Abstract
Fireworks algorithm (FWA) is a relatively new metaheuristic in swarm intelligence and EFWA is an enhanced version of FWA. This paper presents a new improved method, named IEFWA, which modifies EFWA in two aspects: a new Gaussian explosion operator that enables new sparks to learn from more exemplars in the population and thus improves solution diversity and avoids being trapped in local optima, and a new population selection strategy that enables high-quality solutions to have high probabilities of entering the next generation without incurring high computational cost. Numerical experiments show that the IEFWA algorithm outperforms EFWA on a set of benchmark function optimization problems.
Supported by grants from National Natural Science Foundation (No. 61105073) and Zhejiang Provincial Natural Science Foundation (No. LY14F030011) of China.
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Zhang, B., Zhang, M., Zheng, YJ. (2014). Improving Enhanced Fireworks Algorithm with New Gaussian Explosion and Population Selection Strategies. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_7
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DOI: https://doi.org/10.1007/978-3-319-11857-4_7
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