An Efficient Modified Particle Swarm Optimization Algorithm for Solving Mixed-Integer Nonlinear Programming Problems
- 10.2991/ijcis.d.190402.001How to use a DOI?
- Particle swarm optimization; Mixed-integer nonlinear programming; Constrained optimization; Simulated annealing
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.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Ying Sun AU - Yuelin Gao PY - 2019 DA - 2019/04/12 TI - An Efficient Modified Particle Swarm Optimization Algorithm for Solving Mixed-Integer Nonlinear Programming Problems JO - International Journal of Computational Intelligence Systems SP - 530 EP - 543 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.190402.001 DO - 10.2991/ijcis.d.190402.001 ID - Sun2019 ER -