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

被引:22
|
作者
Han, Shoufei [1 ,2 ]
Zhu, Kun [1 ,2 ]
Zhou, MengChu [3 ]
Liu, Xiaojing [1 ,2 ]
Liu, Haoyue [3 ]
Al-Turki, Yusuf [4 ,5 ]
Abusorrah, Abdullah [4 ,5 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211106, Peoples R China
[2] Collaborat Innovat Ctr Novel Software Technol & I, Nanjing 211106, Peoples R China
[3] New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
[4] King Abdulaziz Univ, Ctr Res Excellence Renewable Energy & Power Syst, Dept Elect & Comp Engn, Fac Engn, Jeddah 21589, Saudi Arabia
[5] King Abdulaziz Univ, KA CARE Energy Res & Innovat Ctr, Jeddah 21589, Saudi Arabia
基金
中国国家自然科学基金;
关键词
Adaptive strategy; fireworks algorithm; multimodal multiobjective optimization problems (MMOP); OPTIMIZATION; SEARCH;
D O I
10.1109/JAS.2022.105752
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 (ie., 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'.
引用
收藏
页码:1476 / 1489
页数:14
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