Research Article | Open Access
An Indicator and Decomposition Based Steady-State Evolutionary Algorithm for Many-Objective Optimization
An indicator based selection method is a major ingredient in the formulation of indicator based evolutionary multiobjective optimization algorithms. The existing classical indicator based selection methodologies have demonstrated an excellent performance to solve low-dimensional optimization problems. However, the indicator based evolutionary multiobjective optimization algorithms encounter enormous challenges in high-dimensional objective space. Our main purpose is to explore how to extend the indicator to handle many-objective optimization problems. After analyzing the indicator, the objective space partition strategy, and the decomposition method, we propose a steady-state evolutionary algorithm based on the indicator and the decomposition method, named, -MOEA/D, to obtain well-converged and well-distributed Pareto front. The main contribution of this paper contains two aspects. (1) The convergence and diversity for the indicator based selection are analyzed. Two improper selection situations will be properly solved via applying the decomposition method. (2) According to the position of a new individual in the steady-state evolutionary algorithm, two different objective space partition strategies and the corresponding selection methods are proposed. Extensive experiments are conducted on a variety of benchmark test problems, and the experimental results demonstrate that the proposed algorithm has competitive performance in comparison with several tailored algorithms for many-objective optimization.
A large number of evolutionary multiobjective optimization algorithms (EMOAs) have been introduced to solve multiobjective optimization problems (MOPs) [1–5]. Most of these algorithms have demonstrated their excellent performance to deal with MOPs involving two or three objectives. However, most of optimization problems, such as water resource management problem , general aviation aircraft design problem , and hybrid electric vehicle controller design problem , often relate to many-objective optimization problems  (MaOPs). The traditional EMOAs face substantial difficulties when addressing MaOPs. There is enormous amount of researches discussing most existing methodologies to solve them. It has been one of the major research aspects as the detailed survey of MaOPs .
Convergence is to find a set of solutions as close as possible to the Pareto front while diversity is to obtain well-distributed solutions. Balancing convergence and diversity is a key issue for solving MaOPs. In order to obtain the better performance, several EMOAs have been presented in the literature which can be divided into four categories.
The idea of the first category is based on the Pareto dominance relation. In the dominance based methods, the Pareto dominance can be defined as the preference of decision maker. The popular EMOAs, such as nondominated sorting genetic algorithm II (NSGA-II)  and multiple particle swarm optimizer (MOPSO) , are based on the Pareto dominance. These algorithms can deal with MOPs. However, the selection pressure of Pareto dominance is severe loss with the dimension of the objective space increasing. Therefore, there is a large amount of studies to modify the dominance relations or adopt the secondary metric. Fuzzy dominance [13, 14] and preference inspired method  have been deployed for handling MaOPs. However, the final obtained Pareto solutions may present an excellent convergence in objective space but with the poor diversity.
The second category is the decomposition based selection method. The method is to decompose an MOP into many single-objective optimization subproblems through a scalarizing function. It aims to optimize these subproblems in a collaborative manner by evolving the population. C-MOGA  and MOEA/D  are the most representative of this sort. During the past few years, as a major framework to design EMOAs, the decomposition based selection method has spawned a large number of research works, for example, incorporating self-adaptation mechanisms in reproduction  and hybridizing with the swarm intelligence [19, 20].
The third avenue is known as the indicator based approaches, which merge the convergence and diversity into an indicator. The indicator based evolutionary algorithms employ a single performance indicator to provide a desired ordering among individuals that represent Pareto front approximations. The hypervolume  is probably the most popular indicator ever adopted due to its satisfactory theoretical properties. Some hypervolume based EMOAs have been established, such as indicator-based evolutionary algorithm (IBEA)  and multiobjective covariance matrix adaptation evolution strategy (MO-CMA-ES) . However, the computational cost of hypervolume hinders these algorithms for MaOPs .
Recently, several new performance indicators called [25, 26], , and [28–31] have been proposed. More specifically, is composed of slight modifications of and performance indicators via introducing the averaged Hausdorff distance. Its computation cost is lower than that of the hypervolume, and it can handle outliers as well, which makes it attractive for assessing performance of EMOAs . Meanwhile, it has been shown in [29, 33] that prefers evenly spread solutions along the Pareto front for biobjective and three-objective optimization problems, respectively. However, the main limitation of the indicator is how to produce the well-converged and well-distributed candidate solutions in high-dimensional objective spaces. Nowadays, a new performance indicator called the indicator is proposed in  to compare approximation sets based on a set of utility functions . The indicator is weakly monotonic and performs a lower computational overhead than the hypervolume. Due to these characteristics, the indicator is recommended for dealing with MaOPs . MOMBI  and MOMBI-II  have been proposed in the context of MaOPs.
