Real Estate Market-Based Optimization Algorithm (REMARK): a market-inspired metaheuristic optimization algorithm based on the law of supply and demand

被引：2
作者：
Nobahari H. [1 ]
Eqra N. [2 ]
Bighashdel A. [3 ]
机构：
[1] Department of Aerospace Engineering, Sharif University of Technology, Tehran
[2] Department of Mechanical Engineering, Sharif University of Technology, Tehran
[3] Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven
来源：
Journal of Ambient Intelligence and Humanized Computing | 2023年 / 14卷 / 9期
关键词：
Demand; Game theory; Human-based; Metaheuristic; Optimization; Supply;
D O I：
10.1007/s12652-022-04332-8
中图分类号：
学科分类号：
摘要：
In this work, a metaheuristic optimization algorithm is developed based on the idea of interaction between the demanders and the suppliers in the real estate market. After reviewing the basic theory behind the idea, the working principles of the algorithm are developed and explained in details. The proposed framework yields the exploration and exploitation ability of the algorithm and also leads the algorithm to converge to the global maxima. In order to test the performance of the algorithm, 23 well-known benchmark functions of different characteristics are selected from the literature and the results are compared with seven metaheuristic algorithms. The algorithm is also evaluated on two engineering design problems. Results show the comparable performance of the REMARK and verify its potential to solve the optimization problems. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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收藏
页码：12387 / 12405
页数：18
相关论文
共 39 条
  • [1] Abedinpourshotorban H., Shamsuddin S.M., Beheshti Z., Jawawi D.N., Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm, Swarm Evol Comput, 26, pp. 8-22, (2016)
  • [2] Alikhani Koupaei J., Firouznia M., A chaos-based constrained optimization algorithm, J Ambient Intell Humaniz Comput, 12, pp. 9953-9976, (2021)
  • [3] Boussaid I., Lepagnot J., Siarry P., A survey on optimization metaheuristics, Inf Sci, 237, pp. 82-117, (2013)
  • [4] Chakraborty S., Saha A.K., Sharma S., Chakraborty R., Debnath S., A hybrid whale optimization algorithm for global optimization, J Ambient Intell Human Comput, pp. 1-37, (2021)
  • [5] Dhiman G., Garg M., Nagar A., Kumar V., Dehghani M., A novel algorithm for global optimization: rat swarm optimizer, J Ambient Intell Human Comput, 12, pp. 8457-8482, (2021)
  • [6] Digalakis J.G., Margaritis K.G., On benchmarking functions for genetic algorithms, Int J Comput Math, 77, pp. 481-506, (2001)
  • [7] Cheng, X, (2014)
  • [8] Garg H., A hybrid PSO-GA algorithm for constrained optimization problems, Appl Math Comput, 274, pp. 292-305, (2016)
  • [9] Goldstein A.A., On steepest descent, J Soc Ind Appl Math Ser A Control, 3, pp. 147-151, (1965)
  • [10] Huan T.T., Kulkarni A.J., Kanesan J., Huang C.J., Abraham A., Ideology algorithm: a socio-inspired optimization methodology, Neural Comput Appl, 28, pp. 845-876, (2017)
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