Paper Info
Title
Integrating particle swarm optimization with reinforcement learning in noisy problems
Abstract
Noisy optimization problems arise very often in real-life applications. A common practice to tackle problems characterized by uncertainties, is the re-evaluation of the objective function at every point of interest for a fixed number of replications. The obtained objective values are then averaged and their mean is considered as the approximation of the actual objective value. However, this approach can prove inefficient, allocating replications to unpromising candidate solutions. We propose a hybrid approach that integrates the established Particle Swarm Optimization algorithm with the Reinforcement Learning approach to efficiently tackle noisy problems by intelligently allocating the available computational budget. Two variants of the proposed approach, based on different selection schemes, are assessed and compared against the typical alternative of equal sampling. The results are reported and analyzed, offering significant evidence regarding the potential of the proposed approach.
Year
DOI
Venue
2012
10.1145/2330163.2330173
GECCO
Keywords
Field
DocType
objective value,actual objective value,available computational budget,hybrid approach,common practice,reinforcement learning approach,objective function,noisy problem,integrating particle swarm optimization,noisy optimization problem,optimization problem,point of interest,particle swarm optimization,reinforcement learning
Particle swarm optimization,Mathematical optimization,Computer science,Budget allocation,Multi-swarm optimization,Sampling (statistics),Artificial intelligence,Point of interest,Optimization problem,Machine learning,Metaheuristic,Reinforcement learning
Conference
Citations 
PageRank 
References 
10
0.61
11
Authors
5
Name
Order
Citations
PageRank
Grigoris S. Piperagkas1201.79
George Georgoulas29613.15
Konstantinos E. Parsopoulos319916.50
Chrysostomos D. Stylios464952.33
aristidis likas51926140.40