Novel Swarm Intelligence Models, Algorithms and Their Applications
摘要截稿:
全文截稿: 2018-07-30
影响因子: 3.818
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 工程技术 - 2区
• 小类 : 计算机：人工智能 - 2区
• 小类 : 计算机：理论方法 - 2区
Overview
In recent years, several researchers have paid greater attention to swarm intelligence which refers to the collective behaviors of decentralized, self-organized and populated systems in nature biological systems, social phenomena and artificial swarm system. By such inspirations, there are a plenty of models and algorithms under the title of swarm intelligence to be put forward by showing an excellent performance over the state-of-the-art of many swarm and evolutionary computation algorithms. Besides, there are many real-world applications of swarm intelligence methods to be reported in a variety of technical reviews and reports, which one more time demonstrated the effectiveness of swarm intelligence.
The ICSI International Conference series since 2010 has nine years of glorious history and has become a highly reputed forum for researchers to share the latest advances in theories, technologies, and applications of swarm intelligence and related areas.
This special issue is organized not only for ICSI conference authors but also for all researchers who are interested in these topics below. Papers are solicited on swarm intelligence models and algorithms as well as their applications, in particular, multi-objective optimization applications. Potential topics include, but are not limited to:
- Novel Swarm Intelligence Models / Algorithms including Fireworks Algorithm, Cuckoo Search, Firefly algorithm, Grey Wolf Optimization, Fruit Fly Algorithm and so on provided the proposed techniques show clear and consistent superiority over the existing best known optimizers for a similar problem domain on the well accepted set of benchmarks and/or real world problem instances.
- Advances in Particle Swarm Optimization and its variants
- Advances in the Ant Colony Optimization and related algorithms
- Theoretical analysis of the advanced swarm intelligence algorithms with clear practical implication of the results derived.
- Advances in Foraging-based optimization algorithms including Artificial Bee Colony (ABC), Bacterial Foraging, Honey Bee Foraging, and Group Search Optimization and so on.
- Swarm Intelligence based algorithms for optimization in complex scenarios including:
