Table 2 Literature review summary.

⁹

TSEA

1.Two stage evolutionary algorithm

2. Adaptive switching based on noise level

3. output solution selection to choose fitter non-dominated solutions

DTLZ and WFG problems

Modified inverted generational distance and Hypervolume

1.Optimiztion performance is good for unimodal and multi-modal problems

2. not effective in solving WFG problems higher level of noise

²⁸

MS-MOEA

1.Adaptive model switch-based surrogate assisted evolutionary algorithm SAEA (MSMOEA)

2. IBEA \(\in\)+ is the basic optimizer

3. Switches adaptively between

Radial basis function network and gaussian regression for noise reduction

DTLZ 1 to DTLZ7 test problems (Two and three objectives)

Inverted Generational distance

1.Best results for 12 problems

2.additive noise is studied, multiplicative noise experimentation not done

³⁰

GMRM-NSGA-II

1. Gaussian model and regularity model based on fast elitist non-dominated sorting genetic algorithm

2. population split into subpopulation, and extends different model and selection techniques on each population

ZDT, DTLZ and WFG

Inverted Generational Distance

1.Performs better in handling problems with varied properties like modality, inseparable, etc

2. increased time consumption is a limitation

²⁷

FNSGA-II

1. Fliters based NSGA-II

2. mean and weiner filters are applied for denoising

3. To handle noise in image, signals

ZDT, DTLZ and WFG

Inverted Generational distance

1. Acheieved good results for most of the problems

2. Performance limitations observed for problems, where True front is discontinuous

³⁹

RTEA

1. Rolling tide evolutionary algorithm

2. elite archive with regular resampling of solutions in it

3. refinement phase during later stages of evolution to improve accuracy

CEC2009 test problems

Noise misinformation measure, Inverse generational distance, Hypervolume

1.Optimization performance is better for most problems compared to other algorithms

2. Limitations is, adaptation of resampling according to convergence stage will be beneficial

²⁵

RM-NSGA-II

1. Regularity model in NSGA-II

2. regularity model is included like a reproduction operator

3. Extra denoising to choose appropriate sample points

ZDT, DTLZ and WFG

Generational Distance, Minimal spacing, Inverted Generational Distance

1.Improved performance of NSGA-II based algorithm in handling noisy optimization problem

2.Limitation observed in solving multimodal problem