An Efficient Modified Particle Swarm Optimization Algorithm for Solving Mixed-Integer Nonlinear Programming Problems

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 strat...

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Bibliographic Details
Published in:International journal of computational intelligence systems Vol. 12; no. 2; pp. 530 - 543
Main Authors: Sun, Ying, Gao, Yuelin
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.01.2019
Springer Nature B.V
Springer
Subjects:
Constrained optimization
Evolutionary algorithms
Mixed integer
Mixed-integer nonlinear programming
Nonlinear programming
Optimization algorithms
Particle swarm optimization
Performance indices
Research Article
Simulated annealing
Mixed-integer nonlinear programming
Particle swarm optimization
Simulated annealing
Constrained optimization
ISSN:1875-6891, 1875-6883, 1875-6883
Online Access:Get full text
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Summary: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.
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ISSN:1875-6891
1875-6883
1875-6883
DOI:10.2991/ijcis.d.190402.001