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Applied Intelligence

, Volume 48, Issue 8, pp 2268–2283 | Cite as

MOGOA algorithm for constrained and unconstrained multi-objective optimization problems

  • Alaa Tharwat
  • Essam H. Houssein
  • Mohammed M. Ahmed
  • Aboul Ella Hassanien
  • Thomas Gabel
Article

Abstract

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.

Keywords

Multi-objective optimization Grasshopper optimization algorithm Pareto optimal solutions Evolutionary algorithm Constrained optimization Unconstrained optimization 

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Alaa Tharwat
    • 1
    • 2
    • 5
  • Essam H. Houssein
    • 3
    • 5
  • Mohammed M. Ahmed
    • 3
    • 5
  • Aboul Ella Hassanien
    • 4
    • 5
  • Thomas Gabel
    • 1
  1. 1.Faculty of Computer Science and EngineeringFrankfurt University of Applied SciencesFrankfurt am MainGermany
  2. 2.Faculty of EngineeringSuez Canal UniversityIsmailiaEgypt
  3. 3.Faculty of Computers and InformationMinia UniversityMiniaEgypt
  4. 4.Faculty of Computers and InformationCairo UniversityGizaEgypt
  5. 5.Scientific Research Group in Egypt (SRGE)CairoEgypt

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