Abstract
As rising swarm intelligence, fireworks algorithm (FWA) is designed to search the global optimum by the cooperation between the firework with the best fitness named as core firework (CF) and the other non-CFs. Loser-out tournament based fireworks algorithm (LoTFWA) is the most pioneering variant characterized by using competition as a new manner of interaction. However, its independent selection operator may prevent non-CFs from aggregating to CF in the late evolutionary phase if they fall into different local optima. This work proposes a last-position elimination-based fireworks algorithm which allocates more fireworks in the initial process of the optimization to search and locate the scattered local optima. Then for every fixed number of generations, the firework with the worst performance is eliminated and its budget of sparks is reallocated to other fireworks. In the final stage of optimization, only CF survives with all the budget of sparks and thus the aggregation of non-CFs to CF is ensured. Extensive experimental results performed on both CEC2013 and CEC2015 benchmarks covering 43 functions show that the proposed algorithm significantly outperforms most of the state-of-the-art FWA variants.
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Acknowledgement
This work is supported by China NSF under Grants No. 61572359 and 61272271.
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Zhang, J., Li, W. (2019). Last-Position Elimination-Based Fireworks Algorithm for Function Optimization. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_25
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DOI: https://doi.org/10.1007/978-3-030-26369-0_25
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