508 research outputs found
Multi-Guide Set-Based Particle Swarm Optimization for Multi-Objective Portfolio Optimization
No full textPortfolio optimization is a multi-objective optimization problem (MOOP) with risk and profit, or some form of the two, as competing objectives. Single-objective portfolio optimization requires a trade-off coefficient to be specified in order to balance the two objectives. Erwin and Engelbrecht proposed a set-based approach to single-objective portfolio optimization, namely, set-based particle swarm optimization (SBPSO). SBPSO selects a sub-set of assets that form a search space for a secondary optimization task to optimize the asset weights. The authors found that SBPSO was able to identify good solutions to portfolio optimization problems and noted the benefits of redefining the portfolio optimization problem as a set-based problem. This paper proposes the first multi-objective optimization (MOO) approach to SBPSO, and its performance is investigated for multi-objective portfolio optimization. Alongside this investigation, the performance of multi-guide particle swarm optimization (MGPSO) for multi-objective portfolio optimization is evaluated and the performance of SBPSO for portfolio optimization is compared against multi-objective algorithms. It is shown that SBPSO is as competitive as multi-objective algorithms, albeit with multiple runs. The proposed multi-objective SBPSO, i.e., multi-guide set-based particle swarm optimization (MGSBPSO), performs similarly to other multi-objective algorithms while obtaining a more diverse set of optimal solutions
Computational Intelligence: An Introduction
No full textComputational Intelligence: An Introduction, Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments. The main focus of this text is centred on the computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation.
Engelbrecht provides readers with a wide knowledge of Computational Intelligence (CI) paradigms and algorithms; inviting readers to implement and problem solve real-world, complex problems within the CI development framework. This implementation framework will enable readers to tackle new problems without any difficulty through a single Java class as part of the CI library.
Key features of this second edition include:
A tutorial, hands-on based presentation of the material.
State-of-the-art coverage of the most recent developments in computational intelligence with more elaborate discussions on intelligence and artificial intelligence (AI).
New discussion of Darwinian evolution versus Lamarckian evolution, also including swarm robotics, hybrid systems and artificial immune systems.
A section on how to perform empirical studies; topics including statistical analysis of stochastic algorithms, and an open source library of CI algorithms.
Tables, illustrations, graphs, examples, assignments, Java code implementing the algorithms, and a complete CI implementation and experimental framework.
Computational Intelligence: An Introduction, Second Edition is essential reading for third and fourth year undergraduate and postgraduate students studying CI. The first edition has been prescribed by a number of overseas universities and is thus a valuable teaching tool. In addition, it will also be a useful resource for researchers in Computational Intelligence and Artificial Intelligence, as well as engineers, statisticians, operational researchers, and bioinformaticians with an interest in applying AI or CI to solve problems in their domains.
Check out http://www.ci.cs.up.ac.za for examples, assignments and Java code implementing the algorithms
Book Reviews
No full textA.G. van Aarde - Engelbrecht, E 1985. Sending en geregtigheid in die MatteusevangelieBoekaankondigings; Instituut vir Reformatoriese Studies, PotchefstroomJ.P. Louw - Barkhuizen, J.H. Carmen ChristianumH.G. van der Westhuizen - Kritzinger, J.J., Meiring, P.G.J. & Saayman, W.H., You will be my witnessesD.J. Smith - Schulze, L, Calvin and ‘Sound Ethics': His views on property, interest and usuryE Engelbrecht - Fowler S. Biblical studies in the gospel and societyE Engelbrecht - Fowler S. The Word of GodE Engelbrecht - Van Rensburg, J.J., Nie deur geweld . . . 'n Kritiese studie oor rewolusie en teologieS.J. Botha - Van der Walt, J, Calvin and his timesP.J. van Staden, Jeugwerk: Programsketse I en IIW.H. Swart, Rondom die teeka
Analysis of RED packet loss performance in a simulated IP WAN
Get PDFDissertation (MEng)--University of Pretoria, 2013.The Internet supports a diverse number of applications, which have different requirements for a number of services. Next generation networks provide high speed connectivity between hosts, which leaves the service provider to configure network devices appropriately, in order to maximize network performance. Service provider settings are based on best recommendation parameters, which give an opportunity to optimize these settings even further. This dissertation focuses on a packet discarding algorithm, known as random early detection (RED), to determine parameters which will maximize utilization of a resource. The two dominant traffic protocols used across an IP backbone are UDP and TCP. UDP traffic flows transmit packets regardless of network conditions, dropping packets without changing its transmission rates. However, TCP traffic flows concern itself with the network condition, reducing its packet transmission rate based on packet loss. Packet loss indicates that a network is congested. The sliding window concept, also known as the TCP congestion window, adjusts to the amount of acknowledgements the source node receives from the destination node. This paradigm provides a means to transmit data across the available bandwidth across a network. A well known and widely implemented simulation environment, the network simulator 2 (NS2), was used to analyze the RED mechanism. The network simulator 2 (NS2) software gained its popularity as being a complex networking simulation tool. Network protocol traffic (UDP and TCP) characteristics comply with theory, which verifies that the traffic generated by this simulator is valid. It is shown that the autocorrelation function differs between these two traffic types, verifying that the generated traffic does conform to theoretical and practical results. UDP traffic has a short-range dependency while TCP traffic has a long-range dependency. Simulation results show the effects of the RED algorithm on network traffic and equipment performance. It is shown that random packet discarding improves source transmission rate stabilization, as well as node utilization. If the packet dropping probability is set high, the TCP source transmission rates will be low, but a low packet drop probability provides high transmission rates to a few sources and low transmission rates to the majority of other sources. Therefore, an ideal packet drop probability was obtained to complement TCP source transmission rates and node utilization. Statistical distributions were fitted to sampled data from the simulations, which also show improvements to the network with random packet discarding. The results obtained contribute to congestion control across wide area networks. Even though a number of queuing management implementation exists, RED is the most widely used implementation used by service providers.Electrical, Electronic and Computer Engineeringunrestricte
