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)

Model Pengoptimuman Portofolio Mean-Variance dan Perkembangan Praktisnya

Many research about portfolio optimization in Indonesia still uses the ‘original’ mean-variance model as proposed by Markowitz more than 60 years ago. This article reviews the development and modification of the Markowitz’s mean-variance model, especially that dealing with real stock-market features, which could help the investor to create their own portfolio. There were several real-stock market features that implemented in the modification of mean-variance portfolios optimization models, such as the minimum transaction lots, the transaction cost, the cardinality constraint, the weight constraint, and the sectoral constraint. To implement these features, several heuristic methods were used to obtain the optimal portfolio weight, such as genetic algorithm, Tabu search, bee colony algorithm, particle swarm algorithm, and simulated annealing. These methods become alternative to the mathematical programming method

On the use of biased-randomized algorithms for solving non-smooth optimization problems

Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines

A superior active portfolio optimization model for stock exchange

Due to the vast number of stocks and the multiple appearances of developing investment portfolios, investors in the financial market face multiple investment opportunities. In this regard, the investor task becomes extremely difficult as investors define their preferences for expected return and the amount to which they want to avoid potential investment risks. This research attempts to design active portfolios that outperform the performance of the appropriate market index. To achieve this aim, technical analysis and optimization procedures were used based on a hybrid model. It combines the strong features of the Markowitz model with the General Reduced Gradient (GRG) algorithm to maintain a good compromise between diversification and exploitation. The proposed model is used to construct an active portfolio optimization model for the Iraq Stock Exchange (ISX) for the period from January 2010 to February 2020. This is applied to all 132 companies registered on the exchange. In addition to the market portfolio, two methods, namely, Equal Weight (EW) and Markowitz were used to generate active portfolios to compare the research findings. After a thorough review based on the Sharpe ratio criterion, the suggested model demonstrated its robustness, resulting in maximizing earnings with low risks

Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

A simplified binary artificial fish swarm algorithm for uncapacitated facility location problems

Uncapacitated facility location problem (UFLP) is a combinatorial optimization problem, which has many applications. The artificial fish swarm algorithm has recently emerged in continuous optimization problem. In this paper, we present a simplified binary version of the artificial fish swarm algorithm (S-bAFSA) for solving the UFLP. In S-bAFSA, trial points are created by using crossover and mutation. In order to improve the quality of the solutions, a cyclic reinitialization of the population is carried out. To enhance the accuracy of the solution, a local search is applied on a predefined number of points. The presented algorithm is tested on a set of benchmark uncapacitated facility location problems.Fundação para a Ciência e a Tecnologia (FCT

Improvements of task scheduling and load balancing in cloud environment by swarm intelligence metaheuristics

