Vector Field Benchmark for Collective Search in Unknown Dynamic Environments

• Published in: Swarm Intelligence Vol. 11172; pp. 411 - 419
• Main Authors: Bartashevich, Palina, Knors, Welf, Mostaghim, Sanaz
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 3030005321; 9783030005320
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-00533-7_36

This paper presents a Vector Field Benchmark (VFB) generator to study and evaluate the performance of collective search algorithms under the influence of unknown external dynamic environments. The VFB generator is inspired by nature (simulating wind or flow) and constructs artificially dynamic environments based on time-dependent vector fields with moving singularities (vortices). Some experiments using the Particle Swarm Optimization (PSO) algorithm, along with two specially developed updating mechanisms for the global knowledge about the external environment, are conducted to investigate the performance of the proposed benchmarks.