It is worth noting that, unlike the three above-mentioned categories, the objective space partition strategy can be regarded as the fourth category to handle MaOPs. The representative EMOAs are MOEA/D-M2M  and MOEA/DD . The motivation of this category is to balance convergence and diversity in each subspace. In comparison to the traditional decomposition based methods , these EMOAs can achieve a better performance via introducing the sophisticated selection methods.
In other words, the generational and steady-state schemes are two commonly used reproduction operators to produce the offspring population. Most of generational EMOAs have been deeply studied other than the steady-state EMOAs, especially in -EMOAs. There is still huge room for improvement especially for the steady-state evolutionary algorithm to resolve MaOPs. Meanwhile, MOMBI-II  and MOEA/DD  are suitable for different problems. This phenomenon motivates us on the merits of both selection strategies for further research to balance convergence and diversity. This paper mainly focuses on the indicator based steady-state evolutionary algorithm which adopts the modified Tchebycheff and penalty-based boundary intersection (PBI) decomposition methods to balance convergence and diversity for MaOPs. The main contributions of this paper are as follows.
(1) It is the first time to combine the indicator and the decomposition method into a steady-state evolutionary algorithm to address MaOPs. To be specific, we find that the indicator based selection method is not always proper for the steady-state evolutionary algorithm. It is harmful for the population diversity when purely applying the indicator to handle MaOPs. Therefore, we employ the objective space partition strategy and the PBI decomposition method to achieve a balance between convergence and diversity.
(2) The selection procedure of the steady-state evolutionary algorithm is to delete a candidate solution from the combined candidate solutions which contain the parent population and an offspring individual. Two types of the selection approaches will be introduced to delete a candidate solution in terms of the ideal point. On the one hand, we first repartition the objective space into a number of subspaces if the position of the ideal point changes. Then, we adopt the indicator and the decomposition method to prune the combined candidate solutions. On the other hand, we cluster the offspring candidate solution to the nearest reference vector if the ideal point remains unchanged. Then, the decomposition method is adopted to discard a solution which has the worst performance in the most crowded subspaces.
The rest of this paper is organized as follows. Section 2 provides the background and motivation of this paper. Section 3 is devoted to the description of our proposed -MOEA/D for MaOPs. Comprehensively experimental design and experimental results are provided in Sections 4 and 5. Finally, Section 6 concludes this paper.
2. Background and Motivation
In this section, some basic definitions and related works about -MOEA/D are firstly introduced. Then, the motivation of -MOEA/D will be illustrated in detail.
2.1. Basic Definitions
Without loss of generality, a minimization multiobjective optimization problem can be formulated as follows:where is the vector of decision variables, is the dimension of solution space, is the th objective, is the number of objectives, and is the vector of dimensional objectives. Given two vectors and in the objective space, we say that dominates if , for all , and . Pareto set (PS) corresponding to all of nondominated solutions in decision space and Pareto front (PF) is in objective space.
Definition 1. For a set of reference vectors and the modified Tchebycheff utility function, the indicator of a solution set can be defined as follows:
Definition 2. The contribution of a solution to the indicator can be of interest to assess the performance of each individual, which is defined as follows:where is called a set of reference vectors, is an ideal point, and is the individual of population .
In this paper, we adopt the penalty-based boundary intersection (PBI) method, due to its promising performance for many-objective optimization reported in MOEA/DD . Without loss of generality, the PBI decomposition method can be given as follows:where is the convergence distance metric, denotes the diversity distance metric, and is an important parameter to balance convergence and diversity.
2.2. Related Works
In this subsection, we would illustrate the representative indicator and decomposition based selection methods which are based on a set of reference vectors.
(i) MOMBI  and MOMBI-II  are two representative -EMOAs which abandon the Pareto dominance concept for MaOPs. The quality of a solution is fully determined by a set of reference vectors. The achievement scalarizing function is first introduced into the -EMOAs. MOMBI-II has adopted statistical information of previous generations to normalize the candidate solutions. However, the fast ranking strategy scarcely considers the relationship between the adjacent ranks. The convergence is well while the diversity will be destroyed.
