In today world, there exists different algorithmic approaches in Computer Science to solve the complex real-world optimization problems around us. Based on the number of objectives they are classified as single-objective, multi-objective or many-objective optimization problems. Though many approaches are available for solving single-objective optimization problems, they are not directly scalable to solve Multi-objective and Many-objective optimization problems. However, the Evolutionary Computing (EC) based approaches are becoming most popular and common to solve all the types of optimization problems, due to their simplicity and wide applicability. This paper presents the details of the Evolutionary Algorithms (EAs) to solve the optimization problem with the category of EAs for Multi-objective and EAs for Many-Objective optimization problems. Insight about the Hybridization of EAs is also added. The details about the algorithmic components required for designing EAs is also presented along with the Benchmarking Problems and Performance Metrics available to validate the newly designed EAs.
S. S. Shinde, Dr. Thangavelu S., and Dr. Jeyakumar G., “Evolutionary Computing Approaches for Solving Multi-Objective and Many-Objective Optimization Problems: A Review”, in 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), 2019.