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Volume 9 Issue 8
Aug.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(8): 1476-1489
S. F. Han, K. Zhu, M. C. Zhou, X. J. Liu, H. Y. Liu, Y. Al-Turki, and A. Abusorrah, “A novel multiobjective fireworks algorithm and its applications to imbalanced distance minimization problems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1476–1489, Aug. 2022. doi: 10.1109/JAS.2022.105752
Citation: S. F. Han, K. Zhu, M. C. Zhou, X. J. Liu, H. Y. Liu, Y. Al-Turki, and A. Abusorrah, “A novel multiobjective fireworks algorithm and its applications to imbalanced distance minimization problems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1476–1489, Aug. 2022. doi: 10.1109/JAS.2022.105752
shu

A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

doi: 10.1109/JAS.2022.105752
  • 1.

    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, and also with the Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 211106, China

  • 2.

    Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark NJ 07102 USA

  • 3.

    Center of Research Excellence in Renewable Energy and Power Systems, Department of Electrical and Computer Engineering, Faculty of Engineering, and also with the K. A. CARE Energy Research and Innovation Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Funds:  This work was supported in part by the National Natural Science Foundation of China (62071230, 62061146002), the Natural Science Foundation of Jiangsu Province (BK20211567), and the Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia (FP-147-43)
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    Abstract
  • Recently, multimodal multiobjective optimization problems (MMOPs) have received increasing attention. Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible. Although some evolutionary algorithms for them have been proposed, they mainly focus on the convergence rate in the decision space while ignoring solutions diversity. In this paper, we propose a new multiobjective fireworks algorithm for them, which is able to balance exploitation and exploration in the decision space. We first extend a latest single-objective fireworks algorithm to handle MMOPs. Then we make improvements by incorporating an adaptive strategy and special archive guidance into it, where special archives are established for each firework, and two strategies (i.e., explosion and random strategies) are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives. Finally, we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems. Experimental results show that the proposed algorithm is superior to compared algorithms in solving them. Also, its runtime is less than its peers’.

     

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    Highlights

    • A new multiobjective fireworks algorithm is proposed
    • A special archive for each firework is established to guide firework explosion
    • An adaptive strategy is designed to ensure fast convergence and high solution diversity in decision space
    • Extensive experimental results indicate the superiority of the proposed algorithm over its state-of-the-art peers

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