Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A favorable weight-based evolutionary algorithm for multiple criteria problems
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Properties of an adaptive archiving algorithm for storing nondominated vectors
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A faster algorithm for calculating hypervolume
IEEE Transactions on Evolutionary Computation
Diversity Management in Evolutionary Many-Objective Optimization
IEEE Transactions on Evolutionary Computation
A Fast Way of Calculating Exact Hypervolumes
IEEE Transactions on Evolutionary Computation
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Multiobjective Optimization (MOO) refers to optimization problems that involve two or more objectives. Unlike in the single objective optimization, a set of solutions representing the tradeoff among the different objects rather than an unique optimal solution is sought in MOO. How to measure the goodness of solutions and the performance of algorithms is important in MOO. In this paper, we first review the performance metrics of multiobjective optimization and then classify variants of performance metrics into three categories: set based metrics, reference point based metrics, and the true Pareto front/set based metrics. The properties and drawbacks of different metrics are discussed and analyzed. From the analysis of different metrics, an algorithm's properties can be revealed and more effective algorithms can be designed to solve MOO problems.