Deterministic Particle Swarm Optimizer with the Convergence and Divergence Dynamics
Tomoyuki Sasaki, Hidehiro NAKANO, Arata Miyauchi, Akira Taguchi · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2017
In this paper, we propose a new paradigm of deterministic PSO, named piecewise-linear particle swarm optimizer (PPSO). In PPSO, each particle has two search dynamics, a convergence mode and a divergence mode. The trajectory of each particle is switched between the two dynamics and is controlled by parameters. We analyze convergence condition of each particle and investigate parameter conditions to allow particles to converge to an equilibrium point through numerical experiments. We further compare solving performances of PPSO. As a result, we report here that the solving performances of PPSO are substantially the same as or superior to those of PSO.