Opposition-based learning competitive particle swarm optimizer with local search

Abstract

In order to improve the optimization ability of particle swarm optimization (PSO) algorithms in complex optimization problems, especially in high dimensional problems, an opposition-based learning competitive particle swarm optimization algorithm based on Solis & Wets (SW-OBLCSO) local search is proposed. The SW-OBLCSO algorithm adopts two learning mechanisms, namely competitive learning and opposition-based learning. An individual-based local search operator is also introduced. The SW-OBLCSO algorithm is compared with various optimization algorithms on the 10 benchmark functions and 12 complex test functions in different dimensions. The experimental results show that the proposed algorithm exhibits outstanding performance in convergence speed and global search ability. Performance comparison on the fuzzy cognitive map (FCM) learning problems shows that the SW-OBLCSO algorithm also has excellent performance when dealing with practical problems.