(ii) DBEA  is an excellent and practical steady-state EMOA for MaOPs which not only considers the benchmark test problems but also takes into account three constrained engineering design optimization problems. The innovation of DBEA contains two parts: one is a simple selection procedure without introducing any penalty parameter, where the diversity distance metric has a precedence over the convergence distance metric, and the other is the normalization method, which is based on the corner sort method.
(iii) MOEA/D-M2M  is the first paper which introduces a set of weight vectors to divide the objective space into a large number of subspaces. Different weight vectors are used to specify different subpopulations for approximating a small segment of the whole Pareto front. The algorithm shows a better performance to deal with biobjective or three-objective optimization problems.
(iv) RVEA  can be seen as a generational EA to produce offspring and the steady-state selection strategy based on a set of reference vectors. The adaptive angle-penalized distance (APD) metric and a reference vector updated strategy are firstly introduced to balance convergence and diversity and to deal with scaled MaOPs. Due to the excellent adaptive reference vector updated strategy, we have embedded this strategy into the proposed -MOEA/D algorithm.
(v) MOEA/DD  is on the merit of Pareto dominance and decomposition method to balance convergence and diversity. The main contribution contains three parts. Firstly, the mating restriction strategy considers neighboring subspace and the whole population. Secondly, the efficient nondominated level update approach is proposed for steady-state EMOAs. Finally, the density of the population is calculated by the local niche count of a subspace. MOEA/DD  is currently an excellent steady-sate EA for MaOPs.
Achieving a balance between convergence and diversity is the cornerstone of the indicator and decomposition based evolutionary algorithms. The motivation of this paper is on the merit of the indicator and decomposition method to balance convergence and diversity for the steady-state evolutionary algorithm to address MaOPs. Two main aspects should be taken into account.
(1) As suggested in , the indicator naturally balances convergence and diversity during the evolutionary procedure. However, the selection pressure for solving MaOPs hardly quantitatively balances convergence and diversity simply depending on the indicator. Meanwhile, the indicator gives an excessive priority to the convergence requirement while the objective space partition and decomposition methods pay more attention to diversity other than convergence. Therefore, how to reasonably balance convergence and diversity on the benefit of the indicator and the decomposition method is vital for solving MaOPs.
(2) As for the steady-state EAs, only an individual is created and a candidate solution will be deleted from the combined candidate solutions in each generation. The key issue of the steady-state EA is to determine which candidate solution will be substituted by an offspring. Considering the position information between the offspring and the ideal point, the objective space partition strategy should be illustrated in detail.
First and foremost, the indicator  which can be seen as the mutual preference between a set of reference vectors and candidate solutions is a vital ingredient in EMOAs for MaOPs. Most of researchers [32, 36, 40–42] believe that the indicator obtains a desirable performance which combines convergence and diversity. Let us consider an example as shown in Figure 1(a), where a candidate solution needs to be deleted from the combined candidate solutions. The indicator is adopted to prune the combined population and will be deleted from the population. It is a proper situation for the indicator based selection method. However, we find that purely adopting the indicator is not always reasonable as shown in Figure 1(b). In this case, although the indicator based selection mechanism intends to achieve a balance between convergence and diversity, the selected candidate solutions fail to keep the population diversity especially in high-dimensional objective space. The main reason is that the indicator based selection method will discard which is located in the isolated subspace. To relieve this effect, whose position is located in the most crowded subspace will be discarded while will survive. Based on the above description and discussion, it is interesting to note that combining the indicator with decomposition method is a potential improvement strategy to balance convergence and diversity for MaOPs.
(a) Proper situation
(b) Improper situation
The following paragraph will illustrate the objective space partition strategy to avoid the redundant cluster operation as given in Figure 2. When an offspring is produced by commonly used genetic operators, the objective space partition strategy includes two situations: one is clustering the offspring to a set of reference vectors, the other is clustering the combined population to a set of reference vectors. To provide a clear explanation of two situations, we give two examples for biobjective optimization problems as illustrated in Figures 2(a) and 2(b). If the ideal point remains unchanged as shown in Figure 2(a), is located in the subspace without clustering all of the candidate solutions. However, if the ideal point is updated when is located in the second, third, or fourth quadrant as shown in Figure 2(b), the population distribution is different from the original. The whole combined population should cluster each candidate solution according to a set of reference vectors.
