Upgraded Firefly Algorithm for Portfolio Optimization Problem

Tuba, Milan; Bačanin, Nebojša

doi:10.1109/uksim.2014.25

Cited by 30 publications

(15 citation statements)

References 25 publications

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“…The stated goal of this work was to improve the quality of solutions of firefly algorithm for the mentioned problem. Ultimately, these modifications resulted in a significant improvement in the results of the firefly algorithm for portfolio optimization problems [34].…”

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confidence: 99%

Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm

Goli¹,

Zare²,

Tavakkoli–Moghaddam³

et al. 2019

Numerical Algebra, Control &Amp; Optimization

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Product portfolio optimization (PPO) is a strategic decision for many organizations. There are several technical methods for facilitating this decision. According to the reviewed studies, the implementation of the robust optimization approach and the invasive weed optimization (IWO) algorithm is the research gap in this field. The contribution of this paper is the development of the PPO problem with the help of the robust optimization approach and the multi-objective IWO algorithm. Considering the profit margin uncertainty in real-world investment decisions, the robust optimization approach is used to address this issue. To illustrate the real-world applicability of the model, it is implemented for dairy products of Pegah Golpayegan Company in Iran. The numerical results obtained from the IWO algorithm demonstrate the effectiveness of the proposed algorithm in tracing out the efficiency frontier of the product portfolio. The average risk of efficient frontier solutions in the deterministic model is about 0.4 and for the robust counterpart formulation is at least 0.5 per product. The efficient frontier solutions obtained from robust counterpart formulation demonstrate a more realistic risk level than the deterministic model. The comparisons between CPLEX, IWO and genetic algorithm (GA) shows that the performance of the IWO algorithm is much better than the older algorithms and can be considered as an alternative to algorithms, such as GA in product portfolio optimization problems.

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confidence: 99%

Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm

Goli¹,

Zare²,

Tavakkoli–Moghaddam³

et al. 2019

Numerical Algebra, Control &Amp; Optimization

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“…Portfolio optimization can be expressed as a mean-variance problem which belongs to the group of quadratic mixed-integer programming problems. In [106,107], firefly algorithm has been extended with the use of rounding function and constraint handling approach. Deb's method [108] is also used for constraint handling.…”

Section: Modifications For Mixed Optimization Problemsmentioning

confidence: 99%

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

Khan¹,

Hamadneh²,

Tilahun³

et al. 2016

Optimization Algorithms - Methods and Applications

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Firefly algorithm is one of the well-known swarm-based algorithms which gained popularity within a short time and has different applications. It is easy to understand and implement. The existing studies show that it is prone to premature convergence and suggest the relaxation of having constant parameters. To boost the performance of the algorithm, different modifications are done by several researchers. In this chapter, we will review these modifications done on the standard firefly algorithm based on parameter modification, modified search strategy and change the solution space to make the search easy using different probability distributions. The modifications are done for continuous as well as non-continuous problems. Different studies including hybridization of firefly algorithm with other algorithms, extended firefly algorithm for multiobjective as well as multilevel optimization problems, for dynamic problems, constraint handling and convergence study will also be briefly reviewed. A simulationbased comparison will also be provided to analyse the performance of the standard as well as the modified versions of the algorithm. . IntroductionAn optimization problem refers to the maximization or minimization of an objective function by setting suitable values for the variables from a set of feasible values. These problems appear not only in complex scientific studies but also in our day-to-day activities. For instance, when a person wants to go from one place to another and has multiple possible routes, a decision needs to be made on which route to take. The decision can be with the objective to minimize travel time, fuel consumption and so on. However, these kinds of problems with fewer number of alternatives can easily be solved by looking at the outcome of each of the alternatives. However, in real problems, it is not always the case to have a finite and small number of alternatives. Hence, different solution methods are proposed based on the behaviour of the problem. Firefly algorithm is among those metaheuristic algorithms which have different applications. Its uncomplicated and easy steps with its effectiveness attract researchers from different disciplines it. Different studies have been performed to modify the standard firefly algorithm to boost its performance and to make it suitable for a problem at hand. In this chapter, a comprehensive study will be presented on firefly algorithm and its modified versions. A brief discussion on extended firefly algorithm with other relevant studies will also be provided. In the next section, a discussion on optimization problems with their solution methods will be given followed by a review on studies on firefly algorithm, which includes a discussion on the standard firefly algorithm with its modified versions and other relevant studies on firefly algorithm, in Section . In Section , a comparative study based on simulation results will be presented followed by summary of the chapter in Section . . Optimization problemsDecision-making problems can be found ...

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“…The authors developed these to address unconstrained portfolio optimization as well as portfolios with cardinality and bounding constraints. However, because the results were satisfactory at most even after modifications, the authors hybridized FA and ABC by incorporating the FA's search strategy into ABC to enhance exploitation and found that their data suggested superiority of the methodology compared to GA, SA, TS, and PSO [30] for unconstrained and cardinality-constrained portfolios. Streichert et al [27] account for further constraints, namely buy-in thresholds (acquisition prices) and roundlots (smallest volume of an asset that can be purchased).…”

Section: The Multi-objective Portfolio Optimization Problemmentioning

confidence: 99%

Solving Realistic Portfolio Optimization Problems via Metaheuristics: A Survey and an Example

Doering

Juan

Kizys

et al. 2016

Lecture Notes in Business Information Processing

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Computational finance has become one of the emerging application fields of metaheuristic algorithms. In particular, these optimization methods are quickly becoming the solving approach alternative when dealing with realistic versions of financial problems, such as the popular portfolio optimization problem (POP). This paper reviews the scientific literature on the use of metaheuristics for solving rich versions of the POP and illustrates, with a numerical example, the capacity of these methods to provide high-quality solutions to complex POPs in short computing times, which might be a desirable property of solving methods that support real-time decision making.

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Upgraded Firefly Algorithm for Portfolio Optimization Problem

Cited by 30 publications

References 25 publications

Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm

Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

Solving Realistic Portfolio Optimization Problems via Metaheuristics: A Survey and an Example

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