A Multiobjective Incremental Control Allocation Strategy for Tailless Aircraft

Table 1

An overview of MOEAs.

Basic ideas	(1) Using the Pareto fitness allocation strategy to find all the nondominated individuals from the current population
	(2) Using the performance evaluation indicator function to guide the search process and the solution selection process
	(3) Decomposing the complex multiobjective problem into several single objective subproblems or multiple simple multiobjective problems
Application scenarios	(1) Multiobjective optimization problems with 2 or 3 objectives
	(2) Multiobjective optimization problems with irregular Pareto front
	(3) Many-objective optimization problems (MaOPs)
Typical algorithms	(1) NSGA-II (nondominated sorting genetic algorithm-II); SPEA2 (strength Pareto evolutionary Algorithm2); PESA-II (Pareto envelop-based selection algorithm-II)
	(2) IBEA (indicator-based evolutionary algorithm); HypE (Hypervolume based evolutionary algorithm); SMS-EMOA (S-metric selection based evolutionary multiobjective algorithm)
	(3) MOEA/D (multiobjective evolutionary algorithm based on decomposition); REVA (reference vector guided evolutionary algorithm); NSGA-III (nondominated sorting genetic algorithm-III)