DM: Dehghani Method for Modifying Optimization Algorithms
| Autor: | David Sotelo, Haidar Samet, Ali Dehghani, Gaurav Dhiman, Ricardo A. Ramirez-Mendoza, Zeinab Montazeri, Carlos Sotelo, Mohammad Javad Dehghani, Om P. Malik, Ali Ehsanifar, Josep M. Guerrero |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Optimization
population-based algorithm Mathematical optimization Modifying Optimization problem Computer science Process (engineering) 020209 energy Population optimization algorithm 02 engineering and technology Population-based algorithm lcsh:Technology lcsh:Chemistry Set (abstract data type) modifying Genetic algorithm 0202 electrical engineering electronic engineering information engineering General Materials Science education lcsh:QH301-705.5 Instrumentation Physical law Fluid Flow and Transfer Processes education.field_of_study Optimization algorithm lcsh:T Process Chemistry and Technology General Engineering Particle swarm optimization lcsh:QC1-999 Computer Science Applications lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 020201 artificial intelligence & image processing lcsh:Engineering (General). Civil engineering (General) optimization Dehghani method lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 10, Iss 7683, p 7683 (2020) Applied Sciences Volume 10 Issue 21 Dehghani, M, Montazeri, Z, Dehghani, A, Samet, H, Sotelo, C, Sotelo, D, Ehsanifar, A, Malik, O P, Guerrero, J M, Dhiman, G & Ramirez-Mendoza, R A 2020, ' DM : Dehghani method for modifying optimization algorithms ', Applied Sciences (Switzerland), vol. 10, no. 21, 7683, pp. 1-25 . https://doi.org/10.3390/app10217683 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10217683 |
| Popis: | In recent decades, many optimization algorithms have been proposed by researchers to solve optimization problems in various branches of science. Optimization algorithms are designed based on various phenomena in nature, the laws of physics, the rules of individual and group games, the behaviors of animals, plants and other living things. Implementation of optimization algorithms on some objective functions has been successful and in others has led to failure. Improving the optimization process and adding modification phases to the optimization algorithms can lead to more acceptable and appropriate solution. In this paper, a new method called Dehghani method (DM) is introduced to improve optimization algorithms. DM effects on the location of the best member of the population using information of population location. In fact, DM shows that all members of a population, even the worst one, can contribute to the development of the population. DM has been mathematically modeled and its effect has been investigated on several optimization algorithms including: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching-learning-based optimization (TLBO), and grey wolf optimizer (GWO). In order to evaluate the ability of the proposed method to improve the performance of optimization algorithms, the mentioned algorithms have been implemented in both version of original and improved by DM on a set of twenty-three standard objective functions. The simulation results show that the modified optimization algorithms with DM provide more acceptable and competitive performance than the original versions in solving optimization problems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|