Particle swarm optimization with state-based adaptive velocity limit strategy

Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive VL strategies have been introduced with which the performance of PSO can be improved. However, the existing adaptive VL strategies simply adjust their VL based on iterations, leading to unsatisfactory optimization results because of the incompatibility between VL and the current searching state of particles. To deal with this problem, a novel PSO variant with state-based adaptive velocity limit strategy (PSO-SAVL) is proposed. In the proposed PSO-SAVL, VL is adaptively adjusted based on the evolutionary state estimation (ESE) in which a high value of VL is set for global searching state and a low value of VL is set for local searching state. Besides that, limit handling strategies have been modified and adopted to improve the capability of avoiding local optima. The good performance of PSO-SAVL has been experimentally validated on a wide range of benchmark functions with 50 dimensions. The satisfactory scalability of PSO-SAVL in high-dimension and large-scale problems is also verified. Besides, the merits of the strategies in PSO-SAVL are verified in experiments. Sensitivity analysis for the relevant hyper-parameters in state-based adaptive VL strategy is conducted, and insights in how to select these hyper-parameters are also discussed.Comment: 33 pages, 8 figure

A Vector Grouping Learning Brain Storm Optimization Algorithm for Global Optimization Problems

The original brain storm optimization (BSO) method does not rationally compromise global exploration and local exploitation capability, which results in the premature convergence when solving complicated optimization problems like the shifted or shifted rotated functions. To address this problem, the paper develops a vector grouping learning BSO (VGLBSO) method. In VGLBSO, the individuals’ creation based on vector grouping learning (IC-VGL) scheme is first developed to improve the population diversity and compromise the global exploration and local exploitation capability. Moreover, a hybrid individuals’ update (H-IU) scheme is established by reasonably combing two different individuals’ update schemes, which further compromises the global exploration and local exploitation capability. Finally, the random grouping (RG) scheme, instead of K-means grouping is allowed to shrink the computational cost and maintain the diversity of the information exchange between different individuals. Twenty-eight popular benchmark functions are used to compare VGLBSO with 12 BSO and nine swarm intelligence methods. Experimental results present that VGLBSO achieves the best overall performance including the global search ability, convergence speed, and scalability amongst all the compared algorithms