Hybrid non-dominated sorting genetic algorithm with adaptive operators selection

Abstract

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.

Multi‐objective robust fuzzy fractional order proportional–integral–derivative controller design for nonlinear hydraulic turbine governing system using evolutionary computation techniques

“…NSGA-II is a multi-objective evolutionary computation technique, where fitness is evaluated using non-dominated sorting technique (Khan Mashwani et al, 2017). The pareto fronts are generated such that the individuals, which are not dominated by any other individuals, are assigned in the first front (Wang et al, 2017).…”

“…The non-dominated sorting genetic algorithm-II (NSGA-II), which is one among the multi-objective evolutionary computation techniques, performs non-dominated sorting to assign the fitness values among conflicting objectives (Kalaivani, Subburaj, & Willjuice Iruthayarajan, 2013;Khan Mashwani, Salhi, Yeniay, Hussian, & Jan, 2017;Wang, He, Xue, & Du, 2017). Modified NSGA-II (MNSGA-II) is the modified version of NSGA-II, which incorporates the additional strategies like dynamic crowding distance (DCD) and controlled elitism (CE) to attain uniform horizontal and lateral diversities (Ji, Yuan, & Yuan, 2017;Li, Chen, Zhang, Wang, & Ba, 2016;Piraisoodi, Iruthayarajan, & Kadhar, 2017;Ramesh, Kannan, & Baskar, 2012).…”

An Improved Adaptive Genetic Algorithm for Solving 3-SAT Problems Based on Effective Restart and Greedy Strategy

“…So M. Srinivas and L. M. Patnaik proposed an adaptive genetic algorithm(AGA) 43 , when the individual fitness is high, the crossover probability and mutation probability is reduced; when the fitness of the individual is low, the crossover probability and the mutation probability increase, that is to say, in each iteration, the crossover probability and the mutation probability are adaptively set according to the individual fitness value, which makes the adaptive genetic algorithm more efficient and global optimal. At present, many scholars have realized the advantages of dynamic adaptive technology and applied it in many fields [44][45][46][47] . However, as an optimization, adaptive genetic algorithm has some limitations, among which the biggest deficiency is premature convergence.…”

Hybrid adaptive evolutionary algorithm based on decomposition

“…This variant was then applied to five standard ZDT test problems [79] as well as the CEC'09 test instances [74]. In [39,37,43,45], MOEA/D [72] and NSGA-II [13], two different MOEA approaches have been used synergetically at population and generation levels. These two algorithms have also been used in [48] to solve hard multiobjective optimization problems.…”