A fuzzy particle swarm optimization algorithm for computer communication network topology design
Get PDFParticle swarm optimization (PSO) is a powerful optimization technique that has been applied to solve a number of complex optimization problems. One such optimization
problem is topology design of distributed local area networks (DLANs). The problem is defined as a multiobjective optimization problem requiring simultaneous optimization
of monetary cost, average network delay, hop
count between communicating nodes, and reliability under a set of constraints. This paper presents a multi-objective particle
swarm optimization algorithm to efficiently solve the DLAN topology design problem. Fuzzy logic is incorporated
in the PSO algorithm to handle the multi-objective nature of the problem. Specifically, a recently proposed fuzzy
aggregation operator, namely the unified And-Or operator (Khan and Engelbrecht in Inf. Sci. 177: 2692–2711, 2007), is used to aggregate the objectives. The proposed fuzzy PSO (FPSO) algorithm is empirically evaluated through a preliminary
sensitivity analysis of the PSO parameters. FPSO is also compared with fuzzy simulated annealing and fuzzy ant colony optimization algorithms. Results suggest that the
fuzzy PSO is a suitable algorithm for solving the DLAN topology design problem
Benchmarks for dynamic multi-objective optimisation algorithms
Get PDFAlgorithms that solve Dynamic Multi-Objective Optimisation Problems (DMOOPs) should be tested on benchmark functions to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for Dynamic Multi-Objective Optimisation (DMOO), no standard benchmark functions are used. A number of DMOOPs have been proposed in recent years. However, no comprehensive overview of DMOOPs exist in the literature. Therefore, choosing which benchmark functions to use is not a trivial task. This article seeks to address this gap in the DMOO literature by providing a comprehensive overview of proposed DMOOPs, and proposing characteristics that an ideal DMOO benchmark function suite should exhibit. In addition, DMOOPs are proposed for each characteristic. Shortcomings of current DMOOPs that do not address certain characteristics of an ideal benchmark suite are highlighted. These identified shortcomings are addressed by proposing new DMOO benchmark functions with complicated Pareto-Optimal Sets (POSs), and approaches to develop DMOOPs with either an isolated or deceptive Pareto-Optimal Front (POF). In addition, DMOO application areas and real-world DMOOPs are discussed.http://surveys.acm.orghj201
Empirical Analysis of A Partial Dominance Approach to Many-Objective Optimisation
No full textStudies on standard many-objective optimisation problems have indicated that multi-objective optimisation algorithms struggle to solve optimisation problems with more than three objectives, because many solutions become dominated. Therefore, the Paretodominance relation is no longer efficient in guiding the search to find an optimal Pareto front for many-objective optimisation problems. Recently, a partial dominance approach has been proposed to address the problem experienced with application of the dominance relation on many objectives. Preliminary results have illustrated that this partial dominance relation has promise, and scales well with an increase in the number of objectives. This paper conducts a more extensive empirical analysis of the partial dominance relation on a larger benchmark of difficult many-objective optimisation problems, in comparison to state-of-the-art algorithms. The results further illustrate that partial dominance is an efficient approach to solve many-objective optimisation problems.No Full Tex
Partial Dominance for Many-Objective Optimization
No full textMany optimisation problems have more than three objectives, referred to as many-objective optimisation problems (MaOPs). As the number of objectives increases, the number of solutions that are non-dominated with regards to one another also increases. Therefore, multi-objective optimisation algorithms (MOAs) that use Pareto-dominance struggle to converge to the Pareto-optimal front (POF) and to find a diverse set of solutions on the POF. This article investigates the use of MOAs to solve MaOPs by guiding the search through Pareto-dominance on three randomly selected objectives. This approach is applied to the non-dominated sorting genetic algorithm II (NSGA-II) and a multi-objective particle swarm optimisation (OMOPSO) algorithm, where three objectives are randomly selected at either every iteration or every five iterations. These algorithms are compared against the original versions of these algorithms. The results indicate that the proposed partial dominance approach outperformed the original versions of these algorithms, especially on benchmarks with 8 and 10 objectives.No Full Tex
Automatic road network extraction from high resolution satellite imagery using spectral classification methods
Get PDFDissertation (MSc)--University of Pretoria, 2010.Road networks play an important role in a number of geospatial applications, such as cartographic, infrastructure planning and traffic routing software. Automatic and semi-automatic road network extraction techniques have significantly increased the extraction rate of road networks. Automated processes still yield some erroneous and incomplete results and costly human intervention is still required to evaluate results and correct errors. With the aim of improving the accuracy of road extraction systems, three objectives are defined in this thesis: Firstly, the study seeks to develop a flexible semi-automated road extraction system, capable of extracting roads from QuickBird satellite imagery. The second objective is to integrate a variety of algorithms within the road network extraction system. The benefits of using each of these algorithms within the proposed road extraction system, is illustrated. Finally, a fully automated system is proposed by incorporating a number of the algorithms investigated throughout the thesis. CopyrightComputer Scienceunrestricte
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