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MOGOA algorithm for constrained and unconstrained multi-objective optimization problems

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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.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences, 60318, Frankfurt am Main, Germany

    Alaa Tharwat & Thomas Gabel

  2. Faculty of Engineering, Suez Canal University, Ismailia, Egypt

    Alaa Tharwat

  3. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein & Mohammed M. Ahmed

  4. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  5. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Alaa Tharwat, Essam H. Houssein, Mohammed M. Ahmed & Aboul Ella Hassanien

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  1. Alaa Tharwat
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  2. Essam H. Houssein
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  4. Aboul Ella Hassanien
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Correspondence to Alaa Tharwat.

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Tharwat, A., Houssein, E.H., Ahmed, M.M. et al. MOGOA algorithm for constrained and unconstrained multi-objective optimization problems. Appl Intell 48, 2268–2283 (2018). https://doi.org/10.1007/s10489-017-1074-1

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