396,427 research outputs found
A multiobjective model for passive portfolio management: an application on the S&P 100 index
This is an author's accepted manuscript of an article published in:
âJournal of Business Economics and Management"; Volume 14, Issue 4, 2013; copyright Taylor & Francis; available online at: http://dx.doi.org/10.3846/16111699.2012.668859Index tracking seeks to minimize the unsystematic risk component by imitating the movements of a reference index. Partial index tracking only considers a subset of the stocks in the index, enabling a substantial cost reduction in comparison with full tracking. Nevertheless, when heterogeneous investment profiles are to be satisfied, traditional index tracking techniques may need different stocks to build the different portfolios. The aim of this paper is to propose a methodology that enables a fund s manager to satisfy different clients investment profiles but using in all cases the same subset of stocks, and considering not only one particular criterion but a compromise between several criteria. For this purpose we use a mathematical programming model that considers the tracking error variance, the excess return and the variance of the portfolio plus the curvature of the tracking frontier. The curvature is not defined for a particular portfolio, but for all the portfolios in the tracking frontier. This way funds managers can offer their clients a wide range of risk-return combinations just picking the appropriate portfolio in the frontier, all of these portfolios sharing the same shares but with different weights. An example of our proposal is applied on the S&P 100.GarcĂa GarcĂa, F.; Guijarro MartĂnez, F.; Moya Clemente, I. (2013). A multiobjective model for passive portfolio management: an application on the S&P 100 index. Journal of Business Economics and Management. 14(4):758-775. doi:10.3846/16111699.2012.668859S758775144Aktan, B., KorsakienÄ, R., & SmaliukienÄ, R. (2010). TIMEâVARYING VOLATILITY MODELLING OF BALTIC STOCK MARKETS. 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V., & Stasytyte, V. (s. f.). Decision Making Strategies in Global Exchange and Capital Markets. Advances and Innovations in Systems, Computing Sciences and Software Engineering, 17-22. doi:10.1007/978-1-4020-6264-3_4Tabata, Y., & Takeda, E. (1995). Bicriteria Optimization Problem of Designing an Index Fund. Journal of the Operational Research Society, 46(8), 1023-1032. doi:10.1057/jors.1995.139TeresienÄ, D. (2009). LITHUANIAN STOCK MARKET ANALYSIS USING A SET OF GARCH MODELS. Journal of Business Economics and Management, 10(4), 349-360. doi:10.3846/1611-1699.2009.10.349-36
Novel Artificial Human Optimization Field Algorithms - The Beginning
New Artificial Human Optimization (AHO) Field Algorithms can be created from
scratch or by adding the concept of Artificial Humans into other existing
Optimization Algorithms. Particle Swarm Optimization (PSO) has been very
popular for solving complex optimization problems due to its simplicity. In
this work, new Artificial Human Optimization Field Algorithms are created by
modifying existing PSO algorithms with AHO Field Concepts. These Hybrid PSO
Algorithms comes under PSO Field as well as AHO Field. There are Hybrid PSO
research articles based on Human Behavior, Human Cognition and Human Thinking
etc. But there are no Hybrid PSO articles which based on concepts like Human
Disease, Human Kindness and Human Relaxation. This paper proposes new AHO Field
algorithms based on these research gaps. Some existing Hybrid PSO algorithms
are given a new name in this work so that it will be easy for future AHO
researchers to find these novel Artificial Human Optimization Field Algorithms.
