MOGOA algorithm for constrained and unconstrained multi-objective optimization problems

Grasshopper Optimization Algorithm (GOA) was modified in this paper, to optimize multi-objective problems, and the modified version is called Multi-Objective Grasshopper Optimization Algorithm (MOGOA). An external archive is integrated with the GOA for saving the Pareto optimal solutions. The archiv...

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Veröffentlicht in:Applied intelligence (Dordrecht, Netherlands) Jg. 48; H. 8; S. 2268 - 2283
Hauptverfasser: Tharwat, Alaa, Houssein, Essam H., Ahmed, Mohammed M., Hassanien, Aboul Ella, Gabel, Thomas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.08.2018
Springer Nature B.V
Schlagworte:
Archives & records
Artificial Intelligence
Classification
Computer Science
Genetic algorithms
Machines
Manufacturing
Mechanical Engineering
Multiple objective analysis
Optimization algorithms
Pareto optimum
Particle swarm optimization
Processes
Sorting algorithms
Pareto optimal solutions
Multi-objective optimization
Grasshopper optimization algorithm
Unconstrained optimization
Evolutionary algorithm
Constrained optimization
ISSN:0924-669X, 1573-7497
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Zusammenfassung:Grasshopper Optimization Algorithm (GOA) was modified in this paper, to optimize multi-objective problems, and the modified version is called Multi-Objective Grasshopper Optimization Algorithm (MOGOA). An external archive is integrated with the GOA for saving the Pareto optimal solutions. The archive is then employed for defining the social behavior of the GOA in the multi-objective search space. To evaluate and verify the effectiveness of the MOGOA, a set of standard unconstrained and constrained test functions are used. Moreover, the proposed algorithm was compared with three well-known optimization algorithms: Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Ant Lion Optimizer (MOALO), and Non-dominated Sorting Genetic Algorithm version 2 (NSGA-II); and the obtained results show that the MOGOA algorithm is able to provide competitive results and outperform other algorithms.
Bibliographie:ObjectType-Article-1
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-017-1074-1