Fast adaptive search on the line in dual environments

Junqi Zhang, Yuheng Wang, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A stochastic point location problem considers that a learning mechanism (agent, algorithm, etc.) searches the target point on a one-dimensional domain by operating a controlled random walk after receiving some direction information from a stochastic environment. A method named Adaptive Step Search has been the fastest algorithm so far for solving a stochastic point location problem, which can be applied in Particle Swarm Optimization (PSO), the establishment of epidemic models and many other scenarios. However, its application is theoretically restrained within the range of informative environment in which the probability of an environment providing a correct suggestion is strictly bigger than a half. Namely, it does not work in a deceptive environment where such a probability is less than a half. In this paper, we present a novel promotion to overcome the difficult issue facing Adaptive Step Search, by means of symmetrization and buffer techniques. The new algorithm is able to operate a controlled random walk in both informative and deceptive environments and to converge eventually without performance loss. Finally, experimental results demonstrate that the proposed scheme is efficient and feasible in dual environments.

Original languageEnglish (US)
Title of host publication2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PublisherIEEE Computer Society
Pages1540-1545
Number of pages6
ISBN (Electronic)9781509067800
DOIs
StatePublished - Jul 1 2017
Event13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China
Duration: Aug 20 2017Aug 23 2017

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2017-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other13th IEEE Conference on Automation Science and Engineering, CASE 2017
Country/TerritoryChina
CityXi'an
Period8/20/178/23/17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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