A novel hybrid dynamic fireworks algorithm with particle swarm optimization
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METHODOLOGIES AND APPLICATION
A novel hybrid dynamic fireworks algorithm with particle swarm optimization Fang Zhu1 • Debao Chen1 • Feng Zou1
Ó Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In recent years, the fireworks algorithm (FWA) has attracted more and more attention due to its strong ability to solve optimization problems. However, the global performance of FWA is significantly affected by the explosion amplitude. In this paper, a dynamic fireworks algorithm with particle swarm optimization (DFWPSO) is developed to improve the global performance of FWA. In DFWPSO, a dynamic explosion amplitude mechanism based on the evolution speed of population, which is dynamically adjusted by evaluating the evolution speed of fitness in each iteration process, is designed to control the global and local searching information. Moreover, a new nonlinear minimal amplitude check strategy based on function decreasing is designed to obtain appropriate amplitude. Furthermore, a new firework updating mechanism based on particle swarm optimization (PSO) is implemented to accelerate the convergence of algorithm and cut down on computing resources. In addition, the selection operator of FWA is abandoned and all fireworks are updated by velocity and current location in each iteration process. To verify the performance of the proposed DFWPSO algorithm, three groups of the benchmark functions are used and tested for experiments. Compared with other variants of FWA and PSO variants, results show that the proposed algorithm performs competitively and effectively. Keywords Fireworks algorithm Dynamic explosion amplitude Global best firework Updating process Particle swarm optimization
1 Introduction Inspired by human intelligence, the sociality of biological groups and the laws of natural phenomena, many heuristic optimization algorithms have been presented in recent decades. Some classic representatives of these swarm intelligence algorithms, such as genetic algorithm (GA) (Tu and Yong 2004), particle swarm optimization (PSO) (Kennedy and Eberhart 1995), differential evolution (DE) (Storn and Price 1997), bat algorithm (BA) (Yang 2010), backtracking search optimization algorithm (BSA) (Civicioglu 2013) and so on, have been successfully used to solve some complex optimization problems. In addition, some variants, such as a comprehensive learning particle
Communicated by V. Loia. & Fang Zhu [email protected] 1
School of Physics and Electrical Information, Huaibei Normal University, Huaibei 235000, China
swarm optimizer (CLPSO) for global optimization of multimodal functions (Liang et al. 2006), chaotic particle swarm optimization algorithm (CSPSO) for solving combinatorial optimization problems (Xu et al. 2018), a new version of the differential evolution algorithm with selfadaptive control parameter (jDE) (Brest et al. 2006), an enhanced bat algorithm (EBA) (Yilmaz and Kucuksille 2014) and an adaptive BSA with knowledge learning (KLBSA) (Chen et al. 2018a
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