1 Introduction
Multiobjective optimization problems (MOPs) involve the optimization of more than one objective function. In the real world, many optimization problems invariably involve a number of constraints and a multiple conflicting objectives. In general, a CMOP can mathematically be described as follows:
(1) 
where is an
dimensional objective vector,
is the inequality constraint, and is the equality constraint. is an dimensional decision vector. The feasible region is defined as the set and .In CMOPs, there are usually more than one constraint. To demonstrate the degree of constraint violation, these constraints are commonly summarized into a scalar value as follows:
(2) 
when , the solution is feasible, otherwise it is infeasible.
For any two feasible solutions and of a CMOP, it can be said that dominates if the following condition is met:
(3) 
where . If there exists a solution , which dominates any other solutions in , can be said as a Pareto optimal solution. The set of all Pareto optimal solution belonging to is called as Pareto set (PS). The set of the mapping vectors of PS in the objective space is named as Pareto front (PF), which can be defined in the form of .
CMOPs are consist of a few objectives and constraints. To solve CMOPs, the constrainthandling technique should be applied to the framework of multiobjective evolutionary algorithm (MOEA).
According to different selection strategies, MOEAs mainly can be classified into three types: (1) Paretodominationbased; (2) decompositionbased; (3) indicatorbased. The typical examples of dominationbased MOEAs include NSGAII
996017 , MOGA Murata1995MOGA , PAESII corne2001pesa , SPEAII zitzler2001spea2 and NPGA Horn2002A . In recent years, decompositionbased MOEAs attract a lot of attention. The most representative algorithm of this type is MOEA/D Zhang:2007va . Some variants of MOEA/D include MOEA/DDE Li:2009vo , MOEA/DM2M Liu:2014jb , EAGMOEA/D Cai:2015gi and MOEA/DSAS Cai:2016ii . For indicatorbased MOEAs, they use a scalar metric to assist the selection, typical examples of this type are IBEA Zitzler:2004tm , SMSEMOA Beume2007SMS , HypE Bader2011Hype and FVMOEA Jiang:2015hj .To solve CMOPs, constrainthandling mechanisms are important. In recent years, many different constrainthandling mechanisms have been proposed Cai:2013iz ; Hu:2013kc . According to CoelloCoello20021245 , constrainthandling techniques can be generally classified into (1) penalty functions; (2) special representations and operators; (3) repair algorithms; (4) separate objectives and constraints; and (5) hybrid methods.
As a representative objectiveconstraint separating method, constraineddomination principle (CDP) proposed in 996017 is widely used. It solves CMOPs by treating constraints as the top priority. Stochastic ranking runarsson2000stochastic , constrained method Takahama2006cons and nongreedy constrainthandling technique singh2010c are also in this category.
Currently, CTPs deb2001multi and CFs zhang2008multiobjective are the most widely used CMOP test instances. The common characteristic of these benchmarks is that they all have large feasible regions in the objective space. However, constrainthandling mechanisms do not work when the working population falls in feasible regions, so these two series of instances are not actually suitable for testing the effectiveness of constrainthandling mechanisms.
The rest of this paper is organized as follows: Section 2 briefly introduces MOEA/D and four other decompositionbased CMOEAs. Section 3 introduces the details of the anglebased constrained dominance principle embedded in MOEA/D. Section 4 gives comprehensive experimental results of the proposed algorithm MOEA/DACDP and four other CMOEAs on LIRCMOPs and the Ibeam optimization problem. Finally, conclusions are made in section 5.
2 Relative Work
2.1 Moea/d
In the original framework of MOEA/D Zhang:2007va
, given a series of uniform distributed weight vectors, a MOP is decomposed into
scalar subproblems (SOPs), and each SOP relates to one solution. In MOEA/D, a set of uniformly spread weight vectors is initially generated for subproblems. A weight vector satisfies the following conditions:(4) 
There are several approaches to decompose a MOP into a number of scalar optimization subproblems Zhang:2007va ; miettinen1999nonlinear . Three decomposition approaches, including weighted sum miettinen1999nonlinear , Tchebycheff miettinen1999nonlinear and boundary intersection approaches Zhang:2007va are commonly used. In this paper, Tchebycheff decomposition method is used in the MOEA/D framework. The th subproblem is defined as follows:
minimize  (5)  
subject to 
where is the ideal point, and .
2.2 Decompositionbased CMOEAs
Decompositionbased CMOEAs combine the MOEA/D with different constrainthandling mechanisms. In this paper, we introduce four representative decompositionbased CMOEAs including CMOEA/D Asafuddoula2012An , MOEA/DCDP jan2013study , MOEA/DEpsilon Yang2014Epsilon , and MOEA/DSR jan2013study .

