MOPs (multi-objective optimization problems) are commonly found in real-world applications. MOEAs (multi-objective evolutionary algorithms) are useful for solving MOPs with few objectives. However, in recent years, MOEAs have reported difficulties in solving MOPs with four or more objectives. These are referred to as Many-objective Optimization Problems (MaOPs). The inability of dominance-based MOEAs to converge to the Pareto front with good diversity, high computational complexity in the computation of performance indicators, and difficulties in decision making, visualisation, and understanding the relationships between objectives and articulated preferences are all challenges faced by population-based algorithms when solving MaOPs. Many objective evolutionary algorithms (MaOEAs) have been developed and tested on standard benchmark problems to address these issues.

The objective of this Special Issue is to evaluate MOEAs as well as the recently developed MaOEAs on newly designed challenging MaOPs. Original research and review articles are welcome

Potential topics include but are not limited to the following:

• Many-objective optimization for performance indicators
• Many-objective optimization for objective reduction
• Many-objective optimization for visualization techniques
• Many-objective optimization for preference Articulation
• Many-objective optimization for decision-making methods
• Many-objective optimization for hybridized algorithms
• Many-objective optimization for development of further challenging benchmark problems
• Many-objective real-world optimization problems
• Many-objective optimization for model learning
• Many-objective optimization for estimating knee, nadir points and constraint handling methods
• Many-objective optimization with objectives' constraints
• Many-objective optimization algorithms' robustness improvement
• Many-objective optimization computing efficiency improvements