An optimizer using swarm of chaotic dynamical particles

Abstract

A novel optimization method called, "optimizer using swarm of chaotic dynamical particles," (OSCDP) is proposed. The proposed method searches for an optimal solution on the basis of particles governed by chaotic dynamics. The chaotic dynamics make the particles behave in a complex way, by stretching and holding mechanisms, even though the dynamics do not contain any stochastic terms. The particles share information of their best position that is a candidate of the optimal solution, as with particle swarm optimization (PSO). The complex behavior and the sharing information mechanism enable the proposed method to search for the optimal solution. This method consists of only three system parameters, making it suitable for implementing the algorithm. We compared the proposed method with other deterministic PSOs and the standard PSO, and found that the proposed method exhibited a better searching performance.

Bifurcation analysis by particle swarm optimization

“…This study has used the standard PSO since this type of PSO is the simplest and includes only a fraction of parameters. However, we should compare the standard PSO used in this study to modified PSOs including deterministic PSO [14][15][16] or other metaheuristic optimizers [17], such as the Nelder-Mead simplex method and differential evolution [18]. Moreover, we also would like to develop a NLPSO-based bifurcation curve tracing method and a MATLAB/Python toolbox or software to carry out our method.…”

Piecewise linear switched dynamical systems: A review

“…Second, the MPL can be implemented by a simple hardware and chaos generation is confirmed experimentally [13,14]. Third, the MPL has been applied to various engineering systems including communication systems, signal processor, radar systems, and particle swarm optimizers [22][23][24][25][26]. The MPL is defined by the following second order piecewise linear equation and hysteresis switching rule: x…”

Swarm intelligence algorithm based on spiking neural-oscillator networks, coupling interactions and search performances

“…Search performances of OSNNs were compared to The number of trials (T ) 51 those of the classical PSO [3], OPRC [18] and Uncoupled OSNNs. The literature [18] shows that search performances of OPRC are superior to those of OSCDP [17]. As such, we adopted OPRC as the comparison method.…”

“…On the other hand, various D-PSO algorithms to improve search performances have been proposed [14][15][16][17][18][19]. Optimizer using Swarm of Chaotic Dynamical Particles (OSCDP) [17], Optimizer based on Piecewise-Rotational Chaotic system (OPRC) [18], and Piecewise-linear Particle Swarm Optimization (PPSO) [19], which are one of the piecewise-linear systems, have better search performances than the stochastic PSO. As such, OSCDP, OPRC and PPSO can be theoretically analyzed for behaviors of their particles.…”