Constrained Optimization Solution Based on an Improved Genetic Algorithm
2012 ◽
Vol 190-191
◽
pp. 334-337
Keyword(s):
A hybrid adaptive genetic algorithm is proposed for solving constrained optimization problems. The algorithm combines adaptive penalty method and smoothing technique in order to get no parameter tuning and easily escaping from the local optimal solutions. Meanwhile, local line search technique is introduced and a new crossover operator is designed for getting much faster convergence. The performance of the algorithm is tested on thirteen benchmark functions and the results indicate that the proposed algorithm is robust and effective.
2002 ◽
Vol 16
(1)
◽
pp. 23-30
◽
2014 ◽
Vol 8
(1)
◽
pp. 904-912
◽
A Simulated Annealing-Based Barzilai–Borwein Gradient Method for Unconstrained Optimization Problems
2019 ◽
Vol 36
(04)
◽
pp. 1950017
◽
2013 ◽
Vol 333-335
◽
pp. 1256-1260