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

Shoufei Han, Kun Zhu, Meng Chu Zhou, Xiaojing Liu, Haoyue Liu, Yusuf Al-Turki, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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'.

Original languageEnglish (US)
Pages (from-to)1476-1489
Number of pages14
JournalIEEE/CAA Journal of Automatica Sinica
Issue number8
StatePublished - Aug 1 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Control and Optimization
  • Artificial Intelligence


  • Adaptive strategy
  • fireworks algorithm
  • multimodal multiobjective optimization problems (MMOP)


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