COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.

https://downloads.hindawi.com/journals/tswj/2014/176718.pdf

Improved bat algorithm applied to multilevel image thresholding.

Portfolio optimization is the process of determining the best combination of securities and proportions with the aim of having less risk and obtaining more profit in an investment. Utilizing covariance as a risk measure, mean-variance portfolio optimization model has brought a revolutionary approach to quantitative finance. Since then, along with the advancements in computational power and algorithmic enhancements, a lot of efforts have been made on improving this model by considering real-life conditions and solving model variants with various methodologies tested on various data and performance measures. A comprehensive literature review of recent and novel papers is crucial to establish a pattern of the past, and to pave the way on future directions. In this paper, a total of 175 papers published in the last two decades are selected within the scope of operations research community and reviewed in detail. Thus, a comprehensive survey on the deterministic models and applications suggested for mean-variance portfolio optimization in which several variants of this model as well as additional real-life constraints are studied. The review classifies the approaches according to exact and approximate attempts and analyzes the proposed algorithms based on various data and performance indicators in depth. Areas of future research are outlined.

A comprehensive review of deterministic models and applications for mean-variance portfolio optimization

A modified firefly algorithm for global minimum optimization

In portfolio optimization (PO), often, a risk measure is an objective to be minimized or an efficient frontier representing the best tradeoff between return and risk is sought. In order to overcome computational difficulties of this NP-hard problem, a growing number of researchers have adopted swarm intelligence (SI) methodologies to deal with PO. The main PO models are summarized, and the suggested SI methodologies are analyzed in depth by conducting a survey from the recent published literature. Hence, this study provides a review of the SI contributions to PO literature and identifies areas of opportunity for future research.

A survey of swarm intelligence for portfolio optimization: Algorithms and applications

Portfolio selection (optimization) problem is a very important and widely rese arched problem in the areas of finance and economy. Literature review shows that many methods and heuristics were applied to this hard optimization problem, however, there are only few implementations of swarm intelligence metaheuristics. This paper presents artificial bee colony (ABC) algorithm applied to the cardinality constrained mean-variance (CCMV) portfolio optimization model. By analyzing ABC metaheuristic, some deficiencies such as slow convergence to the optimal region, were noticed. In this paper ABC algorithm improved by hybridization with the firefly algorithm (FA) is presented. FA's search procedure was incorporate d into the ABC algorithm to enhance the process of exploitation. We tested our proposed algorithm on standard test data used in the literature. Comparison with other state-of-the-art optimization metaheuristics including genetic algorithms, simulated annealing, tabu searc h and particle swarm optimization (PSO) shows that our approach is superior considering quality of the portfolio optimization results , especially mean Euclidean distance from the standard efficiency frontier.

/pdf/artificial-bee-colony-algorithm-hybridized-with-firefly-1whbgorda6.pdf

Milan Tubaand

Papers

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