Paper Info
Title
A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems
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'.
Year
DOI
Venue
2022
10.1109/JAS.2022.105752
IEEE/CAA Journal of Automatica Sinica
Keywords
DocType
Volume
Adaptive strategy,fireworks algorithm,multimodal multiobjective optimization problems (MMOP)
Journal
9
Issue
ISSN
Citations 
8
2329-9266
0
PageRank 
References 
Authors
0.34
50
7
Name
Order
Citations
PageRank
Shoufei Han100.34
Kun Zhu200.34
MengChu Zhou38989534.94
Xiaojing Liu400.34
Haoyue Liu5212.38
Yusuf Al-Turki65712.44
Abdullah Abusorrah700.34