A Binary Particle Swarm Optimization with the Hybrid S-Shaped and V-Shaped Transfer Function

Jiang, Lei; Liu, Jianhua; Cui, Dongli; Bu, Guannan; Zhang, Dongyang; Hu, Renyuan

doi:10.1007/978-981-15-3308-2_8

Cited by 1 publication

(2 citation statements)

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“…Interestingly, NMBPSO [87] shows a hybrid transfer function of S and V-shaped that seems really effective because it provides local (V-shaped) and global (S-shaped) search over the iterations. We argue that new algorithms in the future might use hybrid transfer functions to better balance the exploration and exploitation for other swarmbased algorithms.…”

Section: Discussion and Directionsmentioning

confidence: 99%

“…Modified versions of the V-shaped transfer function is also present in the literature, such as MBPSO [88]. Jiang et al proposed a binary version of PSO (NMBPSO) using a hybrid transfer function of S and V-shaped [87] outperforming BPSO because NMBPSO provides local search capability even at later iterations. In the same way, Minzu et al [86] suggested the application of the |tanh(v t i,d )| from (4).…”

Section: ) Bird-inspiredmentioning

confidence: 99%

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Overview on Binary Optimization Using Swarm-Inspired Algorithms

et al. 2021

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Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility. Based on simple rules, simple reactive agents -swarm (e.g. fish, bird, and ant)directly or indirectly exchange information to find an optimal solution. Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application. This scenario gets even more challenging when considering binary optimisation because of the absence of overview papers that assembles the trends, benefits and limitations of swarmbased techniques. Based on 403 scientific papers, we describe the basis of the leading binary swarm-based algorithms presenting their rationales, equations, pseudocodes, and descriptions of their applications to tackle this research gap. We also define a new classification based on the mechanism to update the solutions and the displacements, indicating that the Binary-Binary approach -binary decision variables and binary search space -is more efficient for binary optimisation in accuracy and computational cost.

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Section: Discussion and Directionsmentioning

confidence: 99%