveis, para um determinado problema. Actualmente, são várias as
técnicas de optimização aplicadas a problemas de engenharia, de informática e da indústria.
Dada a grande panóplia de aplicações, existem inúmeros trabalhos publicados que propõem
métodos para resolver, de forma óptima, problemas específicos.
Esta dissertação foca-se na adaptação de dois algoritmos inspirados na natureza que,
tendo como base técnicas de optimização, são capazes de calcular aproximações para zeros
de polinómios e raízes de equações não lineares e sistemas de equações não lineares.
Embora já existam muitos métodos iterativos para encontrar todas as raízes ou zeros de
uma função, eles usualmente exigem: (a) deflações repetidas, que podem levar a resultados
muito inexactos, devido ao problema da acumulação de erros de arredondamento a cada
iteração; (b) boas aproximações iniciais para as raízes para o algoritmo convergir, ou (c) o
cálculo de derivadas de primeira ou de segunda ordem que, além de ser computacionalmente
intensivo, para muitas funções é impossível de se calcular.
Estas desvantagens motivaram o uso da Optimização por Enxame de Partículas (PSO) e
de Redes Neurais Artificiais (RNAs) para o cálculo de raízes. Estas técnicas são conhecidas,
respectivamente, pela sua capacidade de explorar espaços de dimensão superior (não exigindo
boas aproximações iniciais) e pela sua capacidade de modelar problemas complexos. Além
disto, tais técnicas não necessitam de deflações repetidas, nem do cálculo de derivadas.
Ao longo deste documento, os algoritmos são descritos e testados, usando um conjunto de
problemas numéricos com aplicações nas ciências e na engenharia. Os resultados foram comparados com outros disponíveis na literatura e com o método de Durand–Kerner, e sugerem
que ambos os algoritmos são capazes de resolver os problemas numéricos considerados
Informative and misinformative interactions in a school of fish
It is generally accepted that, when moving in groups, animals process
information to coordinate their motion. Recent studies have begun to apply
rigorous methods based on Information Theory to quantify such distributed
computation. Following this perspective, we use transfer entropy to quantify
dynamic information flows locally in space and time across a school of fish
during directional changes around a circular tank, i.e. U-turns. This analysis
reveals peaks in information flows during collective U-turns and identifies two
different flows: an informative flow (positive transfer entropy) based on fish
that have already turned about fish that are turning, and a misinformative flow
(negative transfer entropy) based on fish that have not turned yet about fish
that are turning. We also reveal that the information flows are related to
relative position and alignment between fish, and identify spatial patterns of
information and misinformation cascades. This study offers several
methodological contributions and we expect further application of these
methodologies to reveal intricacies of self-organisation in other animal groups
and active matter in general
The development and application of metaheuristics for problems in graph theory: A computational study
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.It is known that graph theoretic models have extensive application
to real-life discrete optimization problems. Many of these models
are NP-hard and, as a result, exact methods may be impractical for
large scale problem instances. Consequently, there is a great interest
in developing e±cient approximate methods that yield near-optimal
solutions in acceptable computational times. A class of such methods,
known as metaheuristics, have been proposed with success.
This thesis considers some recently proposed NP-hard combinatorial
optimization problems formulated on graphs. In particular, the min-
imum labelling spanning tree problem, the minimum labelling Steiner
tree problem, and the minimum quartet tree cost problem, are inves-
tigated. Several metaheuristics are proposed for each problem, from
classical approximation algorithms to novel approaches. A compre-
hensive computational investigation in which the proposed methods
are compared with other algorithms recommended in the literature is
reported. The results show that the proposed metaheuristics outper-
form the algorithms recommended in the literature, obtaining optimal
or near-optimal solutions in short computational running times. In
addition, a thorough analysis of the implementation of these methods
provide insights for the implementation of metaheuristic strategies for
other graph theoretic problems
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New variants of variable neighbourhood search for 0-1 mixed integer programming and clustering
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Many real-world optimisation problems are discrete in nature. Although recent rapid developments in computer technologies are steadily increasing the speed of computations, the size of an instance of a hard discrete optimisation problem solvable in prescribed time does not increase linearly with the computer speed. This calls for the development of new solution methodologies for solving larger instances in shorter time. Furthermore, large instances of discrete optimisation problems are normally impossible to solve to optimality within a reasonable computational time/space and can only be tackled with a heuristic approach.
In this thesis the development of so called matheuristics, the heuristics which are based on the mathematical formulation of the problem, is studied and employed within the variable neighbourhood search framework. Some new variants of the variable neighbourhood searchmetaheuristic itself are suggested, which naturally emerge from exploiting the information from the mathematical programming formulation of the problem. However, those variants may also be applied to problems described by the combinatorial formulation. A unifying perspective on modern advances in local search-based metaheuristics, a so called hyper-reactive approach, is also proposed. Two NP-hard discrete optimisation problems are considered: 0-1 mixed integer programming and clustering with application to colour image quantisation. Several new heuristics for 0-1 mixed integer programming problem are developed, based on the principle of variable neighbourhood search. One set of proposed heuristics consists of improvement heuristics, which attempt to find high-quality near-optimal solutions starting from a given feasible solution. Another set consists of constructive heuristics, which attempt to find initial feasible solutions for 0-1 mixed integer programs. Finally, some variable neighbourhood search based clustering techniques are applied for solving the colour image quantisation problem. All new methods presented are compared to other algorithms recommended in literature and a comprehensive performance analysis is provided. Computational results show that the methods proposed either outperform the existing state-of-the-art methods for the problems observed, or provide comparable results.
