Multi-guiding spark fireworks algorithm: Solving multimodal functions by multiple guiding sparks in fireworks algorithm

Many real-world problems can be abstracted as multimodal global optimization, which is one of the main challenges for optimization algorithms due to its complexity. The fireworks algorithm (FWA) is a swarm intelligence optimization algorithm that has been widely studied and applied by virtue of the...

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Vydané v:Swarm and evolutionary computation Ročník 85; s. 101458
Hlavní autori: Meng, Xiangrui, Tan, Ying
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.03.2024
Predmet:
Evolutionary computation
Fireworks algorithm
Multi-guiding spark
Multimodal global optimization
Swarm intelligence
Fireworks algorithm
Swarm intelligence
Multi-guiding spark
Evolutionary computation
Multimodal global optimization
ISSN:2210-6502
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Shrnutí:Many real-world problems can be abstracted as multimodal global optimization, which is one of the main challenges for optimization algorithms due to its complexity. The fireworks algorithm (FWA) is a swarm intelligence optimization algorithm that has been widely studied and applied by virtue of the synergistic property among fireworks. Current FWA variants have poor exploitation capability to handle some locally complex multimodal functions, which greatly limits the application of the FWA to practical problems. To solve the above problems, in this paper, we propose the multi-guiding spark fireworks algorithm (MGFWA) to solve multimodal functions by enhancing FWA exploitation capabilities. Three different strategies which are boosted guiding vector, multi-guiding sparks, and population-based random mapping are designed to boost the guiding vector, enrich the guiding spark diversity, and fix the mapping function, separately. The validity and parameter setting of MGFWA are theoretically analyzed. Experimentally, the results on the CEC2013 and CEC2017 single objective optimization benchmarks illustrate the remarkable performance of the MGFWA compared to other typical optimization algorithms and FWA variants. Moreover, the ablation study shows each of the three parts plays an important role in the algorithm and the efficiency experiment shows the MGFWA can improve the efficiency of guiding sparks by 10%. We believe that the MGFWA can be considered the SOTA variant of the FWA. •Utilizing boosted guiding vector, multi-guiding sparks, and population-based random mapping to enhance the exploitation capability of the fireworks algorithm.•State-of-the-art performance among fireworks algorithm variants.•Superior performance on multimodal global optimization among several typical swarm intelligence optimization algorithms and evolutionary algorithms.•Extensive experiments (about 60 test functions) and theoretical analyses to demonstrate the effectiveness.
ISSN:2210-6502
DOI:10.1016/j.swevo.2023.101458