CMOEA/D Asafuddoula2012An uses a variant of the epsilon constrainthandling technique. In this technique, the epsilon level is set to handle constraints according to the constraint violation and the proportion of feasible solutions in the current population. When comparing any two solutions, if overall constraint violations of the solutions are both less than the epsilon level, the one with a better aggregation value dominates the other. Otherwise, the one with a smaller overall constraint violation dominates the other.

MOEA/DCDP jan2013study uses CDP 996017 to judge the domination relationship between two arbitrary solutions. CDP can be simply summarized as following three rules:
1) When two feasible solutions are compared, the one with a better aggregation value dominates the other.
2) When a feasible solution is compared with an infeasible solution, the feasible solution dominates the infeasible solution.
3) When two infeasible solutions are compared, the one with a smaller degree of constraint violation dominates the other.
The second and the third rules can be combined as a single rule: When at least one of two compared solutions is infeasible, the one with a smaller degree of constraint violation dominates the other. 
MOEA/DEpsilon Yang2014Epsilon uses the original epsilon constrainthandling technique. The epsilon level setting can be referred to Takahama2006cons . With the generation counter increasing, the epsilon level will dynamically decrease.

MOEA/DSR jan2013study embeds the stochastic ranking method (SR) runarsson2000stochastic in MOEA/D to deal with constraints. A threshold parameter is set to balance the selection between the objectives and the constraints in MOEA/DSR. When comparing two solutions, if a random number in is less than , the one with a better aggregation value is retained into the next generation. If the random number in is greater than , the whole framework of MOEA/DSR is similar to that of MOEA/DCDP. In the case of , MOEA/DSR is equivalent to MOEA/DCDP.
3 MOEA/D with Anglebased Constrained Dominance Principle
In this section, the definition of the proposed ACDP and the effectiveness of this mechanism in MOEA/D are detailed.
3.1 Anglebased Constrained Dominance Principle
In the CDP approach 996017 , with its three basic rules, the overall constraint violation is the most important factor during the evolutionary process, and some useful information in the infeasible regions tends to be ignored.
The angle between any two solutions in the objective space is useful information, which is related to the similarity between these two solutions. The definition of angle between any two solutions and is given as follows:
(6) 
where is the ideal point, and . is the twonorm of a vector.
As shown in Fig. 1, given any two solutions and , the angle between them in the objective space is . Obviously, the angle between any two solutions is less than or equal to , which means that the range of angle between any two solutions belongs in
Given any two solutions and , an threshold of angle , a random number and a parameter () which denotes the proportion of feasible solutions in the current population, the ACDP is defined as follows:

If and are both feasible, the one dominating the other is better.

If there is at lease one infeasible solution and , the one with a smaller constraint violation dominates the other.

When there is at least one infeasible solution and , if , the one dominating the other is better, otherwise, they are incomparable.
3.2 ACDP in the framework of MOEA/D
As we know, MOEA/D uses the value of decomposition function of a solution to update its neighbors. In order to use ACDP to handle constraints in the framework of MOEA/D, here we provide a version of ACDP which is suitable to the algorithm.
Given a subproblem with the weight vector , for two solutions and , their overall constraint violations are and , and their decomposition values on the subproblem are and . The ACDP dominance in the framework of MOEA/D is defined as follows:
(7) 
where is a threshold parameter, which is defined by users. In Eq. (7), the constrainthandling method ACDP is equivalent to CDP 996017 when .
In Rule 1 of ACDP, when these two solutions are both feasible, the solution with a lower aggregation value dominates the other, which is similar to the first rule of CDP.
When at least one of and is infeasible, CDP only utilizes the constraint violations of these two solutions to compare, which is difficult to keep the diversity of the working population when most of solutions in the population are infeasible. Nevertheless, ACDP utilizes additional information to compare the two solutions, which includes the angle between the two compared solutions in the objective space and the proportion of feasible solutions in the current population (). More details of ACDP in this situation are listed as follows:

In Rule 2 of ACDP, if the angle between and in the objective space is smaller than the parameter , ACDP considers that these two solutions are similar and compares them according to their constraint violations. Because these two solutions are similar, based on the framework of MOEA/D, they will be considered to relate to the same subproblem. In this situation, using the constraint violations to compare the two solutions will not cause the loss of the diversity.

