Chaos driven PSO with attractive search space border points

In this paper, a chaotic pseudorandom generator is used to enhance the performance of Particle Swarm Optimization with attractive search space borders. The original modification was proposed to prolong the exploration phase and improve the ability of the swarm to avoid local minima. The particles are randomly attracted towards the border points of the search space in each dimension. The well-known and frequently used CEC'13 Benchmark function set is used to test the performance of the proposed method and compare it to canonical PSO. © 2018 IEEE.Ministry of Education, Youth and Sports of the Czech Republic [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science Technology) [Action CA15140, Action IC1406]; A.I. Lab at the Faculty of Applied Informatics, Tomas Bata University in Zli