Prof. Ying Tan, Peking University, China, firstname.lastname@example.org
Prof. Junqi Zhang, Tongji University, China, email@example.com
Fireworks Algorithm (FWA) is a new swarm-based optimization algorithm which has different cooperation framework and search manner compared to other SI algorithms, such as Particle Swarm Optimization, Ant Colony Optimization and Genetic Algorithm. Locally, population called fireworks exploit local landscape by a simple sampling method called explosion operation. Globally, fireworks exchange condensed information and collaboratively decide parameters of their explosion. FWA achieved overwhelming success on both benchmark objective functions and real-world problems. Recent researches include many effective variants and huge amount of successful applications. FWA framework has revealed competitive performance compared with other SI optimization methods. The below image shows a dendrogram of variants of FWA.
There is also a growing trend of applying FWA to sovlve real world problem.Janecek and Tanused FWA together with Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Differential Evolution (DE), and Fish School Search for improving the initialization of Non-negative Matrix Factorization (NMF). Their results indicate that FWA could not compete with the other optimization algorithms when the number of dimensions (i.e., the rank of NMF) was small, but achieved good results when the number of dimensions was increased. Bureerat compared twelve different optimization algorithms on 35 benchmark functions with different dimensions ranging from 2 to 30. FWA was ranked as the 6-th best algorithm for optimizing these benchmark functions, which is better than GA and PSO. Gao Hongyuan and Ming Diao proposed the Cultural Firework Algorithm (CFA) which combines ideas from Cultural Algorithms (CAs) and FWA. CFA acquires problem-solving knowledge (beliefs) from the explosion of fireworks and in return makes use of that knowledge to better guide the search . Results indicate that CFA is well suited for optimizing FIR and IIR digital filters, and outperforms various PSO variants for this type of optimization problem. W.R.He et al. used FWA for the parameters optimization in the process of pattern extraction for spam detection while Zheng Shaoqiu and Tan Ying used FWA for the parameters determination of the combination of the difference orientation patterns. Both of the results indicate that the optimization of FWA gains improvements. Recently, FWA has been used for selective harmonic elimination, network eeconfiguration mass and minimisation of trusses. Tuba et al.used the GFWA and the BBFWA, respectively, to optimize the JPEG quantization table in image compression
For more information, please visit our FWA forum .
The main aim of this special session is to gather both experts’ experience and new-comers’ innovations of fireworks algorithm and its applications. We’re expecting researches on theoretical analysis and improvement of FWA and applications of all kinds of practical situations.
Full papers are invited on recent advances in the development of FWA, i.e., FWA improvements and applications. The session seeks to promote the discussion and presentation of novel works related with (but not limited to) the following issues:
|Submission deadline||January 31st, 2021|
|Notification of acceptance||March 22nd, 2021|
|Final paper submission||April 7th, 2021|
|Conference starts||June 28th, 2021|
Please follow the submission guideline from the IEEE CEC 2021 Submission Website. Special session papers are treated the same as regular conference papers. Please specify that your paper is for the Special Session on fireworks algorithm. All papers accepted and presented at CEC 2021 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.