Bibliography


Featured Publications


PDF

Distance based parameter adaptation for Success-History based Differential Evolution

Adam Viktorin, Roman Senkerik, Michal Pluhacek, Tomas Kadavy, Ales Zamuda

Swarm and Evolutionary Computation Volume 50, November 2019

This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adaptation in Success-History based Adaptive Differential Evolution (SHADE), which can be used as a framework to all SHADE-based algorithms. The performance impact of the proposed method is shown on the real-parameter single objective optimization (CEC2015 and CEC2017) benchmark sets in 10, 30, 50, and 100 dimensions for all SHADE, L-SHADE (SHADE with linear decrease of population size), and jSO algorithms. The proposed distance based parameter adaptation is designed to address the premature convergence of SHADE–based algorithms in higher dimensional search spaces to maintain a longer exploration phase. This design effectiveness is supported by presenting a population clustering analysis, along with a population diversity measure. Also, the new distance based algorithm versions (Db_SHADE, DbL_SHADE, and DISH) have obtained significantly better optimization results than their canonical counterparts (SHADE, L_SHADE, and jSO) in 30, 50, and 100 dimensional functions.

Cite as:

A. Viktorin, R. Senkerik, M. Pluhacek, T. Kadavy, A. Zamuda, Distance based parameter adaptation for Success-History based Differential Evolution, Swarm Evol. Comput. 50 (2019) 100462. doi:10.1016/J.SWEVO.2018.10.013.


PDF

Particle swarm optimization algorithm driven by multichaotic number generator

Michal Pluhacek,Roman Senkerik, Ivan Zelinka

Soft Computing, April 2014, Volume 18, Issue 4, pp 631–639

In this paper, the utilization of different chaotic systems as pseudo-random number generators (PRNGs) for velocity calculation in the PSO algorithm are proposed. Two chaos-based PRNGs are used alternately within one run of the PSO algorithm and dynamically switched over when a certain criterion is met. By using this unique technique, it is possible to improve the performance of PSO algorithm as it is demonstrated on different benchmark functions.

Cite as:

M. Pluhacek, R. Senkerik, I. Zelinka, Particle swarm optimization algorithm driven by multichaotic number generator, Soft Comput. 18 (2014) 631–639. doi:10.1007/s00500-014-1222-z.