MODIFIED HYBRID GREY WOLF OPTIMIZER AND GENETIC ALGORITHM (HmGWOGA) FOR GLOBAL OPTIMIZATION OF POSITIVE FUNCTIONS
In this paper, a hybrid algorithm is proposed with combination of modified grey wolf optimizer (GWO) algorithm and genetic algorithm (GA). This algorithm called HmGWOGA minimizes positive functions. The main idea is to apply at each iteration the operators of GA to the population before applying the steps of GWO algorithm.
The genetic algorithm used is an adaptation of non-dominated sorting genetic algorithm-II (NSGA-II) proposed by Deb et al. [4] to solve multi-objective problems to the resolution of mono-objective problem.
First some benchmark test functions are used to compare the hybrid algorithm proposed with GWO algorithm in evolving best solution. Then the algorithm was used to identify parameters in three models of population dynamics. In most of the cases tested, the results show that the hybrid algorithm converges faster than the GWO algorithm. In addition, the results are more precise.
meta-heuristic, global optimization, grey wolf optimizer algorithm, genetic algorithm, inverse problem.