Stock exchange trading optimization algorithm: a human-inspired method for global optimization

Emami, Hojjat

doi:10.1007/s11227-021-03943-w

Cited by 48 publications

(8 citation statements)

References 84 publications

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“…The fourth class of population-based meta-heuristic optimization algorithm, Human-Based algorithms imitates human behaviour, supremacy and intelligence. Few of the human-based algorithms as depicted in the figure are listed below: Cultural Algorithm [69], Imperialist Competitive Algorithm [70], Teaching Learning-Based Optimization [71], Brain Storm Optimization [72], Human Behavior-Based Optimization [73], Human Mental Search [74], Social Engineering Optimizer [75], Queuing Search Algorithm [76], Search and Rescue Optimization [77], Life Choice-Based Optimization [78], Social Ski-Driver Optimization [79], Gaining Sharing Knowledge-Based Algorithm [80], Future Search Algorithm [81], Forensic-Based Investigation Optimization [82], Political Optimizer [83], Heap-Based Optimizer [84], Human Urbanization Algorithm [85], Battle Royale Optimization [86], Corona virus Herd Immunity Optimization [87], Passing Vehicle Search [88], Jaya Algorithm [89], Seeker Optimization Algorithm [90], Interior Search Algorithm [91], Soccer League Competition Algorithm [92], Exchange Market Algorithm [93], Group Counseling Optimization Algorithm [94], Tug of War Optimization [95], Most Valuable Player Algorithm [96], Volleyball Premier League Algorithm [97], Dynastic Optimization Algorithm [98], Focus Group [99], Stock Exchange Trading Optimization [100], Anti Corona virus Optimization Algorithm [101], Socio Evolution and Learning Optimization [102], League Championship Algorithm [103], Ideology Algorithm …”

Section: Introductionmentioning

confidence: 99%

An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

Rai

Dhal

Das

et al. 2023

Arch Computat Methods Eng

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Marine Predators Algorithm (MPA) is the existing population-based meta-heuristic algorithms that falls under the category of Nature-Inspired Optimization Algorithm (NIOA) enthused by the foraging actions of the marine predators that principally pursues Levy or Brownian approach as its foraging strategy. Furthermore, it employs the optimal encounter rate stratagem involving both the predator as well as prey. Since its introduction by Faramarzi in the year 2020, MPA has gained enormous popularity and has been employed in numerous application areas ranging from Mathematical and Engineering Optimization problems to Fog Computing to Image Processing to Photovoltaic System to Wind-Solar Generation System for resolving continuous optimization problems. Such huge interest from the research fraternity or the massive recognition of MPA is due to several factors such as its simplicity, ease of application, realistic execution time, superior convergence acceleration rate, soaring effectiveness, its ability to unravel continuous, multi-objective and binary problems when compared with other renowned optimization algorithms existing in the literature. This paper offers a detailed summary of the Marine Predators Algorithm (MPA) and its variants. Furthermore, the applications of MPA in a number of spheres such as Image processing, classification, electrical power system, Photovoltaic models, structural damage detection, distribution networks, engineering applications, Task Scheduling, optimization problems etc., are illustrated. To conclude, the paper highlights and thereby advocates few of the potential future research directions for MPA.

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Section: Introductionmentioning

confidence: 99%

An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

Rai

Dhal

Das

et al. 2023

Arch Computat Methods Eng

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“…To investigate the performance of ACVO in solving engineering applications, it is evaluated on seven real-world engineering problems. These problems are drawn from CEC2011 competition (Das and Suganthan 2012 ) and related literature (Emami 2020 , 2021 ), and include the frequency-modulated sound waves (FMSW), static economic load dispatch (SELD), transmission network expansion planning (TNEP), spread spectrum radar poly phase code design (SSRPCD), speed reducer design (SRD), welded beam design (WBD), and rolling element bearing design (REBD). The characteristics of FMSW, SELD, TNEP, and SSRPCD problems are summarized in Table 17 .…”

