This paper proposes a new modified Bare Bones Particle Swarms algorithm employed Differential Evolution strategy (BBPSO-DE) for large scale optimization. To the best of the authors' knowledge, a number of modified Particle Swarm Optimization algorithms have been suggested to solve large scale problems. However, the performance of these algorithms are influenced by their control parameters. The Bare Bones Particle Swarms Optimizer (BBPSO) is a simple and parameter-free algorithm where the positions of particles are sampled from Gaussian distribution. Although BBPSO is extensively used in literature, it also suffers premature convergence problem. In this paper, several strategies are integrated into BBPSO to alleviate premature convergence. These manipulations are mainly including using ring neighbourhood topology, introducing particle's local best position and centroid position of all particles to scale the standard deviation. If a particle's local best position is equal to personal best position, incorporated mutation and crossover operators of DE strategy is adopted in modified BBPSO, namely BBPSO-DE. Experimental results on test functions up to 1000 dimensions show that BBPSO-DE is a very competitive algorithm for solving both unimodal and multimodal problems. Furthermore, one key advantage of proposed BBPSO-DE is that there is no need to specify any control parameters that are often required in traditional stochastic algorithms.
['Shiqin Yang', 'Yuji Sato']
Modified bare bones particle swarm optimization with differential evolution for large scale problem