A new hybrid particle swarm optimization with variable neighborhood search for solving unconstrained global optimization problems

Citation:
Ali, A. F., A. E. Hassanien, V. Snasel, and M. F.Tolba, "A new hybrid particle swarm optimization with variable neighborhood search for solving unconstrained global optimization problems", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014.

Date Presented:

22-24 June

Over the past few decades, metaheuristics have been emerged
to combine basic heuristic techniques in higher level frameworks to explore
a search space in an ecient and an e ective way. Particle swarm
optimization (PSO) is one of the most important method in meta-heuristics
methods, which is used for solving unconstrained global optimization
prblems. In this paper, a new hybrid PSO algorithm is combined with
variable neighborhood search (VNS) algorithm in order to search for the
global optimal solutions for unconstrained global optimization problems.
The proposed algorithm is called a hybrid particle swarm optimization
with a variable neighborhood search algorithm (HPSOVNS). HPSOVNS
aims to combine the PSO algorithm with its capability of making wide
exploration and deep exploitation and the VNS algorithm as a local
search algorithm to re ne the overall best solution found so far in each
iteration. In order to evaluate the performance of HPSOVNS, we compare
its performance on nine di erent kinds of test benchmark functions
with four particle swarm optimization based algorithms with di erent
varieties. The results show that HPSOVNS algorithm achieves better
performance and faster than the other algorithms.