Klaud racunarstvo pripada grupi novijih racunarskih paradigmi, koja se poput paradigme mrežnog racunarstva, bazira na grupisanju resursa i na korišcenju mrežnih i Internet tehnologija. U opštem smislu, klaud racunarstvo se odnosi na novi nacin isporuke racunarskih resursa u vidu usluge, gde se pod resursima podrazumeva gotovo sve, od podataka i softvera, do hardverskih komponenti, kao što su procesirajuci elementi, memorija i skladišta. Klaud racunarstvo je aktuelna i važna multidisciplinarna oblast, o cemu svedoci veliki broj objavljenih radova u vrhunskim me unarodnim casopisima i prikazanih na najznacajnijim svetskim skupovima. Na osnovu naucnih rezultata prikupljenih u objavljenim radovima iz ovog domena, može da se zakljuci da u klaud okruženju postoji veliki broj izazova i problema, za cije rešavanje mogu da se prona u bolje metode, tehnike i algoritmi. Jedan od najvažnijih izazova savremenog klaud okruženja je raspore ivanje zahteva krajnjih korisnika za izvršavanje na ogranicenom skupu raspoloživih resursa (virtuelnih mašina). Problem raspore ivanja na klaudu odnosi se na definisanje rasporeda izvršavanja zadataka na ogranicenom skupu raspoloživih resursa uzimajuci pritom u obzir potencijalna ogranicenja i funkciju cilja koju je potrebno optimizovati. Raspore ivanje poslova vrše algoritmi raspore ivanja, koji mogu da se podele na staticke i dinamicke. U slucaju statickog raspore ivanja, gde se poslovi ne mogu dinamicki prebacivati sa preopterecnih na manje opterecene virtuelne mašine, zadaci se raspore uju za izvršavanje na raspoložive virtuelne mašine pre pocetka izvršavanja. S druge strane, primenom metoda dinamickog raspore ivanja, koje je u literaturi poznato pod nazivom balansiranje opterecenja, vrši se preraspodela poslova izme u aktivnih virtuelnih mašina tokom samog izvršavanja programa raspore ivanja. Preraspodela se vrši tako što se zadaci sa virtuelnih mašina koje imaju vece opterecenje dinamicki prebacuju za izvršavanje na virtuelnim mašinama koje imaju manje opterecenje. Za potrebe dinamickog raspore ivanja koriste se uglavnom heuristicke i metaheuristicke optimizacione metode i algoritmi, koji postižu dobre rezultate. Problemi raspore ivanja poslova i balansiranja opterecenja na klaudu pripadaju grupi NP teških kombinatornih i/ili globalnih problema sa ili bez ogranicenja. Na osnovu publikovanih rezultata u relevantnim literaturnim izvorima, vidi se da su metaheuristike inteligencije rojeva, koje spadaju u grupu prirodom-inspirisanih algoritama, uspešno testirane na bencmark problemima i primenjivane na prakticnim NP teškim optimizacionim problemima (globalnim i kombinatornim) i da mogu da postignu bolje rezultate u smislu brzine konvergencije i kvaliteta rešenja, od drugih metoda, tehnika i algoritama. Polazeci od navedenog, u ovom radu je ispitivano da li je moguce dalje unaprediti rešavanja problema raspore ivanja poslova i balansiranja opterecenja na klaudu primenom metaheuristika inteligencije rojeva. Tokom sprovedenog istraživanja, unapre eno je i adaptirano više metaheuristika inteligencije rojeva za rešavanje problema raspore ivanja poslova i balansiranja opterecenja u klaud okruženju. U disertaciji su detaljno prikazane implementacije dva unapre ena algoritma rojeva - algoritma optimizacije monarh leptirovima i algoritma optimizacije jatom kitova. Za potrebe testiranja, rešavana su dva modela raspore ivanja poslova na klaudu. Prvi model, koji pripada grupi jednokriterijumske optimizacije, uzima u obzir minimizaciju vremena izvršavanja svih zadataka na klaudu, dok drugi, višekriterijumski model uzima u obzir minimizaciju vremena izvršavanja svih zadataka na klaudu i budžeta, tj. troškova za izvršavanje svih zahteva krajnjih korisnika. Simulacije su vršene u robusnom okruženju CloudSim simulatora i oba algoritma su testirana sa skupom veštackih podataka, generisanih u okviru CloudSim platforme, i realnih podataka, koji su preuzeti iz globalno dostupne bencmark baze. Osim testiranja za praktican izazov na klaudu, da bi se preciznije utvrdila unapre- enja modifikovanih metaheuristika u odnosu na osnovne verzije, obe metaheuristike su verifikovane i testiranjima na standardnim skupovima bencmark funkcija za globalnu optimizaciju bez ogranicenja. Upore ivanjem generisanih rezultata (kvalitet rešenja i brzina konvergencije) sa rezultatima najboljih poznatih metaheuristika i heuristika iz literature, koje su primenjivane na iste instance problema (na praktican problem raspore ivanja na klaudu i bencmark testove), dokazan je kvalitet implementiranih algoritama, cime je potvr ena i osnovna hipoteza ovog rada da se rešavanje izazova raspore ivanja poslova i balansiranja opterecenja u klaud okruženju mogu dalje unaprediti primenom metaheuristika inteligencije rojeva