Yang Nan

• Southern University of Science and Technology

The use of an unbounded archive (UA) has attracted much attention in the filed of evolutionary multi-objective optimization (EMO) since a solution set selected from the UA is often better than the final population. The size of the UA is very large (e.g., 1,000,000) since it is unbounded and it stores all the examined non-dominated solutions during...

For a control problem with multiple conflicting objectives, there exists a set of Pareto-optimal policies called the Pareto set instead of a single optimal policy. When a multi-objective control problem is continuous and complex, traditional multi-objective reinforcement learning (MORL) algorithms search for many Pareto-optimal deep policies to app...

For a control problem with multiple conflicting objectives, there exists a set of Pareto-optimal policies called the Pareto set instead of a single optimal policy. When a multi-objective control problem is continuous and complex, traditional multi-objective reinforcement learning (MORL) algorithms search for many Pareto-optimal deep policies to app...

Hypervolume subset selection (HSS) has received significant attention since it has a strong connection with evolutionary multi-objective optimization (EMO), such as environment selection and post-processing to identify representative solutions for decision-makers. The goal of HSS is to find the optimal subset that maximizes the hypervolume indicato...

Recently, it was demonstrated that a decomposition-based multiobjective evolutionary algorithm with a pre-specified weight vector set cannot find a uniformly-distributed solution set over an inverted triangular Pareto front (PF). This is because the weight vectors are created by a simplex-lattice structure with a triangular shape. Much more boundar...

Degenerate multi-objective test problems are included in test suites to evaluate EMO algorithms on a wide variety of test problems. However, it was pointed out in some studies that the frequently-used degenerate DTLZ5, DTLZ6 and WFG3 test problems do not have degenerate Pareto fronts. Their Pareto fronts are different from the originally intended d...

Performance of evolutionary multi-objective optimization (EMO) algorithms is usually evaluated using artificial test problems such as DTLZ and WFG. Every year, new EMO algorithms with high performance on those test problems are proposed. One question is whether they also work well on real-world problems. In this paper, we try to find an answer to t...

Normalization is commonly used in multiobjective evolutionary algorithms (MOEAs) in order to handle multiobjective optimization problems with differently-scaled objectives. The goal of normalization is to obtain uniformly-distributed solutions over the entire Pareto front. However, in practice, such a uniform solution set may not be a well-distribu...

Recently, it has been demonstrated that a solution set that is better than the final population can be obtained by subset selection in some studies on evolutionary multi-objective optimization. The main challenge in this type of subset selection is how to efficiently handle a huge candidate solution set, especially when the hypervolume-based subset...

In the field of evolutionary multi-objective optimization (EMO), the standard practice is to present the final population of an EMO algorithm as the output. However, it has been shown that the final population often includes solutions which are dominated by other solutions generated and discarded in previous generations. Recently, a novel EMO frame...

Hypervolume subset selection (HSS) has received considerable attention in the field of evolutionary multi-objective optimization (EMO). It aims to select a representative subset from a candidate solution set so that the hypervolume of the selected subset is maximized. A number of HSS methods have been proposed in the literature, attempting to eithe...

An unbounded external archive has been used to store all nondominated solutions found by an evolutionary multi-objective optimization algorithm in some studies. It has been shown that a selected solution subset from the stored solutions is often better than the final population. However, the use of the unbounded archive is not always realistic. Whe...

An unbounded external archive has been used to store all nondominated solutions found by an evolutionary multi-objective optimization algorithm in some studies. It has been shown that a selected solution subset from the stored solutions is often better than the final population. However, the use of the unbounded archive is not always realistic. Whe...

Recently, an R2-based hypervolume contribution approximation (i.e., R2HVC indicator) has been proposed and applied to evolutionary multi-objective algorithms and subset selection. The R2HVC indicator approximates the hypervolume contribution using a set of line segments determined by a direction vector set. Although the R2HVC indicator is computati...

Real-world multiobjective optimization problems (MOPs) usually have conflicting and differently-scaled objectives. To deal with such problems, objective space normalization is widely used in multiobjective evolutionary algorithm (MOEA) design, especially, in the design of decomposition-based MOEAs. It has been demonstrated that uniformly-distribute...

In the evolutionary multi-objective optimization (EMO) field, the standard practice is to present the final population of an EMO algorithm as the output. However, it has been shown that the final population often includes solutions which are dominated by other solutions generated and discarded in previous generations. Recently, a new EMO framework...

Supplementary material for the paper "A Survey of Normalization Methods in Multiobjective Evolutionary Algorithms".

Hypervolume is widely used in the evolutionary multiobjective optimization (EMO) field to evaluate the quality of a solution set. For a solution set with $\mu $ solutions on a Pareto front, a larger hypervolume means a better solution set. Investigating the distribution of the solution set with the largest hypervolume is an important topic in EMO...

A real-world multiobjective optimization problem (MOP) usually has differently scaled objectives. Objective space normalization has been widely used in multiobjective optimization evolutionary algorithms (MOEAs). Without objective space normalization, most of the MOEAs may fail to obtain uniformly distributed and well-converged solutions on MOPs wi...

Hypervolume is widely used in the evolutionary multi-objective optimization (EMO) field to evaluate the quality of a solution set. For a solution set with $\mu$ solutions on a Pareto front, a larger hypervolume means a better solution set. Investigating the distribution of the solution set with the largest hypervolume is an important topic in EMO,...

In the field of evolutionary multi-objective optimization (EMO), most EMO algorithms try to find a set of non-dominated solutions to approximate the Pareto front of a multi-objective optimization problem. In these algorithms, a population is evolved from one generation to another, and the population of the last generation is presented as the final...

The hypervolume contribution is an important concept in hypervolume-based evolutionary multi-objective optimization algorithms. It describes the loss of the hypervolume when a solution is removed from the current population. Since its calculation is #P-hard in the number of objectives, its approximation is necessary for many-objective optimization...

In this brief, a low-voltage low-temperature- coefficient (TC) subthreshold CMOS voltage reference (CVR) is presented. The proposed CVR employs the ΔV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GS</sub> of different-threshold and same-threshold nMOS transistor pairs to generate the complementary-t...