A cooperative system for metaheuristic algorithms

Tezel, Baris Tekin; Mert, Ali

doi:10.1016/j.eswa.2020.113976

Cited by 19 publications

(11 citation statements)

References 36 publications

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“…The cooperation of metaheuristics combines two or more algorithms from the class of metaheuristics to overcome the limitations of each algorithm separately [30]. It involves amalgamating the strengths of multiple algorithms to find an optimal solution based on more parameters and restrictions [30]. To combine these algorithms, developers might implement hybridization or parallelization.…”

Section: Cooperation Of Metaheuristicsmentioning

confidence: 99%

Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection

Krasnov,

Davis,

Malott

et al. 2023

Entropy

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This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Classical methods are labour-intensive, and, hence, expensive in terms of healthcare resources. Recent research supplements classical methods with automated mammogram analysis. The basic FCM method relies upon the Euclidean distance, which is not optimal for measuring non-spherical structures. To address these limitations, we review the implementation of a Mahalanobis-distance-based FCM (FCM-M). The three objectives of the paper are: (1) review FCM, FCM-M, and three centroid initialization algorithms in the literature, (2) illustrate the effectiveness of these algorithms in image segmentation, and (3) develop a Python package with the optimized algorithms to upload onto GitHub. Image analysis of the algorithms shows that using one of the three centroid initialization algorithms enhances the performance of FCM. FCM-M produced higher clustering accuracy and outlined the tumour structure better than basic FCM.

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Section: Cooperation Of Metaheuristicsmentioning

confidence: 99%

Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection

Krasnov,

Davis,

Malott

et al. 2023

Entropy

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“…, to evaluate the effectiveness of the proposed algorithm and compare it with the results of other research. Tezel and Mert (2021) attempted to solve the problem of selecting an appropriate and efficient algorithm. They used fuzzy logic and fuzzy systems to create a collaboration scheme to automatically select appropriate meta-heuristic algorithms and dynamically control the search process.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The prices of this index are taken from Extra Trading -The Financial Times Stock Exchange 100 Indexalso known as the FTSE 100 Index, the FTS 100 Index or informally the FTSE, a stock market index of the 100 companies listed on the London Stock Exchange with the highest market capitalization, S&P 100 and Nikkei 225 indices extracted by Cheng et al, who also used daily prices between May 2013 and April 2016, as described by Kalayci et al, to evaluate the effectiveness of the proposed algorithm and compare it with the results of other research. Tezel and Mert (2021) attempted to solve the problem of selecting an appropriate and efficient algorithm. They used fuzzy logic and fuzzy systems to create a collaboration scheme to automatically select appropriate meta-heuristic algorithms and dynamically control the search process.…”

Section: Mf 494mentioning

confidence: 99%

Portfolio optimization in the capital market bubble space, an application of meta-heuristic algorithms

Mohammadi

Khoshouei²,

Chadegani³

2022

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PurposeIn this study, to maximize returns and minimize investment risk, an attempt was made to form an optimal portfolio under conditions where the capital market has a price bubble. According to the purpose, the research was of the applied type, in terms of data, quantitative and postevent, and in terms of the type of analysis, it was of the descriptive-correlation type. Sequence, skewness and kurtosis tests were used to identify the months with bubbles from 2015 to 2021 in the Tehran Stock Exchange. After identifying the bubble courses, artificial bee colony meta-heuristic and invasive weed algorithms were used to optimize the portfolio. The purpose of this paper is to address these issues.Design/methodology/approachThe existence of bubbles in the market, especially in the capital market, can prevent the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. However, due to the goal of investors to achieve a portfolio of high returns with the least amount of risk, there is need to pay attention to these markets increases.FindingsThe results identify 14 periods of price bubbles during the study period. Additionally, stock portfolios with maximum returns and minimum risk were selected for portfolio optimization. According to the results of using meta-heuristic algorithms to optimize the portfolio, in relation to the obtained returns and risk, no significant difference was observed between the returns and risk of periods with price bubbles in each of the two meta-heuristic algorithms. This study can guide investors in identifying bubble courses and forming an optimal portfolio under these conditions.Research limitations/implicationsOne of the limitations of this research is the non-generalizability of the findings to stock exchanges of other countries and other time periods due to the condition of the price bubble, as well as other companies in the stock market due to the restrictions considered for selecting the statistical sample.Originality/valueThis study intends to form an optimal stock portfolio in a situation where the capital market suffers from a price bubble. This study provides an effective and practical solution for investors in the field of stock portfolio optimization.

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“…A metaheuristic is a method or heuristic used in computer science and mathematical optimization to find, create, or choose a heuristic that may provide the best available solution to a problem 9,10 . An optimization problem is a problem in mathematics, computer science, or economics in which the aim is to find the best solution from a set of alternatives 11 .…”

Section: Introductionmentioning

confidence: 99%

Honeyword Generation Using a Proposed Discrete Salp Swarm Algorithm

2023

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Honeywords are fake passwords that serve as an accompaniment to the real password, which is called a "sugarword." The honeyword system is an effective password cracking detection system designed to easily detect password cracking in order to improve the security of hashed passwords. For every user, the password file of the honeyword system will have one real hashed password accompanied by numerous fake hashed passwords. If an intruder steals the password file from the system and successfully cracks the passwords while attempting to log in to users' accounts, the honeyword system will detect this attempt through the honeychecker. A honeychecker is an auxiliary server that distinguishes the real password from the fake passwords and triggers an alarm if intruder signs in using a honeyword. Many honeyword generation approaches have been proposed by previous research, all with limitations to their honeyword generation processes, limited success in providing all required honeyword features, and susceptibility to many honeyword issues. This work will present a novel honeyword generation method that uses a proposed discrete salp swarm algorithm. The salp swarm algorithm (SSA) is a bio-inspired metaheuristic optimization algorithm that imitates the swarming behavior of salps in their natural environment. SSA has been used to solve a variety of optimization problems. The presented honeyword generation method will improve the generation process, improve honeyword features, and overcome the issues of previous techniques. This study will demonstrate numerous previous honeyword generating strategies, describe the proposed methodology, examine the experimental results, and compare the new honeyword production method to those proposed in previous research.

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A cooperative system for metaheuristic algorithms

Cited by 19 publications

References 36 publications

Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection

Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection

Portfolio optimization in the capital market bubble space, an application of meta-heuristic algorithms

Honeyword Generation Using a Proposed Discrete Salp Swarm Algorithm

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