In Rule 3 of ACDP, if the angle between and in the objective space is larger than the parameter
, ACDP considers that these two solutions are dissimilar, and the solution with a lower decomposition value will dominate the other with a probability of
. Some solutions with low aggregation values but large constraint violations will have a chance to be selected in the next generation, which can enhance the convergence of the working population effectively. 
The probability in Rule 3 of ACDP is set to be the proportion of feasible solutions in the current population. It keeps the balance of the exploration of the working population between infeasible regions and feasible regions. When is large, ACDP tends to explore infeasible regions. When is small, ACDP tends to explore feasible regions.
3.3 Effectiveness of ACDP in MOEA/D
The evolutionary process of a CMOEA can be generally divided in three stages according to the status of the working population.
In the first stage, a population is generated randomly, and most of the individuals are far away from the real PF as shown in Fig. 2 (a) and Fig. 2 (b).
In the second stage, the working population begins to explore the search space. As shown in Fig. 2 (c), when using CDP in MOEA/D, the working population will be attracted to feasible regions and actually difficult to get across infeasible regions. As shown in Fig. 2 (d), when ACDP is applied to MOEA/D, the working population can maintain the diversity by using angle information. Some individuals can enter infeasible regions, which can help the working population to go across infeasible regions effectively. Additionally, ACDP uses the information of the proportion of feasible solutions in the current population as the probability to select solutions, which can help to balance the search between feasible and infeasible regions.
In the third stage, the working population will converge to its near feasible regions. when using CDP, the population is trapped into local optimum, because of the difficulty to get across infeasible regions in the second stage, as shown in Fig. 2 (e). Conversely, when using ACDP, the working population can converge to the real PF more completely as shown in Fig. 2 (f), because the population can keep the diversity and explore infeasible regions in the second stage.
(a) Stage 1 of CDP (b) Stage 1 of ACDP 
(c) Stage 2 of CDP (d) Stage 2 of ACDP 
(e) Stage 3 of CDP (f) Stage 3 of ACDP 
3.4 The Parameter Setting of Theta
In the early stage of evolutionary process, the population is commonly far away from real PF. To prevent the population from being trapped into local optimum, the value of should be set small to maintain the diversity. In the later stage of evolutionary process, the convergence to the feasible regions should be emphasized, then the value of should become larger. Based on the above discussions, the threshold should be dynamically increased with the generation counter increasing. A method of setting is proposed as follows:
(8) 
where is an initial threshold value which is set as , is the size of population and is the maximum generation. is a parameter, which is set as 0.8. is the termination generation to control . Parameter is initialized to .
As we know, the uniform weight vectors defined in Eq. (4) decide that the maximum angle between two vectors is , and the average angle between two adjacent vectors is , where is the size of population. Then, is initially set as . According to Eq. (8), when the generation counter reaches , the value of is , and keeps constant afterwards. As shown in Fig. 3, we assume that the population size and the maximum generation are set as 300 and 500, respectively. Meanwhile is set as 0.8. We can find that . In the early stage of evolutionary process, increases continuously but slowly. It benefits the population to maintain diversity. When gets more and more closed to , rises faster, which helps to accelerate the convergence speed to the feasible regions. When reaches , is equal to , ACDP is transformed into CDP.
3.5 ACDP embedded in MOEA/D
The proposed MOEA/DACDP integrates the general framework of MOEA/D and the anglebased constrained dominance principle.
