A core firework updating information guided dynamic fireworks algorithm for global optimization
As a new variant of swarm intelligence algorithm, fireworks algorithm (FWA) exhibits promising performance on a wide set of optimization problems, for which the fireworks algorithm has been concentrated on and investigated by researchers recently. This paper aims to improve the performance of the FWA by exploiting updating information of the core firework to guide the algorithm’s searching process. Based on this mentality, this paper ameliorated the explosion strategy of core firework of dynamic fireworks algorithm (dynFWA). The proposed algorithm, named dynPgFWA in this paper, improved FWA from two aspects: amplifying the explosion amplitude on the direction on which core firework is updated, and making more sparks which are generated by core firework distributed on this direction to enhance the algorithm’s searching ability on updating direction. A numerical experiment on CEC2015 and CEC2017 test suite was implemented to verify the performance of the proposed algorithm. Results of the experiment indicated that dynPgFWA outperformed the compared evolutionary algorithms in the quality of solutions.
KeywordsFireworks algorithm Updating information Core firework Swarm intelligence algorithm Evolutionary computing
This study was funded by the National Natural Science Foundation Program of China (61572116, 61572117, 61502089), the National key Technology R&D Program of the Ministry of Science and Technology (2015BAH09F02), the Provincial Scientific and Technological Project (2015302002) and the Special Fund for Fundamental Research of Central Universities of Northeastern University (N150408001, N150404009).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no competing interests.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Barraza J et al (2017) Iterative fireworks algorithm with fuzzy coefficients. In: 2017 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEEGoogle Scholar
- Beni G, Wang J (1993) Swarm intelligence in cellular robotic systems. In: Dario P, Sandini G, Aebischer P (eds) Robots and biological systems: towards a new bionics. Springer, pp 703–712Google Scholar
- Chen J, Yang Q, Ni J et al (2015) An improved fireworks algorithm with landscape information for balancing exploration and exploitation. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 1272–1279Google Scholar
- Chen S et al (2018) PS-FW: a hybrid algorithm based on particle swarm and fireworks for global optimization. Comput Intell Neurosci 2018:1–27Google Scholar
- Ding K, Zheng S, Tan Y (2013) A gpu-based parallel fireworks algorithm for optimization. In: Proceedings of the 15th annual conference on genetic and evolutionary computation. ACM, pp 9–16Google Scholar
- Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol 4, pp 1942–1948Google Scholar
- Knowles J, Thiele L, Zitzler E (2006) A tutorial on the performance assessment of stochastic multiobjective optimizers. Tik Rep 214:327–332Google Scholar
- Lana I, Del Ser J, Vélez M (2017) A novel fireworks algorithm with wind inertia dynamics and its application to traffic forecasting. In: 2017 IEEE congress on evolutionary computation (CEC). IEEEGoogle Scholar
- Li J, Tan Y (2015) Orienting mutation based fireworks algorithm. In: IEEE Congress on evolutionary computation (CEC). IEEE, pp 1265–1271Google Scholar
- Li J, Zheng S, Tan Y (2014) Adaptive fireworks algorithm. In: IEEE Congress on evolutionary computation (CEC). IEEE, pp 3214–3221Google Scholar
- Liang JJ, Qu BY, Suganthan PN et al (2014) Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical Report201411A, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, SingaporeGoogle Scholar
- Nowak K, Märtens M, Izzo D (2014) Empirical performance of the approximation of the least hypervolume contributor. In: Bartz-Beielstein T, Branke J, Filipič B, Smith J (eds) International conference on parallel problem solving from nature. Springer, Cham, pp 662–671Google Scholar
- Si T, Ghosh R (2015) Explosion sparks generation using adaptive transfer function in firework algorithm. In: IEEE third international conference on signal processing, communications and networking, pp 305–314Google Scholar
- Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: International conference on advances in swarm intelligence. Springer, Berlin, pp 355–364Google Scholar
- Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. In: IEEE congress on evolutionary computation. IEEE, pp 1658–1665Google Scholar
- Xia C et al (2018) A novel mixed-variable fireworks optimization algorithm for path and time sequence optimization in WRSNs. In: International conference on communicatins and networking in China. Springer, ChamGoogle Scholar
- Ye W, Wen J (2017) Adaptive fireworks algorithm based on simulated annealing. In: 2017 13th International conference on computational intelligence and security (CIS). IEEEGoogle Scholar
- Yu C, Tan Y (2015) Fireworks algorithm with covariance mutation. In: IEEE Congress on Evolutionary computation (CEC). IEEE, pp 1250–1256Google Scholar
- Yu C, Li J, Tan Y (2014) Improve enhanced fireworks algorithm with differential mutation. In: IEEE international conference on systems, man and cybernetics. IEEE, pp 264–269Google Scholar
- Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958Google Scholar
- Zhang B, Zhang MX, Zheng YJ (2014) A hybrid biogeography-based optimization and fireworks algorithm. In: IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 3200–3206Google Scholar
- Zheng S, Janecek A, Tan Y (2013) Enhanced fireworks algorithm. In: IEEE Congress on evolutionary computation. IEEE, pp 2069–2077Google Scholar
- Zheng S, Janecek A, Li J et al (2014) Dynamic search in fireworks algorithm. In: IEEE Congress evolutionary computation (CEC). IEEE, pp 3222–3229Google Scholar
- Zheng S, Yu C, Li J et al (2015c) Exponentially decreased dimension number strategy-based dynamic search fireworks algorithm for solving CEC2015 competition problems. In: 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 1083–1090Google Scholar