Structural change of plastocyanin. Molecular interaction and protein folding

Hirota, Shun; Funasaki, Noriaki; Fujimoto, Yukari; Okumura, Hiroki; Hamasaki-Katagiri, Nobuko; Watanabe, Yoshihito; Choi, Jungkweon; Terazima, Masahide; Arie, Sachiko; Tanaka, Kentaro; Shionoya, Mitsuhiko; Okajima, Toshihide; Takabe, Tetsuko

doi:10.1016/s0162-0134(03)80457-9

Cited by 1 publication

(5 citation statements)

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“…When the random value is less than pk, the random imitation learning behavior is performed, otherwise, the best particle m is treated as the learnt object. This can be formulated as Equation (12).…”

Section: Attraction Between Particlesmentioning

confidence: 99%

“…where r5 and r6 are random values in (0,1) and k stands for the dimension. It is noted that Equation (12) and Equation (13) are the improvements on Equation (3) according to molecular repulsion. Where △Xi,k (t+1) and △Xj,k (t+1) are the particle behavior modification, as is shown in Equation 4.…”

Section: Repulsion Between Particlesmentioning

confidence: 99%

“…On this basis, this paper proposes an exclusion strategy. Considering that the PSO algorithm is a process of random search, random factors are added to Equations (12) and (13) to better realize the process of exploration and exploitation.…”

Section: Repulsion Between Particlesmentioning

confidence: 99%

“…The second step is to perform iterative learning. First, the swarm is sorted according to the fitness, then the Euclidean distance is calculated to determine whether the attraction strategy shown in Equation (12) or the exclusion strategy shown in Equation (13) and Equation 14is implemented. Table II shows the details of the MISL-PSO algorithm.…”

Section: E Misl-pso Algorithmmentioning

confidence: 99%

“…Molecular interactions include attractive and repulsive forces between molecules, and help scientists understand a molecular function and its behavior in different areas including protein folding [11,12], drug design [13,14], voltage sensors [15,16], and nanotechnology [17]. However, molecular interactions have only few applications in swarm intelligence.…”

Section: Introductionmentioning

confidence: 99%

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A Molecular Interactions-Based Social Learning Particle Swarm Optimization Algorithm

et al. 2020

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Social learning particle swarm optimization (SL-PSO) allows individuals to learn from others to improve the scalability with easy parameter settings. However, it still suffers from the poor convergence for those multi-modal problems due to the loss of swarm diversity. To improve both the diversity and the convergence, this paper proposes a novel algorithm to apply the mechanism of molecular interactions to SL-PSO, in which the molecular attraction aims to improve the convergence, and the molecular repulsion intends to enhance the diversity. In the experiments, we compare our algorithm with the SL-PSO algorithm and other representative PSO and evolutionary algorithms on 49 benchmark functions. The results show the performance of the proposed algorithm is better than that of the SL-PSO algorithm and other representative PSO and evolutionary algorithms on average. This work builds the solid foundation for the integration of the molecular interaction mechanism with PSO and other optimization algorithms.

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Section: Attraction Between Particlesmentioning

confidence: 99%

Section: Repulsion Between Particlesmentioning

confidence: 99%