(a) Cluster an offspring
(b) Cluster the combined candidate solutions
3.1. Framework of -MOEA/D
The framework of the proposed -MOEA/D is listed in Algorithm 1. First, a set of reference vectors and initial population are generated. Only an offspring is generated by introducing a traditional reproduction operator until the termination condition is not met. Then, the offspring is combined with the current parent population as a combined population. The indicator and decomposition based selection strategy are employed to prune the combined population. In the following subsections, the implementation details of each component in -MOEA/D will be illustrated step by step.
3.2. Initialization Strategy
To ensure the diversity in the obtained PF, a set of reference vectors is generated by using two-layer reference vector generation method  in high-dimensional objective space to enhance the diversity of population. Most of EMOAs adopt the same method to solve MaOPs, such as DBEA  and MOEA/DD . The initial population consists of individuals which are randomly generated within the variable bounds.
Each reference vector partitions the objective space into a set of subspaces . The initialized population will be allocated to these subspaces by associating each individual with its closest reference vector. Each individual is allocated to subspace if and only if the angle between the individual and the reference vector is minimal.
3.3. -MOEA/D Selection Strategy
As for the steady-state evolutionary algorithm, a new offspring is generated via using simulated binary crossover operator (SBX)  and polynomial mutation operator (PM) . After the generation of a new individual in a steady-state form, we use the individual to update the parent . The pseudocode of -MOEA/D selection procedure is presented in Algorithm 2. Visualization in evolutionary multiobjective optimization is essential in many aspects . To better understand the -MOEA/D selection strategy, the biobjective illustrations are given in Figures 3 and 4. When the number of objectives is greater than three, such a simple and intuitive visualization of approximation sets is much harder to achieve. The main concern is developing such a steady-state selection mechanism based on the indicator and the decomposition method to balance convergence and diversity. The -MOEA/D selection strategy will be repeated multiple rounds during the iteration.
(a) Proper situation
(b) Improper situation
(a) Proper situation
(b) Improper situation
First of all, we should detect whether the ideal point is changed or not after introducing a new offspring. If the ideal point is updated via introducing the new individual as given in Figure 2(b), we need to repartition the objective space and cluster the combined candidate solutions to each corresponding subspace. The instantiation of objective space partition strategy for the combined population is given in Algorithm 3 (Step of Algorithm 2). The angle between the objective vector and the reference vector will be calculated in Steps and of Algorithm 3. If the ideal point remains unchanged as illustrated in Figure 2(a), the traditional objective space partition strategy clusters the combined population and obtains the subspace where each candidate solution belongs to. It wastes a lot of precious time for the steady-state EAs. We only need to cluster the offspring to the corresponding reference vector after comprehensively analyzing the position of the new individual. We cluster the new individual to the nearest reference vector in Step of Algorithm 2 to avoid the wasted computation.
The indicator can obtain weakly dominated solutions compared with the Pareto dominance strategy. However, there is no need to introduce any sophisticated and complicated ranking strategy or the diversity maintaining strategy to distinguish the population in -EMOAs. The contribution is calculated via adopting Algorithm 4. We obtain the value of corresponding to each reference vector from Step to Step in Algorithm 4. We preserve the contribution individuals and their value in Step . Then, we take into account each individual to a set of reference vectors and add all the value together as their contribution in Step in Algorithm 4.
In order to clearly explain the -MOEA/D selection procedure which is based on indicator and decomposition based method, Algorithm 2 contains two aspects while deleting a solution from the combined population. If the ideal point is changed via introducing the new individual, , the indicator and the decomposition method should be combined to delete one solution. If the ideal point remains unchanged, the selection procedure only considers the most crowded subspaces among all subspaces. The worst solution is discarded from the combined population after applying the -MOEA/D selection strategy; the number of subspaces that the discarded solution occupied will be minus one. Then, the -MOEA/D selection procedure will be explained in detail.
