NDLI: A Q-learning-based swarm optimization algorithm for economic dispatch problem

Content Provider	Springer Nature Link
Author	Hsieh, Yi Zeng Su, Mu Chun
Copyright Year	2015
Abstract	In this paper, we treat optimization problems as a kind of reinforcement learning problems regarding an optimization procedure for searching an optimal solution as a reinforcement learning procedure for finding the best policy to maximize the expected rewards. This viewpoint motivated us to propose a Q-learning-based swarm optimization (QSO) algorithm. The proposed QSO algorithm is a population-based optimization algorithm which integrates the essential properties of Q-learning and particle swarm optimization. The optimization procedure of the QSO algorithm proceeds as each individual imitates the behavior of the global best one in the swarm. The best individual is chosen based on its accumulated performance instead of its momentary performance at each evaluation. Two data sets including a set of benchmark functions and a real-world problem—the economic dispatch (ED) problem for power systems—were used to test the performance of the proposed QSO algorithm. The simulation results on the benchmark functions show that the proposed QSO algorithm is comparable to or even outperforms several existing optimization algorithms. As for the ED problem, the proposed QSO algorithm has found solutions better than all previously found solutions.
Starting Page	2333
Ending Page	2350
Page Count	18
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	27
Issue Number	8
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2015-10-30
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Optimization Particle swarm optimization Q-learning Swarm intelligence Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A memetic particle swarm optimization algorithm for solving the DNA fragment assembly problem

Swallow swarm optimization algorithm: a new method to optimization

FAIPSO: fuzzy adaptive informed particle swarm optimization

A modified particle swarm optimization algorithm using Renyi entropy-based clustering

Multi-objective particle swarm-differential evolution algorithm

An efficient algorithm based on artificial neural networks and particle swarm optimization for solution of nonlinear Troesch’s problem

Binary optimization using hybrid particle swarm optimization and gravitational search algorithm

Multi-Verse Optimizer: a nature-inspired algorithm for global optimization

Cost optimization of mixed feeds with the particle swarm optimization method

A Q-learning-based swarm optimization algorithm for economic dispatch problem

Similar Documents

A memetic particle swarm optimization algorithm for solving the DNA fragment assembly problem

Swallow swarm optimization algorithm: a new method to optimization

FAIPSO: fuzzy adaptive informed particle swarm optimization

A modified particle swarm optimization algorithm using Renyi entropy-based clustering

Multi-objective particle swarm-differential evolution algorithm

An efficient algorithm based on artificial neural networks and particle swarm optimization for solution of nonlinear Troesch’s problem

Binary optimization using hybrid particle swarm optimization and gravitational search algorithm

Multi-Verse Optimizer: a nature-inspired algorithm for global optimization

Cost optimization of mixed feeds with the particle swarm optimization method

A Q-learning-based swarm optimization algorithm for economic dispatch problem