An Adaptive Level-Based Learning Swarm Optimizer for Large-Scale Optimization

This paper proposes an adaptive version of an existing promising large-scale optimizer named level-based learning swarm optimizer (LLSO). Though such an optimizer has shown promising performance in dealing with large-scale optimization, it is much sensitive to its two introduced parameters. To alleviate this dilemma, this paper devises two simple yet effective adaptive adjustment strategies for the two parameters, leading to an adaptive LLSO(ALLSO). Specifically, this paper first defines a novel aggregation indicator based on the difference between the global best fitness and the averaged fitness of the swarm, to roughly evaluate the evolution state of the swarm. Then, based on this indicator, two adaptive adjustment strategies are devised to dynamically determine the values of the two parameters during the evolution. With these two strategies, the swarm is expected to maintain a potentially good balance between intensification and diversification. Extensive experiments conducted on two widely used large-scale benchmark sets demonstrate that the two adaptive strategies effectively improve the performance of LLSO.