(1) If the ideal point is updated by the offspring (Step of Algorithm 2), the combined candidate solutions should be repartitioned because the distribution of the original population has already changed in Step and Step in Algorithm 2. When we introduce the indicator to prune the combined population in Step in Algorithm 2, almost few researchers [32, 36, 40, 41] take into account the distribution of the candidate solutions in -EMOAs. It is doubted whether deleting the lowest contribution is reasonable. As further observed from Figures 3 and 4, only adopting the indicator to prune the combined population is not always suitable. The size of the lowest contribution individual contains two situations.(1)The lowest contribution candidate solution has an individual. Most of -EMOAs, such as MOMBI-II  and -MOPSO , will discard the individual. However, it is not always proper for most of test problems after comprehensively balancing convergence and diversity. Then, the proper and improper situations will be analyzed in detail.(a)If there are another solutions in the subspace , the lowest contribution individual does not have any contribution to enhance the algorithm performance from the nature of indicator. The solution will be deleted (from Step to Step in Algorithm 2). Figure 3(a) presents an example of this selection procedure.(b)If the subspace only contains a candidate solution, that is to say, the candidate solution is located in an isolated subspace, the isolated lowest contribution individual should be preserved to maintain the better diversity at the expense of the convergence. As shown in Figure 3(b), although belongs to the lowest contribution level, it is associated with an isolated subspace . It indicates that is important for the population diversity and it should be preserved. Instead, we find the most crowded subspaces and delete the solution which has the maximum PBI value (Step (11) of Algorithm 2). In Figure 3(b), in subspace will be deleted.(2)The lowest contribution level contains more than one individual. The distribution of the population contains two situations.(a)As shown in Figure 4(a), the subspace of the lowest contribution possesses more than one individual. Then, we find the most crowded subspaces in and delete the solution with maximum PBI value solution (from Step (13) to Step (15) in Algorithm 2). In this situation, it is proper to introduce the indicator based selection method without considering the population distribution to prune the candidate solutions.(b)As shown in Figure 4(b), solution in each subspace only contains one individual. The candidate solution is vital for the population diversity. The most crowded subspaces comprehensively consider all of the combined population. There is at least one subspace which contains more than one individual. We eliminate the worst individual which has the maximum PBI value in the most crowded subspaces (from Step to Step in Algorithm 2). It is improper to delete the star individual in an isolated subspace even if it has the lowest contribution. Discarding the cross individual in the first rank can ensure the better diversity.
(2) If remains unchanged via introducing a new individual , only the acute angle between the candidate solution and a set of reference vectors should be calculated in Step of Algorithm 2. The PBI decomposition method is introduced to evaluate the combined candidate solutions . The selection method is to delete which has the maximum PBI value in the most crowded subspaces in Step of Algorithm 2. The size of the next parent population remains unchanged.
After deleting an individual from the combined population , the subspace number of the discarded solution will be minus one. The selected population is the obtained solutions through adopting the -MOEA/D selection strategy. From Step to Step in Algorithm 2, the adaptive reference vector updated strategy from RVEA  is embedded into our -MOEA/D selection strategy. Interested researchers can refer to RVEA  for further reading.
(1) Similarities between -MOEA/D and MOMBI-II . (i) Both of them adopt the indicator to select the candidate solutions.
(ii) Both of them introduce a set of reference vectors to guide the potential solutions.
(iii) Both of them use utility function to evaluate the population.
(2) Similarities between -MOEA/D and MOEA/D-PBI . (i) Both of them adopt a set of reference vectors to guide the selection procedure.
(ii) Both of them apply the PBI decomposition method to select the candidate solutions.
(3) Similarities between -MOEA/D and DBEA . (i) Both of them use a set of reference vectors to maintain the diversity of the population.
(ii) Both of them are the steady-state evolutionary algorithms.
(iii) Both of them are on the merit of nadir points.
(4) Similarities between -MOEA/D and MOEA/DD . (i) Both of them use a set of reference vectors (weight vectors) to partition the objective space into a number of subspaces.
(ii) Both of them use the convergence distance and the diversity distance from the PBI decomposition method  to enhance the performance.
(iii) Both of them are the steady-state evolutionary algorithms.
(5) Difference among the above EMOAs. The selection procedure plays an important role for MaOPs compared with the single objective optimization problems. Generational EMOAs and steady-state EMOAs are two aspects to solve MaOPs. The proposed algorithm has some differences among MOMBI-II , RVEA , DBEA , and MOEA/DD .