The psuecode of MOEA/DACDP is listed in Algorithm 1. Lines 15 initialize some parameters in MOEA/DACDP. First, a CMOP is decomposed into subproblems which are associated with weight vectors . Then the population , the initial increasing factor , the ideal point and the neighbor indexes are initialized. Lines 711 update the angle threshold value . Line 12 updates the proportion of feasible solutions in the current population . Lines 1323 generate a set of new solutions and update the ideal point . To be more specific, lines 1421 determine the set of neighboring solutions that may be updated by a newly generated solution . In line 22, the differential evolution (DE) crossover operator is adopted to generate a new solution . Meanwhile, is further mutated by the polynomial mutation operator. The ideal point is updated in line 23. Lines 2439 update subproblems. In line 27, the subproblems are updated based on the ACDP approach whose detailed psuecode is listed in Algorithm 2. At the end of each generation, nondominated solutions () in the population are selected to update the external archive based on nondominated sorting in line 31.
In Algorithm 2, the algorithm updates a subproblem in terms of Eq. (7). Lines 37 denote that when two feasible solutions and are compared, the one with a better aggregation value is selected. Lines 912 denote that when at least one of two solutions and is infeasible, if the angle between them in the objective space is lower than , the solution with a lower constraint violation is selected. Lines 1317 denote that when at least one of two solutions and is infeasible, if the angle between them in the objective space is larger than , the solution with a lower aggregation value will be selected with a probability of .
4 Experimental Study
4.1 Test Instances LIRCMOPs
To evaluate the performance of the proposed MOEA/DACDP, 14 constrained multiobjective test problems with large infeasible regions in the objective space are used fan2016difficulty ; Fan2017A .
The general characteristic of LIRCMOPs is that their real PFs are blocked by a number of large infeasible regions, and thus hard to be found during an evolutionary process. Their constraint functions are comprised of controllable shape functions and distance functions Huband:2006hi . More specifically, the shape functions are used to turn the PF shapes as convex or concave, while the distance functions are adopted to adjust the convergence difficulty for CMOEAs. Fig. 4 and Fig. 5 plot the feasible regions of LIRCMOPs with two or three objectives, respectively.
(a) LIRCMOP1 (b) LIRCMOP2 (c) LIRCMOP3 
(d) LIRCMOP4 (e) LIRCMOP5 (f) LIRCMOP6 
(g) LIRCMOP7 (h) LIRCMOP8 (i) LIRCMOP9 
(j) LIRCMOP10 (k) LIRCMOP11 (l) LIRCMOP12 
(a) LIRCMOP13 (b) LIRCMOP14 
4.2 Realworld Engineering Optimization: Ibeam
To evaluate the performance of MOEA/DACDP for solving real world optimization problems, an engineering optimization problem with two conflicting objectives is studied.
As defined in Osyczka1985Multicriteri , an optimization problem of Ibeam is a biobjective constrained optimization problem which needs to minimize the following objectives simultaneously:
1. Cross section area of the beam;
2. Static deflection of the beam for the displacement under the force .
To study the landscape in the objective space of the Ibeam optimization problem, 1,000,000 sampling solutions are generated, where 850,000 solutions are generated randomly, and the other 150,000 solutions are generated by MOEA/DACDP. In Fig. 6, it is observed that there exist a few infeasible regions (the proportion of feasible solutions in all sampling solutions , which means that nearly a half of selected points are infeasible.) in the objective space for the Ibeam optimization problem.
4.3 Experimental Settings
To evaluate the performance of the proposed MOEA/DACDP, four popular CMOEAs (CMOEA/D, MOEA/DCDP, MOEA/DEpsilon and MOEA/DSR), with differential evolution (DE) crossover operator, are adopted and tested on LIRCMOP114 and Ibeam optimization problem. The detailed parameters are listed as follows:

