A many-objective evolutionary algorithm based on novel fitness estimation and grouping layering

TL;DR: This study proposes a multi-objective optimization method using Kriging surrogate model and NSGA-III to improve Mg alloy ureteral stent degradation performance, achieving 5.52× degradation uniformity, 10× degradation time, and 4× work time with <6% error.

TL;DR: MaOALO is a novel MaO algorithm that enhances convergence and diversity using reference point, niche preserve and IFM mechanisms. It outperforms other MaO algorithms on various real-world and standard benchmark problems.

TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.

TL;DR: A reference-point-based many-objective evolutionary algorithm that emphasizes population members that are nondominated, yet close to a set of supplied reference points is suggested that is found to produce satisfactory results on all problems considered in this paper.

TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.

TL;DR: A real-coded crossover operator is developed whose search power is similar to that of the single-point crossover used in binary-coded GAs, and SBX is found to be particularly useful in problems having mult ip le optimal solutions with a narrow global basin where the lower and upper bo unds of the global optimum are not known a priori.