Section: Experiments and Analysismentioning

confidence: 99%

“…This maximization problem consists of 9 design constraints and 10 geometric variables to handle the geometric-based and assembly restrictions. The mathematical definition of the problem is as follows (Emami 2021 ): The results generated by ACVO and counterpart algorithms on REBD problems are summarized in Table 21 . The results prove that the ACVO shows the best design and attains better performances compared with other algorithms.…”

Section: Experiments and Analysismentioning

confidence: 99%

Anti-coronavirus optimization algorithm

Emami

2022

Soft Comput

Self Cite

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This paper introduces a new swarm intelligence strategy, anti-coronavirus optimization (ACVO) algorithm. This algorithm is a multi-agent strategy, in which each agent is a person that tries to stay healthy and slow down the spread of COVID-19 by observing the containment protocols. The algorithm composed of three main steps: social distancing, quarantine, and isolation. In the social distancing phase, the algorithm attempts to maintain a safe physical distance between people and limit close contacts. In the quarantine phase, the algorithm quarantines the suspected people to prevent the spread of disease. Some people who have not followed the health protocols and infected by the virus should be taken care of to get a full recovery. In the isolation phase, the algorithm cared for the infected people to recover their health. The algorithm iteratively applies these operators on the population to find the fittest and healthiest person. The proposed algorithm is evaluated on standard multi-variable single-objective optimization problems and compared with several counterpart algorithms. The results show the superiority of ACVO on most test problems compared with its counterparts.

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“…Using lattice conversion, together with Taylor's first series estimation of natural logarithm, they approximated a nonlinear stochastic hybrid optimization model with its linear equivalent. Some authors have used meta-heuristic algorithms to optimize the portfolio, such as the HI algorithm [ 50 ], WOA algorithm [ 51 , 52 ], ICA-FA algorithm [ 53 ], GWO algorithm [ 54 , 55 ], and the IWO algorithm [ 56 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Portfolio Optimization Using a Combined Approach of Multi Objective Grey Wolf Optimizer and Machine Learning Preselection Methods

Mazraeh

Daneshvar

Zaj

et al. 2022

Computational Intelligence and Neuroscience

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The present paper deals with optimizing the stock portfolio of active companies listed on the Tehran Stock Exchange based on the forecast price. This paper is based on a combination of different filtering methods such as optimization of trading rules based on technical analysis (ROC, SMA, EMA, WMA, and MACD at six levels—Very Very Weak (VVW), Very Weak (VW), Weak (W), Strong (S), Very Strong (VS), and Very Very Strong (VVS)), Markov Chains, and Machine Learning (Random Forest and Support Vector Machine) Filter stock exchanges and provide buy signals between 2011 and 2020. In proportion to each combination of filtering methods, a buy signal is issued and based on the mean-variance (M-V) model, the stock portfolio is optimized based on increasing the portfolio return and minimizing the stock portfolio risk. Based on this, out of 480 companies listed on the Tehran Stock Exchange, 85 active companies have been selected and stock portfolio optimization is based on two algorithms, MOGWO and NSGA II. The analysis results show that the use of SVM learning machine leads to minor correlation error than the random forest method. Therefore, this method was used to predict stock prices. Based on the results, it was observed that if the shares of companies are filtered, the risk of transactions decreases, and the return on the stock portfolio increases. Also, if two filtering methods are applied simultaneously, the stock portfolio returns slightly and the risk increases. In the analysis, MOGWO algorithm has obtained 133.13% stock return rate with a risk of 3.346%, while the stock portfolio returns in NSGA II algorithm 107.73, with a risk of 1.459%. Comparison of solution methods shows that the MOGWO algorithm has high efficiency in stock portfolio optimization.

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Stock exchange trading optimization algorithm: a human-inspired method for global optimization

Cited by 48 publications

References 84 publications

An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

Anti-coronavirus optimization algorithm

Stock Portfolio Optimization Using a Combined Approach of Multi Objective Grey Wolf Optimizer and Machine Learning Preselection Methods

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