Artificial Bee Colony Algorithm Hybridized with Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Selection Problem

Tuba, Milan; Bačanin, Nebojša

doi:10.12785/amis/080619

Cited by 106 publications

(46 citation statements)

References 35 publications

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“…The authors propose an efficient hybrid matheuristic that combines local search and quadratic programming to solve the multi-criteria problem and obtain an approximation of the Pareto frontier. Tuba and Bacanin [2014] present a hybrid algorithm combining two population-based metaheuristics to deal with the cardinality constrained POSP. According to their results, their hybrid approach improves each of the individual approaches and also outperform the ones obtained from GA, SA, TS and PSO.…”

Section: Portfolio Optimization and Selection Problemmentioning

confidence: 99%

A Survey on Financial Applications of Metaheuristics

2017

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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, etc. 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 reallife combinatorial optimization problems that arise in a number of financial and banking activities. This paper 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. The paper 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.• Applied computing➝Operational research➝Decision analysis • Applied computing➝Law, social and behavioral sciences➝Economics.

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Section: Portfolio Optimization and Selection Problemmentioning

confidence: 99%

A Survey on Financial Applications of Metaheuristics

2017

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“…Meta-sezgisel algoritmaların orijinal versiyonları, çözüm kalitesini artırmak için modifiye edilir veya melezleştirilir. En yaygın doğadan esinlenen algoritmalar, parçacık sürü optimizasyonu (PSO) [18], diferansiyel evrim (DE) [19], ateşböceği algoritması (FA) [20], [21], guguk kuşu arama (CS) [22], karınca koloni optimizasyonu [23][24][25][26], yapay arı koloni algoritması [27][28][29][30], yarasa algoritması (BA) [31], ağaç tohum algoritması [32] …”

Section: Gi̇ri̇ş (Introduction)unclassified

Gri Kurt Optimizasyon Algoritmasına Dayanan Çok Seviyeli İmge Eşik Seçimi

Koç¹,

Baykan²,

Babaoğlu³

2018

Journal of Polytechnic

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Çoklu eşikleme, görüntü işleme ve örüntü tanıma için önemli bir görüntü işleme tekniğidir. Optimal bir eşik değerinin seçimi görüntü eşiklemede en ciddi aşamalardan birisidir. İki seviye segmentasyon eşik değeri yardımıyla orijinal resmi iki alt bölüme ayırmayı içerirken, çoklu segmentasyon çok eşik değerlerini içermektedir. Özellikle çok seviyeli görüntü eşiklemede, detaylı araştırmaya ilişkin hesaplama zamanı tercih edilen eşik sayısıyla birlikte üstel olarak artmaktadır. Zor problemler için, sürü zekâsı başarılı ve etkili optimizasyon metotlarından biri olarak bilinmektedir. Bu çalışmada, doğadaki gri kurtların sosyal liderlik ve avcılık davranışlarını taklit eden son zamanlarda önerilmiş sürü tabanlı meta sezgisel olan gri kurt algoritması (GWO) çok seviyeli görüntü eşikleme probleminin çözümü için kullanılmaktadır. Standart test resimleri üzerinde yapılan deneysel sonuçlar GWO algoritmasının diğer modern algoritmalarla karşılaştırılabilir olduğunu göstermektedir.Anahtar Kelimeler :Çok seviyeli imge eşikleme, otsu metodu, sürü zekası, optimizasyon algoritmaları, gri kurt algoritması.

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“…Artificial bee colony (ABC) algorithm performs intensification and diversification processes, which are cornerstones of swarm algorithms, by utilizing employed, onlooker and scout bees. It was successfully applied to unconstrained [10], [11] and constrained benchmark problems [12], engineering problems [13] and many others [14]. In this paper we used elements of the ABC algorithm for hybridization of our proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning

Tuba

Bačanin

2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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This paper introduces implementation of hybridized bat algorithm for multi-objective radio frequency identification network planning problem. Multi-objective RFID problem is a well known hard optimization problem that can be solved by using swarm intelligence algorithms. Bat algorithm is a recent mataheuristic, proved to be very successful for tackling such tasks. In our implementation, we hybridized bat algorithm with the artificial bee colony algorithm and adapted it for solving radio frequency identification network planning problem. In the experimental section, we have first shown, by using standard bound-constrained benchmark functions, that our hybridization is justified and that it improves results compared to standard bat algorithm, as well as to other state-of-the-art algorithms. After that, we examined performance of our proposed approach on illustrative RFID network planning problem and compared it with other results from the literature where our proposed algorithm proved to be more successful.Keywords-RFID network planning; bat algorithm swarm intelligence; nature inspired algorithms; hybrid algorithms

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Artificial Bee Colony Algorithm Hybridized with Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Selection Problem

Cited by 106 publications

References 35 publications

A Survey on Financial Applications of Metaheuristics

A Survey on Financial Applications of Metaheuristics

Gri Kurt Optimizasyon Algoritmasına Dayanan Çok Seviyeli İmge Eşik Seçimi

Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning

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