A total of 6 Artificial Human Optimization Field algorithms titled "Human
Safety Particle Swarm Optimization (HuSaPSO)", "Human Kindness Particle Swarm
Optimization (HKPSO)", "Human Relaxation Particle Swarm Optimization (HRPSO)",
"Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", "Human Thinking
Particle Swarm Optimization (HTPSO)" and "Human Disease Particle Swarm
Optimization (HDPSO)" are tested by applying these novel algorithms on Ackley,
Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions. Results
obtained are compared with PSO algorithm.Comment: 25 pages, 41 figure
Cluster-Based Optimization of Cellular Materials and Structures for Crashworthiness
The objective of this work is to establish a cluster-based optimization method for the optimal design of cellular materials and structures for crashworthiness, which involves the use of nonlinear, dynamic finite element models. The proposed method uses a cluster-based structural optimization approach consisting of four steps: conceptual design generation, clustering, metamodel-based global optimization, and cellular material design. The conceptual design is generated using structural optimization methods. K-means clustering is applied to the conceptual design to reduce the dimensional of the design space as well as define the internal architectures of the multimaterial structure. With reduced dimension space, global optimization aims to improve the crashworthiness of the structure can be performed efficiently. The cellular material design incorporates two homogenization methods, namely, energy-based homogenization for linear and nonlinear elastic material models and mean-field homogenization for (fully) nonlinear material models. The proposed methodology is demonstrated using three designs for crashworthiness that include linear, geometrically nonlinear, and nonlinear models
The True Destination of EGO is Multi-local Optimization
Efficient global optimization is a popular algorithm for the optimization of
expensive multimodal black-box functions. One important reason for its
popularity is its theoretical foundation of global convergence. However, as the
budgets in expensive optimization are very small, the asymptotic properties
only play a minor role and the algorithm sometimes comes off badly in
experimental comparisons. Many alternative variants have therefore been
proposed over the years. In this work, we show experimentally that the
algorithm instead has its strength in a setting where multiple optima are to be
identified
Binary Particle Swarm Optimization based Biclustering of Web usage Data
Web mining is the nontrivial process to discover valid, novel, potentially
useful knowledge from web data using the data mining techniques or methods. It
may give information that is useful for improving the services offered by web
portals and information access and retrieval tools. With the rapid development
of biclustering, more researchers have applied the biclustering technique to
different fields in recent years. When biclustering approach is applied to the
web usage data it automatically captures the hidden browsing patterns from it
in the form of biclusters. In this work, swarm intelligent technique is
combined with biclustering approach to propose an algorithm called Binary
Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The
main objective of this algorithm is to retrieve the global optimal bicluster
from the web usage data. These biclusters contain relationships between web
users and web pages which are useful for the E-Commerce applications like web
advertising and marketing. Experiments are conducted on real dataset to prove
the efficiency of the proposed algorithms
An Improved Differential Evolution Algorithm for Maritime Collision Avoidance Route Planning
High accuracy navigation and surveillance systems are pivotal to ensure efficient ship route planning and marine safety. Based on existing ship navigation and maritime collision prevention rules, an improved approach for collision avoidance route planning using a differential evolution algorithm was developed. Simulation results show that the algorithm is capable of significantly enhancing the optimized route over current methods. It has the potential to be used as a tool to generate optimal vessel routing in the presence of conflicts
Formal Proofs for Nonlinear Optimization
We present a formally verified global optimization framework. Given a
semialgebraic or transcendental function and a compact semialgebraic domain
, we use the nonlinear maxplus template approximation algorithm to provide a
certified lower bound of over . This method allows to bound in a modular
way some of the constituents of by suprema of quadratic forms with a well
chosen curvature. Thus, we reduce the initial goal to a hierarchy of
semialgebraic optimization problems, solved by sums of squares relaxations. Our
implementation tool interleaves semialgebraic approximations with sums of
squares witnesses to form certificates. It is interfaced with Coq and thus
benefits from the trusted arithmetic available inside the proof assistant. This
feature is used to produce, from the certificates, both valid underestimators
and lower bounds for each approximated constituent. The application range for
such a tool is widespread; for instance Hales' proof of Kepler's conjecture
yields thousands of multivariate transcendental inequalities. We illustrate the
performance of our formal framework on some of these inequalities as well as on
examples from the global optimization literature.Comment: 24 pages, 2 figures, 3 table
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