A Modified Algorithm of Bare Bones Particle Swarm Optimization
Bare bones particle swarm optimization (PSO) greatly simplifies the particles swarm by stripping away the velocity rule, but performance seems not good as canonical one in some test problems. Some studies try to replace the sampling distribution to improve the performance, but there are some problems in the algorithm itself. This paper proposes a modified algorithm to solve these problems. In addition to some benchmark test functions, we also conducted an application of real-world time series forecasting with support vector regression to evaluate the performance of the proposed PSO algorithm. The results indicate that the modified bare bones particle swarm optimization can be an efficient alternative due to the smaller confidence intervals and fast convergence characteristics.
Keywords: Heuristic Optimization, Particle Swarm Optimization, Bare Bones PSO, Support Vector Regression, Time Series Forecasting
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