TY - JOUR
T1 - A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems
AU - Han, Shoufei
AU - Zhu, Kun
AU - Zhou, Meng Chu
AU - Liu, Xiaojing
AU - Liu, Haoyue
AU - Al-Turki, Yusuf
AU - Abusorrah, Abdullah
N1 - Funding Information:
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).
Publisher Copyright:
© 2014 Chinese Association of Automation.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - 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'.
AB - 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'.
KW - Adaptive strategy
KW - fireworks algorithm
KW - multimodal multiobjective optimization problems (MMOP)
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U2 - 10.1109/JAS.2022.105752
DO - 10.1109/JAS.2022.105752
M3 - Article
AN - SCOPUS:85135742717
SN - 2329-9266
VL - 9
SP - 1476
EP - 1489
JO - IEEE/CAA Journal of Automatica Sinica
JF - IEEE/CAA Journal of Automatica Sinica
IS - 8
ER -