A New Improved Model of Marine Predator Algorithm for Optimization Problems

The marine predator algorithm is a new nature-inspired metaheuristic algorithm that mimics biological interaction between marine predators and prey. It has been also stated from the literature that this algorithm can solve many real-world optimization problems which made it a new popular optimizatio...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Arabian journal for science and engineering (2011) Ročník 46; číslo 9; s. 8803 - 8826
Hlavní autoři: Ramezani, Mehdi, Bahmanyar, Danial, Razmjooy, Navid
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021
Springer Nature B.V
Témata:
D C motors
Electric motors
Engineering
Heuristic methods
Humanities and Social Sciences
Local optimization
Machine learning
Marine biology
multidisciplinary
Optimization algorithms
Optimization techniques
Particle swarm optimization
Predator-prey simulation
Predators
Proportional integral derivative
Research Article-Computer Engineering and Computer Science
Science
Search algorithms
Simulation
Marine predator algorithm
CEC-06 2019 tests
PID control
DC motor
Optimization
ISSN:2193-567X, 1319-8025, 2191-4281
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The marine predator algorithm is a new nature-inspired metaheuristic algorithm that mimics biological interaction between marine predators and prey. It has been also stated from the literature that this algorithm can solve many real-world optimization problems which made it a new popular optimization technique for the researchers. However, there is still a deficiency in the marine predator algorithm such as the inability to produce a diverse initial population with high productivity, lack of quick escaping of the local optimization, and lack of widely and broadly exploration of the search space. In the present study, a developed version of this algorithm is proposed based on the opposition-based learning method, chaos map, self-adaptive of population, and switching between exploration and exploitation phases. The simulations are performed using MATLAB environment on standard test functions including CEC-06 2019 tests and a real-world optimization problem based on PID control applied to a DC motor to evaluate the performance of the suggested algorithm. The simulation results are compared with the original marine predator algorithm and five state-of-the-art optimization algorithms namely Particle Swarm Optimization, Grasshopper Optimization Algorithm, JAYA Algorithm, Equilibrium optimizer Algorithm, Whale Optimization Algorithm, Differential Search Algorithm, and League Championship Algorithm. Eventually, the simulation results proved that the suggested algorithm has better results compared with other algorithms for the studied case studies.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-021-05688-3