Mutation probability ( is the number of decision variables) and its distribution index is set to 20. For DE operator, , .

Population size: . Neighborhood size: .

Stopping condition: each algorithm runs for 30 times independently, and stops when 150,000 function evaluations are reached.

Probability of selecting individuals in the neighborhood: .

The maximal number of solutions replaced by a child: .

Parameter setting in MOEA/DACDP: and .

Parameter setting in MOEA/DEpsilon: , and .

Parameter setting in MOEA/DSR: .
4.4 Performance Metric
To measure the performance of MOEA/DACDP, CMOEA/D, MOEA/DCDP, MOEA/DEpsilon and MOEA/DSR, two widely used metrics inverted generation distance () Bosman2003The and hypervolume () Zitzler1999Multiobjective
are adopted as evaluation metrics. Their definitions are listed as follows.

Inverted Generational Distance ():
is a metric which evaluates the performance related to convergence and diversity simultaneously. Let be a set of uniformly distributed points in the ideal PF. Let denote an approximate PF achieved by a certain CMOEA. The metric that represents average distance from to is defined as:
(9) 
In our experiment, for CMOPs with two objectives, 1000 points are sampled uniformly from the PF to constitute . For CMOPs with three objectives, 10000 points are sampled uniformly from the PF to constitute . A smaller represents a better performance regarding to both diversity and convergence.

Hypervolume ():
reflects the closeness between the nondominated set achieved by a CMOEA and the representative PF. The larger means that the corresponding nondominated set is closer to the true PF.
(10) 
where is the Lebesgue measure, is a reference point in the objective space. For the test instances LIRCMOPs, the reference point is set as 1.4 times the nadir point of the real PF. The with a larger value represents the better performance regarding to both diversity and convergence.
As the real PF of the Ibeam optimization problem is not known, metric can not be calculated. The experiment uses the metric Zitzler1999Multiobjective to measure the performance of these CMOEAs. In the Ibeam optimization case, the reference point is set as .
4.5 Discussion of Experimental Results
4.5.1 Performance Evaluation on LIRCMOP Test Instances
The results of the values on LIRCMOP114 achieved by five CMOEAs in 30 independent runs are shown in Table 1.
As discussed in Section 4, LIRCMOP114 all have large infeasible regions in their objective space. For LIRCMOP314, MOEA/DACDP significantly outperforms the other four compared algorithms in terms of the metric. For LIRCMOP12, MOEA/DACDP significantly outperforms CMOEA/D, MOEA/DCDP and MOEA/DEpsilon.
The results of the values on LIRCMOP114 achieved by five CMOEAs in 30 independent runs are shown in Table 2. For LIRCMOP214, MOEA/DACDP significantly outperforms the compared algorithms in terms of the metric. For the LIRCMOP1, MOEA/DACDP significantly outperforms CMOEA/D, MOEA/DCDP and MOEA/DEpsilon.
Fig. 7 (a) shows the final external archives achieved by MOEA/DACDP and other four CMOEAs with the median values on LIRCMOP3 during 30 independent runs. It is obvious that MOEA/DACDP can almost converge to the whole real PF and has the best diversity among the five CMOEAs.
In Fig. 7 (b), the results of each CMOEA with the median values on LIRCMOP5 during 30 independent runs are shown. The external archive achieved by MOEA/DACDP covers the real PF. However, the other four CMOEAs are trapped into local optimum. As shown in Fig. 7 (c), for LIRCMOP10, MOEA/DACDP performs the best in terms of convergency. In Fig. 7 (d), for LIRCMOP11, it shows that MOEA/DACDP can get the most of the PF, but the other four algorithms can only achieve a few parts of the PF.
It is worthwhile to point out that for threeobjective test instances (LIRCMOP13 and LIRCMOP14), MOEA/DACDP also performs significantly better than the other four CMOEAs.
Based on the above performance comparison on the fourteen test instances LIRCMOP114, it is clear that MOEA/DACDP outperforms the other four decompositionbased CMOEAs on most of cases.
A common feature of the above test instances LIRCMOPs is that they all have large infeasible regions in their objective space. The experimental results demonstrate that the proposed ACDP method can deal with CMOPs well by taking advantage of angle information of the working population.
Test Instances  MOEA/DACDP  CMOEA/D  MOEA/DCDP  MOEA/DEpsilon  MOEA/DSR  

