The Role of Metaheuristic Algorithms in Optimizing Industrial and Financial Decision-Making: A Comparative Analysis of Population-Based and Physics-Based Methods in Solving Test Problems
The Role of Metaheuristic Algorithms in Optimizing Industrial and Financial Decision-Making: A Comparative Analysis of Population-Based and Physics-Based Methods in Solving Test Problems
Subject Areas : Financial Knowledge of Securities Analysis
Alireza Baghban Kohne Rouz
1
,
Abbas Toloie Eshlaghi
2
*
,
Alireza Pourebrahimi
3
,
َAhmad Ebrahimi
4
1 - PhD Candidate in Industrial Management, Department of Industrial Management, Operations Research, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Professor, Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran (Corresponding Author).
3 - Department of Industrial Management, Faculty of Management, Karaj Branch, Islamic Azad University, Karaj, Iran
4 - Department of Industrial Management and Technology, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
Keywords: Comparative analysis, Industrial and financial problems, Optimization, Metaheuristic algorithm ,
Abstract :
Optimization is crucial in finding the best solutions to complex problems across various scientific fields. This study examines and compares four algorithms: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, and Ant Colony Optimization. The research aims to perform a comparative analysis of these algorithms' performance in various optimization domains and to identify their features, strengths, and weaknesses in industrial and financial test functions, including production optimization, finance, inventory control, and vehicle routing. Key performance metrics such as mean, standard deviation, success rate, convergence time, accuracy, and population diversity were used for evaluation, identifying the strengths and weaknesses of each algorithm. The results indicate that the choice of optimization algorithm depends on the specific characteristics of the problem, and no algorithm is absolutely the best. This study emphasizes the need for developing novel algorithms and can aid in selecting appropriate optimization tools to improve optimization processes in industrial and financial problems.
فهرست منابع
(1) Goldberg DE. Genetic Algorithm in Search, Optimization and Machine Learning, Addison. Wesley Publishing Company, Reading, MA. 1989;1(98):9.
(2) Kennedy J, Eberhart R, editors. Particle swarm optimization. Proceedings of ICNN'95-international conference on neural networks; 1995: ieee.
(3) Rajwar K, Deep K, Das S. An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artificial Intelligence Review. 2023;56(11):13187-257.
(4) Kuo R, Hong C. Integration of genetic algorithm and particle swarm optimization for investment portfolio optimization. Applied mathematics & information sciences. 2013;7(6):2397.
(5) Thakkar A, Chaudhari K. A comprehensive survey on portfolio optimization, stock price and trend prediction using particle swarm optimization. Archives of Computational Methods in Engineering. 2021;28(4):2133-64.
(6) Pradeepkumar D, Ravi V. Forecasting financial time series volatility using particle swarm optimization trained quantile regression neural network. Applied Soft Computing. 2017;58:35-52.
(7) Rahman HF, Janardhanan MN, Nielsen IE. Real-time order acceptance and scheduling problems in a flow shop environment using hybrid GA-PSO algorithm. IEEE Access. 2019;7:112742-55.
(8) Abdi A, Abdi A, Fathollahi-Fard AM, Hajiaghaei-Keshteli M. A set of calibrated metaheuristics to address a closed-loop supply chain network design problem under uncertainty. International Journal of Systems Science: Operations & Logistics. 2021;8(1):23-40.
(9) Dokeroglu T, Sevinc E, Kucukyilmaz T, Cosar A. A survey on new generation metaheuristic algorithms. Computers & Industrial Engineering. 2019;137:106040
(10) Saraswat M, Sharma AK. Genetic Algorithm for optimization using MATLAB. Int J Adv Res Comput Sci. 2013;4(3):155-9.
(11) Kirkpatrick S, Gelatt Jr CD, Vecchi MP. Optimization by simulated annealing. science. 1983;220(4598):671-80
(12) Matlab S. Matlab. The MathWorks, Natick, MA. 2012;9.
(13) Derrac J, García S, Hui S, Suganthan PN, Herrera F. Analyzing convergence performance of evolutionary algorithms: A statistical approach. Information Sciences. 2014;289:41-58.
(14) Clerc M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE transactions on Evolutionary Computation. 2002;6(1):58-73.
(15) Tzuu-Shuh C, Yunshyong C. On the convergence rate of annealing processes. SIAM Journal on Control and Optimization. 1988;26(6):1455-70.
(16) Hussain K, Salleh MNM, Cheng S, Shi Y. On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Computing and Applications. 2019;31(11):7665-83.