This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

Selecting team players is a crucial and challenging task demanding a considerable amount of thinking and hard work by the selectors. The present study formulated the selection of an IPL team as a multiobjective optimization problem with the objectives of maximizing the batting and bowling performance of the squad, in which a player's performance is estimated using an efficient Batting Performance Factor and Combined Bowling Rate. Also, the proposed model tries to formulate a balanced squad by constraining the number of pure batters, pure bowlers and all-rounders. Bounds are considered on star players to enhance the performance of the squad and also from the income prospects of IPL. The problem in itself is treated as a 0/1 knapsack problem for which two combinatorial algorithms, namely, BNSGA-II and INSGA-I, are developed. These algorithms were compared with existing modified NSGA-II for IPL team selection and three other popular multi-objective optimization algorithms, NSGA-II, NSDE and MOPSO-CD on the basis of standard performance metrics: hypervolume and inverted generational distance. Both algorithms performed well, with BNSGA-II performing better than all the other algorithms considered in this study. The IPL 2020 players' data validated the applicability of the proposed model and algorithms. The trade-off squads have appropriate proportions of players of each expertise. Further analysis of the trade-off squads demonstrated that many theoretically selected players performed well in IPL 2020 matches.

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