LIRCMOP1  mean  5.159E02  1.591E01  1.348E01  8.234E02  4.406E02 
std  1.815E02  3.534E02  5.996E02  5.321E02  3.360E02  
LIRCMOP2  mean  2.269E02  1.462E01  1.549E01  4.708E02  2.057E02 
std  9.418E03  4.141E02  2.966E02  1.339E02  1.072E02  
LIRCMOP3  mean  4.659E02  2.309E01  2.268E01  7.858E02  1.529E01 
std  1.850E02  4.135E02  4.403E02  2.978E02  7.688E02  
LIRCMOP4  mean  2.784E02  2.080E01  2.188E01  5.662E02  2.038E01 
std  1.477E02  4.197E02  3.766E02  3.366E02  7.907E02  
LIRCMOP5  mean  1.771E02  1.162E+00  1.207E+00  1.201E+00  1.123E+00 
std  2.965E02  2.180E01  1.660E02  1.963E02  2.842E01  
LIRCMOP6  mean  1.757E01  1.265E+00  1.303E+00  1.231E+00  1.175E+00 
std  4.129E02  3.067E01  2.319E01  3.602E01  3.967E01  
LIRCMOP7  mean  1.408E01  1.620E+00  1.623E+00  1.568E+00  1.136E+00 
std  4.385E02  3.036E01  2.905E01  4.101E01  7.315E01  
LIRCMOP8  mean  1.812E01  1.607E+00  1.631E+00  1.577E+00  1.369E+00 
std  4.854E02  2.680E01  2.464E01  3.767E01  5.735E01  
LIRCMOP9  mean  3.595E01  4.981E01  4.868E01  4.962E01  4.813E01 
std  5.345E02  6.991E02  5.372E02  6.987E02  4.571E02  
LIRCMOP10  mean  1.388E01  3.775E01  3.774E01  3.257E01  2.821E01 
std  1.148E01  7.446E02  6.858E02  9.833E02  1.135E01  
LIRCMOP11  mean  1.318E01  4.422E01  4.662E01  4.154E01  3.489E01 
std  4.487E02  1.759E01  1.439E01  1.508E01  1.129E01  
LIRCMOP12  mean  1.497E01  3.597E01  3.236E01  3.680E01  3.012E01 
std  9.985E03  1.074E01  1.023E01  8.664E02  8.989E02  
LIRCMOP13  mean  7.414E02  1.266E+00  1.289E+00  1.183E+00  1.093E+00 
std  2.727E03  2.173E01  6.321E02  3.456E01  4.269E01  
LIRCMOP14  mean  6.732E02  1.235E+00  1.103E+00  1.127E+00  1.143E+00 
std  1.918E03  1.209E01  3.857E01  3.329E01  3.002E01 
Wilcoxon’s rank sum test at a 0.05 significance level is performed between MOEA/DACDP and each of the other four CMOEAs. and denote that the performance of the corresponding algorithm is significantly worse than or better than that of MOEA/DACDP, respectively. The best mean is highlighted in boldface.
Test Instances  MOEA/DACDP  CMOEA/D  MOEA/DCDP  MOEA/DEpsilon  MOEA/DSR  

