Hybrid non-dominated sorting genetic algorithm with adaptive operators selection - Khan Mashwani, Wali; Salhi, Abdellah; Yeniay, Ozgur; Hussian, H.; Jan, M.A. (Applied soft computing, 2017)
The main goal of this paper is to investigate the effect of the multiple search operators with adaptive selection strategy and to develop hybrid version of non-dominated sorting genetic algorithm (HNSGA) for solving recently developed complicated multi-objective optimization test suit for multi-objective evolutionary algorithms (MOEAs) competition in the special session of the congress on evolutionary computing held at Norway in 2009 (CEC’09). The Inverted generational distance (IGD) has been used performance indicator to establish valuable comparison between the suggested algorithm and NSGA-II as shown in the figure below. A set of Pareto optimal solutions with smaller is the IGD values confirm that approximated Pareto front (PF) will cover whole part of true PF in term of proximity and diversity. The average IGD-metric values evolution obtained by HNSGA versus NSGA-II for UF1-UF5 within allowable resources of 300,000 function evaluations. [Display omitted] •A novel hybrid non-dominated sorting genetic algorithm (HNSGA) for multiobjective optimization with continuous variables is developed.•HNSGA includes adaptive operator selection to allocate resources to multiple search operators based on their individual performance at the subpopulation level.•HNSGA is tested in classical benchmark problems taken from the ZDT and CEC’09 suites.•Inverted generational distance (IGD), relative hypervolume (RHV), Gamma and Delta functions are used as performance indicators.•The new algorithm is very competitive with other state-of-the-art optimizers such as AMALGAM, NSGA-II, MOEA/D, Hybrid AMGA, OMOEA, PA-DDS, etc. Multiobjective optimization entails minimizing or maximizing multiple objective functions subject to a set of constraints. Many real world applications can be formulated as multi-objective optimization problems (MOPs), which often involve multiple conflicting objectives to be optimized simultaneously. Recently, a number of multi-objective evolutionary algorithms (MOEAs) were developed suggested for these MOPs as they do not require problem specific information. They find a set of non-dominated solutions in a single run. The evolutionary process on which they are based, typically relies on a single genetic operator. Here, we suggest an algorithm which uses a basket of search operators. This is because it is never easy to choose the most suitable operator for a given problem. The novel hybrid non-dominated sorting genetic algorithm (HNSGA) introduced here in this paper and tested on the ZDT (Zitzler-Deb-Thiele) and CEC’09 (2009 IEEE Conference on Evolutionary Computations) benchmark problems specifically formulated for MOEAs. Numerical results prove that the proposed algorithm is competitive with state-of-the-art MOEAs.