The theory and algorithms presented in this thesis indicate that hybridisation of the CPLEX MIP solver and the VNS metaheuristic can be very effective for solving large instances of the 0-1 mixed integer programming problem. More generally, the results presented in this thesis suggest that hybridisation of exact (commercial) integer programming solvers and some metaheuristic methods is of high interest and such combinations deserve further practical and theoretical investigation. Results also show that VNS can be successfully applied to solving a colour image quantisation problem.Support from the Mathematical Institute, Serbian Academy of Sciences and Arts, are acknowledged for this research
CFSO3: A New Supervised Swarm-Based Optimization Algorithm
We present CFSO3, an optimization heuristic within the class of the swarm intelligence, based on a synergy among three different features of the Continuous Flock-of-Starlings Optimization. One of the main novelties is that this optimizer is no more a classical numerical algorithm since it now can be seen as a continuous dynamic system, which can be treated by using all the mathematical instruments available for managing state equations. In addition, CFSO3allows passing from stochastic approaches to supervised deterministic ones since the random updating of parameters, a typical feature for numerical swam-based optimization algorithms, is now fully substituted by a supervised strategy: in CFSO3the tuning of parameters isa prioridesigned for obtaining both exploration and exploitation. Indeed the exploration, that is, the escaping from a local minimum, as well as the convergence and the refinement to a solution can be designed simply by managing the eigenvalues of the CFSO state equations. Virtually in CFSO3, just the initial values of positions and velocities of the swarm members have to be randomly assigned. Both standard and parallel versions of CFSO3together with validations on classical benchmarks are presented
An Investigation of Factors Influencing Algorithm Selection for High Dimensional Continuous Optimisation Problems
The problem of algorithm selection is of great importance to the optimisation community, with a number of publications present in the Body-of-Knowledge. This importance stems from the consequences of the No-Free-Lunch Theorem which states that there cannot exist a single algorithm capable of solving all possible problems. However, despite this importance, the algorithm selection problem has of yet failed to gain widespread attention . In particular, little to no work in this area has been carried out with a focus on large-scale optimisation; a field quickly gaining momentum in line with advancements and influence of big data processing. As such, it is not as yet clear as to what factors, if any, influence the selection of algorithms
for very high-dimensional problems (> 1000) - and it is entirely possible that algorithms that may not work well in lower dimensions may in fact work well in much higher dimensional spaces and vice-versa. This work therefore aims to begin addressing this knowledge gap by investigating some of these influencing factors for some common metaheuristic variants.
To this end, typical parameters native to several metaheuristic algorithms are firstly tuned using the state-of-the-art automatic parameter tuner, SMAC. Tuning produces separate parameter configurations of each metaheuristic for each of a set of continuous benchmark functions; specifically, for every algorithm-function pairing, configurations are found for each dimensionality of the function from a geometrically increasing scale (from 2 to 1500 dimensions). The nature of this tuning is therefore highly computationally expensive necessitating the use of SMAC. Using these sets of parameter configurations, a vast amount of performance
data relating to the large-scale optimisation of our benchmark suite by each metaheuristic
was subsequently generated. From the generated data and its analysis, several behaviours presented by the metaheuristics as applied to large-scale optimisation have been identified and discussed. Further,
this thesis provides a concise review of the relevant literature for the consumption of other researchers looking to progress in this area in addition to the large volume of data produced, relevant to the large-scale optimisation of our benchmark suite by the applied set of common metaheuristics. All work presented in this thesis was funded by EPSRC grant: EP/J017515/1 through the DAASE project
Multi-guide Particle Swarm Optimisation for Dynamic Multi-objective Optimisation Problems
This study investigates the suitability of, and adapts, the multi-guide particle swarm optimisation (MGPSO) algorithm for dynamic multi-objective optimisation problems (DMOPs). The MGPSO is a multi-swarm approach, originally developed for static multi-objective optimisation problems (SMOPs), where each subswarm optimises one of the objectives. It uses a bounded archive that is based on a crowding distance archive implementation. Compared to static optimization problems, DMOPs pose a challenge for meta-heuristics because there is more than one objective to optimise, and the location of the Pareto-optimal set (POS) and the Pareto-optimal front (POF) can change over time. To efficiently track the changing POF in DMOPs using MGPSO, six archive management update approaches, eight archive balance coefficient initialization strategies, and six quantum particle swarm optimisation (QPSO) variants are proposed. To evaluate the adapted MGPSO for DMOPs, a total of twenty-nine well-known benchmark functions and six performance measures were implemented. Three experiments were run against five different environment types with varying temporal and spatial severities. The best strategies from each experiment were then compared with the other dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that the adapted MGPSO achieves very competitive, and often better, performance compared to existing DMOAs
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