Abstract:
As a new kind of swarm intelligence algorithms, bat algorithm is inspired by the bat's echolocation model to search an optimization solution. In this paper, we propose a ...Show MoreMetadata
Abstract:
As a new kind of swarm intelligence algorithms, bat algorithm is inspired by the bat's echolocation model to search an optimization solution. In this paper, we propose a novel bat algorithm based on collaborative and dynamic learning of opposite population. The proposed algorithm adapts a collaborative strategy to generate the opposite population. Therefore more possible opposite individuals can be dynamically learned and added to the population. We also present elite choices for the current and the opposite population. In this way, the search diversity and search intensity can be achieved. The experimental results of 8 typical test functions show that the proposed algorithm has the characteristics of fast convergence and avoiding falling into local optimal solution.
Published in: 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD))
Date of Conference: 09-11 May 2018
Date Added to IEEE Xplore: 16 September 2018
ISBN Information: