Local Search-based Enhanced Multi-objective Genetic Algorithm and Its Application to the Gestational Diabetes Diagnosis
Jenn-Long Liu, Chung-Chih Li, Chien‐Liang Chen · Journal of Advances in Information Technology · 2015
In evolutionary computation, several multiobjective genetic algorithms (MOGAs) have been widely used to solve multi-objective optimization problems (MOOPs). The version NSGA-II, developed by Deb et al., is a useful package using a population-based genetic algorithm to solve optimization problems with multiple objectives subject to constraints. This study proposes an enhanced version of NSGA-II, termed LS-EMOGA herein, which modifies the crossover and mutation operators of original NSGA-II by an extended intermediate crossover and a nonuniform mutation and also incorporates a local search (LS) procedure to improve the fine-turning ability of the solution searching. The performance of the proposed LS-EMOGA is assessed by evaluating five benchmark cases of MOOPs. The computed solutions are compared with those of obtained using NSGA-II and proposed MOGA without local search procedure (EMOGA version). Moreover, the proposed LS-EMOGA combines a k-means clustering algorithm to apply to the case diagnosis of gestational diabetic disease.