LIRCMOP1  mean  1.365E+00  9.499E01  1.009E+00  1.353E+00  1.376E+00 
std  2.493E02  7.038E02  1.298E01  4.417E02  3.974E02  
LIRCMOP2  mean  1.737E+01  1.395E+01  1.374E+01  1.705E+01  1.736E+01 
std  1.306E02  8.154E02  6.160E02  1.693E02  1.890E02  
LIRCMOP3  mean  1.188E+00  7.558E01  7.600E01  1.184E+00  9.313E01 
std  4.929E02  5.730E02  5.809E02  2.898E02  1.620E01  
LIRCMOP4  mean  1.421E+00  1.069E+00  1.051E+00  1.390E+00  1.089E+00 
std  1.946E02  6.952E02  5.462E02  4.405E02  1.360E01  
LIRCMOP5  mean  1.903E+00  1.192E01  5.805E02  5.829E02  1.707E01 
std  5.658E02  3.352E01  4.042E04  2.022E04  4.442E01  
LIRCMOP6  mean  1.280E+00  7.863E02  4.312E02  1.325E01  1.682E01 
std  4.613E02  3.011E01  2.362E01  4.251E01  4.061E01  
LIRCMOP7  mean  3.408E+00  2.990E01  2.886E01  4.055E01  1.313E+00 
std  1.409E01  6.927E01  6.348E01  8.879E01  1.567E+00  
LIRCMOP8  mean  3.330E+00  3.246E01  2.695E01  3.859E01  8.287E01 
std  1.461E01  5.878E01  5.297E01  8.166E01  1.244E+00  
LIRCMOP9  mean  4.080E+00  3.715E+00  3.755E+00  3.724E+00  3.752E+00 
std  9.501E02  2.079E01  1.600E01  2.033E01  1.142E01  
LIRCMOP10  mean  3.755E+00  3.274E+00  3.268E+00  3.385E+00  3.477E+00 
std  2.208E01  1.623E01  1.416E01  2.122E01  2.383E01  
LIRCMOP11  mean  5.004E+00  3.937E+00  3.842E+00  4.038E+00  4.274E+00 
std  1.564E01  6.479E01  5.507E01  5.727E01  4.463E01  
LIRCMOP12  mean  6.713E+00  5.977E+00  6.134E+00  6.010E+00  6.240E+00 
std  05.874E02  3.855E01  3.617E01  3.074E01  2.950E01  
LIRCMOP13  mean  7.897E+00  6.444E01  4.728E01  1.092E+00  1.513E+00 
std  2.943E02  1.317E+00  2.689E01  2.0522E+00  2.422E+00  
LIRCMOP14  mean  8.641E+00  7.766E01  1.627E+00  1.430E+00  1.269E+00 
std  1.546E02  6.140E01  2.473E+00  2.095E+00  1.919E+00 
Wilcoxon’s rank sum test at a 0.05 significance level is performed between MOEA/DACDP and each of the other four CMOEAs. and denotes that the performance of the corresponding algorithm is significantly worse than or better than that of MOEA/DACDP, respectively. The best mean is highlighted in boldface.
(a) LIRCMOP3 (b) LIRCMOP5 (c) LIRCMOP10 (d) LIRCMOP11 
4.5.2 Performance Evaluation on Ibeam Optimization Problem
The experimental results of values of MOEA/DACDP and the four other CMOEAs on the Ibeam optimization problem are shown in Table 3. It can be observed that MOEA/DACDP significantly outperforms the compared CMOEAs on this engineering problem.
To further study the superiority of the proposed method MOEA/DACDP, the nondominated solutions achieved by each CMOEA during the 30 independent runs are plotted in Fig. 8 (a)(e).The nondominated set of all the above solutions generates a set of ideal reference points. It is clear that the external archive obtained by MOEA/DACDP has a better performance of convergence. The box plot of values of the five CMOEAs is shown in Fig. 8 (f), which further verifies that MOEA/DACDP outperforms the other four CMOEAs on the Ibeam optimization problem.
(a) MOEA/DACDP (b) CMOEA/D (c) MOEA/DCDP 
(d) MOEA/DEpsilon (e) MOEA/DSR (f) The box plots of each CMOEA 
Test Instances  MOEA/DACDP  CMOEA/D  MOEA/DCDP  MOEA/DEpsilon  MOEA/DSR 

mean  3.583E+01  3.481E+01  3.518E+01  3.514E+03  3.477E+03 
std  1.950E01  2.968E01  2.078E01  2.034E01  1.248E+00 
Wilcoxon’s rank sum test at a 0.05 significance level is performed between MOEA/DACDP and each of the other four CMOEAs. and denote that the performance of the corresponding algorithm is significantly worse than or better than that of MOEA/DACDP, respectively. The best mean is highlighted in boldface.
5 Conclusions
This paper proposes a new constrainthandling mechanism named ACDP. It utilizes the angle information of any two solutions to dynamically maintain the diversity of the population during the evolutionary process. A set of CMOP instances named LIRCMOP114 are tested. All the test instances have large infeasible regions in their objective space, which make general CMOEAs difficult to achieve the real PFs. Compared with the other four popular CMOEAs, the proposed algorithm can help the population to go across large infeasible regions more effectively. Additionally, the experimental results demonstrate that the proposed algorithm can work well in the realworld engineering problem. Thus, we can conclude that MOEA/DACDP outperforms the other four CMOEAs when CMOPs. In summary, MOEA/DACDP has following advantages:

The proposed MOEA/DACDP utilizes the angle information of solutions to maintain the diversity of the population for CMOPs.

MOEA/DACDP enhances the convergence to PF by exploring feasible and infeasible regions simultaneously during the evolutionary process, instead of wasting the useful information of the infeasible solutions.
Future work will focus on novel constrainthandling mechanisms to solve CMOPs. A study on developing new mechanisms of mining more useful information during the evolutionary process to further improve the performance of the proposed algorithm will be conducted.
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant (61175073, 61300159, 61332002, 51375287) , the Guangdong Key Laboratory of Digital signal and Image Processing, the Science and Technology Planning Project of Guangdong Province (2013B011304002) and the Project of Educational Commission of Guangdong Province, China 2015KGJHZ014).
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