Many-Objective Evolutionary Algorithms

Li, Bingdong; Li, Jinlong; Tang, Ke; Yao, Xin

doi:10.1145/2792984

Cited by 701 publications

(360 citation statements)

References 170 publications

Supporting

Mentioning

323

Contrasting

Order By: Relevance

“…Kernelized Fuzzy Rough Set Based Semi Supervised Support Vector Machine (KFRS-S3VM) [1] and iii. Multi-objective Particle Swarm Optimization (MPSO) [6], [21][22][23][24] have been discussed in the following subsections.…”

Section: Recently Proposed Data Mining Classifiersmentioning

confidence: 99%

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

Subasree

Gopalan²,

Sakthivel

2018

IJET

View full text Add to dashboard Cite

Microarray based Cancer Pattern Classification is one of the popular techniques in Bioinformatics Research. This Research Work is noticed that for studying the expression levels through the Gene Expression profiling experiments, thousands of Genes have to be simultaneously studied to understand the patterns of the Gene Expression or Cancer Pattern. This research work proposed an efficient Cancer Pattern Clas-sifier called An Enhanced Multi-Objective Pswarm (EMOPS) and it is studied thoroughly in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. The results were compared with the recently proposed classifiers namely Hybrid Ant Bee Algorithm (HABA), Kernelized Fuzzy Rough Set Based Semi Supervised Support Vector Machine (KFRS-S3VM) and Multi-objective Particle Swarm Optimization (MPSO). For analyzing the performances of the proposed model, this work considered a few cancer patterns namely Bladder, Breast, Colon, Endometrial, Kidney, Leukemia, Lung, Melanoma, Mom-Hodgkin, Pancreatic, Prostate and Thyroid. From our experimental results, it was noticed that the proposed model outperforms the identified three classifiers in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. To improve the performance of the system further in term of Processing Time, the proposed model Enhanced Multi-Objective Pswarm (EMOPS) is implemented under Parallel Framework and evaluated. That is the model is tested with Two, Four, Eight and Sixteen Parallel Processors and from the results, it is established that the Processing Time decreases considerably which will improve the performance of the Proposed Model.

show abstract

Section: Recently Proposed Data Mining Classifiersmentioning

confidence: 99%

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

Subasree

Gopalan²,

Sakthivel

2018

IJET

View full text Add to dashboard Cite

show abstract

“…Second, the role of non-dominated sorting in promoting population of KnEA to converge to the Pareto fronts degenerates on MaOPs in comparison to that on MOPs. The main reason is attributed to a phenomenon called dominance resistance [19], since the number of solutions will considerably increase as the number of objectives increases. Taking two random solutions in M-dimensional objective space as an example, the probability that one solution dominates the other one is ( 1 2 ) M−1 , as shown in Fig.…”

Section: Effectiveness For Many-objective Optimizationmentioning

confidence: 99%

“…In the existing MOEAs, non-dominated sorting is mainly performed in environmental selection, where solutions are divided into several ranks by non-dominated sorting and those in the same rank are distinguished by additional criteria, such as crowding distance in NSGA-II, GDE3, SMPSO, and EAG-MOEA/D, the region-based metric in PESA-II, and the enhanced IGD in MOEA/IGD-NS. As for MaOPs, non-dominated sorting has also been favored by researchers in developing MOEAs despite that its effectiveness considerably deteriorates on MaOPs due to the Pareto dominance resistance phenomenon [19]. Some representative MOEAs that directly adopted non-dominated sorting to handle MaOPs include GrEA [20], NSGA-III [21], KnEA [22], and LMEA [23].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

Tian

Wang

Zhang

et al. 2017

Complex Intell. Syst.

113

View full text Add to dashboard Cite

Since non-dominated sorting was first adopted in NSGA in 1995, most evolutionary algorithms have employed non-dominated sorting as one of the major criteria in their environmental selection for solving multi-and many-objective optimization problems. In this paper, we focus on analyzing the effectiveness and efficiency of nondominated sorting in multi-and many-objective evolutionary algorithms. The effectiveness of non-dominated sorting is verified by considering two popular evolutionary algorithms, NSGA-II and KnEA, which were designed for solving multiand many-objective optimization problems, respectively. The efficiency of non-dominated sorting is evaluated by comparing several state-of-the-art non-dominated sorting algorithms for multi-and many-objective optimization problems. These results provide important insights to adopt non-dominated sorting in developing novel multi-and many-objective evolutionary algorithms.

show abstract

“…Recently, many-objective optimization has received a lot of attention in the evolutionary multi-objective optimization (EMO) community, where optimization of four or more objectives is called many-objective optimization [14,15,23]. Many-objective problems present a number of challenges [10,19] to the EMO community such as the deterioration in search ability of Pareto dominance-based algorithms [6,29] and the increase in computation time of hypervolume-based algorithms [2,3].…”

Section: Introductionmentioning

confidence: 99%

On the effect of normalization in MOEA/D for multi-objective and many-objective optimization

Ishibuchi

Doi

Nojima

2017

Complex Intell. Syst.

View full text Add to dashboard Cite

The frequently used basic version of MOEA/D (multi-objective evolutionary algorithm based on decomposition) has no normalization mechanism of the objective space, whereas the normalization was discussed in the original MOEA/D paper. As a result, MOEA/D shows difficulties in finding a set of uniformly distributed solutions over the entire Pareto front when each objective has a totally different range of objective values. Recent variants of MOEA/D have normalization mechanisms for handling such a scaling issue. In this paper, we examine the effect of the normalization of the objective space on the performance of MOEA/D through computational experiments. A simple normalization mechanism is used to examine the performance of MOEA/D with and without normalization. These two types of MOEA/D are also compared with recently proposed many-objective algorithms: NSGA-III, MOEA/DD, and θ -DEA. In addition to the frequently used many-objective test problems DTLZ and WFG, we use their minus versions. We also propose two variants of the DTLZ test problems for examining the effect of the normalization in MOEA/D. where the range of each objective is the same on the Pareto front but totally different over the entire feasible region. Computational experiments on those test problems clearly show the necessity of the normalization. It is also shown that the normalization has both positive and negative effects on the performance of MOEA/D. These observations suggest that the influence of the normalization is strongly problem dependent.

show abstract

Many-Objective Evolutionary Algorithms

Cited by 701 publications

References 170 publications

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

On the effect of normalization in MOEA/D for multi-objective and many-objective optimization

Contact Info

Product

Resources

About