3,034 research outputs found
A survey on financial applications of metaheuristics
Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness
(TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program
(E-2015-36)
A variable neighborhood search simheuristic for project portfolio selection under uncertainty
With limited nancial resources, decision-makers in rms and governments face the task of selecting the best portfolio of projects to invest in. As the pool of project proposals increases and more realistic constraints are considered, the problem becomes NP-hard. Thus, metaheuristics have been employed for solving large instances of the project portfolio selection problem (PPSP). However, most of the existing works do not account for uncertainty. This paper contributes to close this gap by analyzing a stochastic version of the PPSP: the goal is to maximize the expected net present value of the inversion, while considering random cash ows and discount rates in future periods, as well as a rich set of constraints including the maximum risk allowed. To solve this stochastic PPSP, a simulation-optimization algorithm is introduced. Our approach integrates a variable neighborhood search metaheuristic with Monte Carlo simulation. A series of computational experiments contribute to validate our approach and illustrate how the solutions vary as the level of uncertainty increases
A simheuristic algorithm for solving an integrated resource allocation and scheduling problem
Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings
Firefly Algorithm: Recent Advances and Applications
Nature-inspired metaheuristic algorithms, especially those based on swarm
intelligence, have attracted much attention in the last ten years. Firefly
algorithm appeared in about five years ago, its literature has expanded
dramatically with diverse applications. In this paper, we will briefly review
the fundamentals of firefly algorithm together with a selection of recent
publications. Then, we discuss the optimality associated with balancing
exploration and exploitation, which is essential for all metaheuristic
algorithms. By comparing with intermittent search strategy, we conclude that
metaheuristics such as firefly algorithm are better than the optimal
intermittent search strategy. We also analyse algorithms and their implications
for higher-dimensional optimization problems.Comment: 15 page
Variable neighbourhood search for the minimum labelling Steiner tree problem
We present a study on heuristic solution approaches to the minimum labelling Steiner
tree problem, an NP-hard graph problem related to the minimum labelling spanning tree
problem. Given an undirected labelled connected graph, the aim is to find a spanning
tree covering a given subset of nodes of the graph, whose edges have the smallest number
of distinct labels. Such a model may be used to represent many real world problems in
telecommunications and multimodal transportation networks. Several metaheuristics are
proposed and evaluated. The approaches are compared to the widely adopted Pilot Method
and it is shown that the Variable Neighbourhood Search metaheuristic is the most effective
approach to the problem, obtaining high quality solutions in short computational running
times
Recommended from our members
Variable neighbourhood search for the minimum labelling Steiner tree problem
We present a study on heuristic solution approaches to the minimum labelling Steiner tree problem, an NP-hard graph problem related to the minimum labelling spanning tree problem. Given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes of the graph, whose edges have the smallest number of distinct labels. Such a model may be used to represent many real world problems in telecommunications and multimodal transportation networks. Several metaheuristics are proposed and evaluated. The approaches are compared to the widely adopted Pilot Method and it is shown that the Variable Neighbourhood Search that we propose is the most effective metaheuristic for the problem, obtaining high quality solutions in short computational running time
Recommended from our members
Heuristics based on greedy randomized adaptive search and variable neighbourhood search for the minimum labelling spanning tree problem
This paper studies heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree using edges that are as similar as possible. Given an undirected labelled connected graph, the minimum labelling spanning tree problem seeks a spanning tree whose edges have the smallest number of distinct labels. This problem has been shown to be NP-complete. A Greedy Randomized Adaptive Search Procedure (GRASP) and different versions of Variable Neighbourhood Search (VNS) are proposed. They are compared with other algorithms recommended in the literature: the Modified Genetic Algorithm and the Pilot Method. Nonparametric statistical tests show that the heuristics based on GRASP and VNS outperform the other algorithms tested. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristics
Recommended from our members
A survey on portfolio optimisation with metaheuristics.
A portfolio optimisation problem involves allocation
of investment to a number of different assets to maximize return
and minimize risk in a given investment period. The selected
assets in a portfolio not only collectively contribute to its return
but also interactively define its risk as usually measured by a
portfolio variance. This presents a combinatorial optimisation
problem that involves selection of both a number of assets as well
as its quantity (weight or proportion or units). The problem is
extremely complex due to a large number of selectable assets.
Furthermore, the problem is dynamic and stochastic in nature
with a number of constraints presenting a complex model which is
difficult to solve for exact solution. In the last decade research
publications have reported the applications of
metaheuristic-based optimisation methods with some success.,
This paper presents a review of these reported models,
optimisation problem formulations and metaheuristic approaches
for portfolio optimisation
Time-limited Metaheuristics for Cardinality-constrained Portfolio Optimisation
A financial portfolio contains assets that offer a return with a certain
level of risk. To maximise returns or minimise risk, the portfolio must be
optimised - the ideal combination of optimal quantities of assets must be
found. The number of possible combinations is vast. Furthermore, to make the
problem realistic, constraints can be imposed on the number of assets held in
the portfolio and the maximum proportion of the portfolio that can be allocated
to an asset. This problem is unsolvable using quadratic programming, which
means that the optimal solution cannot be calculated. A group of algorithms,
called metaheuristics, can find near-optimal solutions in a practical computing
time. These algorithms have been successfully used in constrained portfolio
optimisation. However, in past studies the computation time of metaheuristics
is not limited, which means that the results differ in both quality and
computation time, and cannot be easily compared. This study proposes a
different way of testing metaheuristics, limiting their computation time to a
certain duration, yielding results that differ only in quality. Given that in
some use cases the priority is the quality of the solution and in others the
speed, time limits of 1, 5 and 25 seconds were tested. Three metaheuristics -
simulated annealing, tabu search, and genetic algorithm - were evaluated on
five sets of historical market data with different numbers of assets. Although
the metaheuristics could not find a competitive solution in 1 second, simulated
annealing found a near-optimal solution in 5 seconds in all but one dataset.
The lowest quality solutions were obtained by genetic algorithm.Comment: 51 pages, 8 tables, 3 figure
- …