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Performance comparison of metaheuristic algorithms using a modified Gaussian fitness landscape generator

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Abstract

Various metaheuristic optimization algorithms are being developed to obtain optimal solutions to real-world problems. Metaheuristic algorithms are inspired by various metaphors, resulting in different search mechanisms, operators, and parameters, and thus algorithm-specific strengths and weaknesses. Newly developed algorithms are generally tested using benchmark problems. However, for existing traditional benchmark problems, it is difficult for users to freely modify the characteristics of a problem. Thus, their shapes and sizes are limited, which is a disadvantage. In this study, a modified Gaussian fitness landscape generator is proposed based on a probability density function, to make up for the disadvantages of traditional benchmark problems. The fitness landscape developed in this study contains a total of six features and can be employed to easily create various problems depending on user needs, which is an important advantage. It is applied to quantitatively evaluate the performance and reliability of eight reported metaheuristic algorithms. In addition, a sensitivity analysis is performed on the population size for population-based algorithms. Furthermore, improved versions of the metaheuristic algorithm are considered, to investigate which performance aspects are enhanced by applying the same fitness landscape. The modified Gaussian fitness landscape generator can be employed to compare the performances of existing optimization algorithms and to evaluate the performances of newly developed algorithms. In addition, it can be employed to develop methods of improving algorithms by evaluating their strengths and weaknesses.

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Funding

This work was supported by the National Research Foundation of Korea (NRF), Grant funded by the Korea Government (MSIT) (No. 2019R1A2B5B03069810), and supported by a Korea University Grant.

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Authors and Affiliations

  1. Research Institute for Mega Construction, Korea University, Seoul, South Korea

    Ho Min Lee

  2. Department of Civil Engineering, Keimyung University, Daegu, South Korea

    Donghwi Jung

  3. Department of Mechanical Engineering, University of Science and Culture, Tehran, Iran

    Ali Sadollah

  4. School of Civil, Environmental and Architectural Engineering, Korea University, 306, Eng. Building, Anam-dong, Seongbuk-gu, Seoul, 02841, South Korea

    Joong Hoon Kim

Authors
  1. Ho Min Lee
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  2. Donghwi Jung
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  3. Ali Sadollah
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  4. Joong Hoon Kim
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Correspondence to Joong Hoon Kim.

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Communicated by V. Loia.

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Lee, H.M., Jung, D., Sadollah, A. et al. Performance comparison of metaheuristic algorithms using a modified Gaussian fitness landscape generator. Soft Comput 24, 7383–7393 (2020). https://doi.org/10.1007/s00500-019-04363-y

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