Particle Field Optimization: A New Paradigm for Swarm Intelligence

被引：0

作者：

Bell, Nathan ^{[1
]}

Oommen, B. John ^{[1
]}

机构：

[1] Carleton Univ, Ottawa, ON, Canada

来源：

PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS (AAMAS'15) | 2015年

关键词：

Particle Swarm Optimization/Intelligence;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Particle Swarm Optimization (PSO) has been a popular meta-heuristic for black-box optimization for almost two decades. In essence, within this paradigm, the system is fully defined by a swarm of "particles" each characterized by a set of features such as its position, velocity and acceleration. The consequent optimized global best solution is obtained by comparing the personal best solutions of the entire swarm. Many variations and extensions of PSO have been developed since its creation in 1995, and the algorithm remains a popular topic of research. In this work we submit a new, abstracted, perspective of the PSO system, where we attempt to move away from the swarm of individual particles, but rather characterize each particle by a field or distribution. The strategy that updates the various fields is akin to Thompson's sampling. By invoking such an abstraction, we present the novel Particle Field Optimization (PFO) algorithm which harnesses this new perspective to achieve a model and behavior completely distinct from the family of traditional PSO systems.

引用

页码：257 / 265

页数：9

共 19 条

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