Paper Info
Title
An Efficient Modified Particle Swarm Optimization Algorithm For Solving Mixed-Integer Nonlinear Programming Problems
Abstract
This paper presents an efficient modified particle swarm optimization (EMPSO) algorithm for solving mixed-integer nonlinear programming problems. In the proposed algorithm, a new evolutionary strategies for the discrete variables is introduced, which can solve the problem that the evolutionary strategy of the classical particle swarm optimization algorithm is invalid for the discrete variables. An update strategy under the constraints is proposed to update the optimal position, which effectively utilizes the available information on infeasible solutions to guide particle search. In order to evaluate and analyze the performance of EMPSO, two hybrid particle swarm optimization algorithms with different strategies are also given. The simulation results indicate that, in terms of robustness and convergence speed, EMPSO is better than the other algorithms in solving 14 test problems. A new performance index (NPI) is introduced to fairly compare the other two algorithms, and in most cases the values of the NPI obtained by EMPSO were superior to the other algorithms. (c) 2019 The Authors. Published by Atlantis Press SARL.
Year
DOI
Venue
2019
10.2991/ijcis.d.190402.001
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
Keywords
Field
DocType
Particle swarm optimization, Mixed-integer nonlinear programming, Constrained optimization, Simulated annealing
Simulated annealing,Particle swarm optimization,Mathematical optimization,Algorithm,Nonlinear mixed integer programming,Mathematics,Constrained optimization
Journal
Volume
Issue
ISSN
12
2
1875-6891
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Ying Sun129140.03
Yuelin Gao210518.34