Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications

Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) is introduced. The main idea in designing the proposed POA is...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 22; H. 3; S. 855
Hauptverfasser: Trojovský, Pavel, Dehghani, Mohammad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 23.01.2022
MDPI
Schlagworte:
Algorithms
Computer Simulation
Design
Foraging behavior
Genetic algorithms
Immune system
Mathematical models
Mathematical optimization
Models, Theoretical
nature inspired
optimization
Optimization algorithms
optimization problem
Optimization techniques
pelican
Physics
Population
population-based algorithm
Problem solving
Simulation
swarm intelligence
Teaching
Germany
optimization problem
nature inspired
optimization
population-based algorithm
pelican
stochastic
swarm intelligence
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) is introduced. The main idea in designing the proposed POA is simulation of the natural behavior of pelicans during hunting. In POA, search agents are pelicans that search for food sources. The mathematical model of the POA is presented for use in solving optimization issues. The performance of POA is evaluated on twenty-three objective functions of different unimodal and multimodal types. The optimization results of unimodal functions show the high exploitation ability of POA to approach the optimal solution while the optimization results of multimodal functions indicate the high ability of POA exploration to find the main optimal area of the search space. Moreover, four engineering design issues are employed for estimating the efficacy of the POA in optimizing real-world applications. The findings of POA are compared with eight well-known metaheuristic algorithms to assess its competence in optimization. The simulation results and their analysis show that POA has a better and more competitive performance via striking a proportional balance between exploration and exploitation compared to eight competitor algorithms in providing optimal solutions for optimization problems.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22030855