(i) MOMBI-II  abandons any Pareto dominance method, while the quality of the candidate solutions is fully determined by a set of reference vectors. The achievement scalarizing function is first embedded into the algorithm. MOMBI-II has adopted statistical information of previous generations to update the nadir point. More specifically, if we only employ the indicator, are preserved as the selected candidate solutions as shown in Figure 5(a). and corresponding to and will be deleted. The diversity has been seriously destroyed.
(a) Selection based on MOMBI-II
(b) Selection based on MOEA/D-PBI
(ii) MOEA/D  has attracted many researchers to remedy optimization problems since it was proposed in 2007. It is an excellent algorithm no matter the dimension of the objective space is low or high. Figure 5(b) has already illustrated the decomposition based selection strategy for a set of potential solutions . are the obtained solutions via adopting the PBI decomposition method. The convergence is perfect and the diversity can be maintained by a set of weight vectors. However, the obtained solutions discard the subspace in the example.
(iii) DBEA  is mainly based on the decomposition method. The algorithm adopts the corner sort ranking method to calculate the intercept point. The algorithm first considers the Pareto dominance to preserve the convergence. Then, the diversity distance metric is used to maintain the diversity. If the Pareto dominance and can not distinguish the potential solutions, the convergence distance metric is introduced. The contribution of DBEA  is the normalization strategy and the PBI decomposition method without introducing any penalty parameter . As shown in Figure 6(a), the ultimate selected solutions are . The subspace and the solution will be eliminated from the population. The algorithm considers convergence a bit more while the diversity will be damaged for MaOPs.
(a) Selection based on DBEA
(b) Selection based on MOEA/DD
(iv) MOEA/DD  is on the merit of Pareto dominance and the decomposition method to address MaOPs. It is an excellent algorithm to remedy high-dimensional optimization problems. As shown in Figure 6(b), the star individuals will be survived. The updated procedure is sophisticated and the efficient nondomination level updated approach (ENLU) is designed for steady-state EMOAs. The algorithm can comprehensively balance the convergence and diversity.
(v) -MOEA/D is the combination between the indicator and the decomposition based method. The indicator based selection can well deal with biobjective and three-objective optimization problems . However, the diversity will be damaged when only adopting the indicator to address MaOPs. Then, the PBI decomposition method is introduced as the diversity maintaining strategy to enhance the performance. More specifically, the final selected solutions are as shown in Figure 6(b). However, the combined selection strategy does not introduce any Pareto dominance approach. When a new offspring is generated, the position of the offspring has the possibility to update the ideal point. If the ideal point is updated, the combined population distribution should be repartitioned. In this situation, the indicator and the decomposition method are integrated to select the candidate solutions. If the ideal point remains unchanged, we only cluster the new individual other than the combined population. In this situation, we delete the individual which has the maximum PBI value in the most crowded subspaces.
4. Experimental Design
This section is devoted to the experimental setup for investigating the performance of -MOEA/D. At first, the benchmark test problems used in our experimental research are given. Then, we introduce the performance indicator to assess the convergence and diversity of these EMOAs. Finally, the experimental settings adopted in this study are provided.
4.1. Benchmark Test Problems
Empirical experiments are conducted on two well-known test suites for MaOPs, that is, DTLZ  and WFG . These test suits can be scaled to any number of objective spaces. For each test instance, the number of objective is varied from three to fifteen; that is, . As for the DTLZ test problems, the total number of decision variable is given by . is set to 5 for DTLZ1 and 10 for DTLZ2 to DTLZ4. As suggested in , the number of decision variables is set as , where is the position-related variable and is the distance-related variable for the WFG test instances.
4.2. Performance Measure
We select as a performance assessment measure. The performance indicator simultaneously evaluate proximity to the Pareto optimal front and spread of solutions along it. Given an approximation set and a sampled Pareto true Pareto front of MOPs, the indicator is defined as follows:where is the Euclidean distance from to its nearest member in and is the Euclidean distance from to its nearest member in . Small values of are preferred. We obtain the true from the PlatEMO . According to [28–31], is selected. Finally, we adopt the performance indicator to evaluate the algorithm performance.
4.3. EMOAs for Comparisons
The population size, termination condition, and parameter settings in each algorithm will be given as follows.
(1) The population size , the number of reference vectors, and reference points for different objectives are summarized in Table 1. They are determined by the simplex-lattice design factor together with the objective ; and are used to generate uniformly distributed reference vectors on the outer boundaries and the inside layers, respectively.