A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization

Asafuddoula, Md.; Ray, Tapabrata; Sarker, Ruhul A.

doi:10.1109/tevc.2014.2339823

Cited by 343 publications

(155 citation statements)

References 38 publications

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“…TCH(y (3) ,z P ,w (1) )=0.1 TCH(y (3) ,z P ,w (2) )=0.12 TCH(y (3) ,z P ,w (3) )=0.23 TCH(y (3) ,z P ,w (4) )=0.35 TCH(y (2) ,z P ,w (1) )=0.25 TCH(y (2) ,z P ,w (2) )=0.17 TCH(y (2) ,z P ,w (3) )=0.08 TCH(y (2) ,z P ,w (4) )=0.1 TCH(y (1) ,z P ,w (1) )=0.45 TCH(y (1) ,z P ,w (2) )=0.3 TCH(y (1) ,z P ,w (3) )=0.15 TCH(y (1) ,z P ,w (4) each solution is regarded as the best solution obtained so far for each subproblem. Therefore, if a solution is not changed in this generation (i.e., no offspring succeeds to replace the parent), its improvement region is still the same in the next generation.…”

Section: Weight Vector Associationmentioning

confidence: 99%

“…Therefore, if a solution is not changed in this generation (i.e., no offspring succeeds to replace the parent), its improvement region is still the same in the next generation. We note that there exist other weight vector association procedures in several weight vector-based MOEAs (e.g., NSGA-III [9], I-DBEA [1]). However, the association procedures in these MOEAs are mainly used for niching or diversity preservation purposes.…”

Section: Weight Vector Associationmentioning

confidence: 99%

“…Especially the class of manyobjective problems, which is often defined as problems that have at least four objectives, poses substantial challenges for many MOEAs. The primary difficulty is that as the number of objectives increases, using the Pareto dominance relation in selection based on an entire population does not effectively maintain (selection) pressure toward the Pareto-optimal front since most solutions in the population of an MOEA are non-dominated [1,12].…”

Section: Introductionmentioning

confidence: 99%

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Improving the performance of MO-RV-GOMEA on problems with many objectives using tchebycheff scalarizations

Luong

Alderliesten

Bosman

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been shown to exhibit excellent performance in solving various bi-objective benchmark and real-world problems. We assess the competence of MO-RV-GOMEA in tackling many-objective problems, which are normally defined as problems with at least four conflicting objectives. Most Pareto dominance-based Multi-Objective Evolutionary Algorithms (MOEAs) typically diminish in performance if the number of objectives is more than three because selection pressure toward the Pareto-optimal front is lost. This is potentially less of an issue for MO-RV-GOMEA because its variation operator creates each offspring solution by iteratively altering a currently existing solution in a few decision variables each time, and changes are only accepted if they result in a Pareto improvement. For most MOEAs, integrating scalarization methods is potentially beneficial in the many-objective context. Here, we investigate the possibility of improving the performance of MO-RV-GOMEA by further guiding improvement checks during solution variation in MO-RV-GOMEA with carefully constructed Tchebycheff scalarizations. Results obtained from experiments performed on a selection of well-known problems from the DTLZ and WFG test suites show that MO-RV-GOMEA is by design already well-suited for many-objective problems. Moreover, by enhancing it with Tchebycheff scalarizations, it outperforms MOEA/D-2TCHMFI, a state-of-the-art decompositionbased MOEA. CCS CONCEPTS

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Section: Weight Vector Associationmentioning

confidence: 99%

Section: Weight Vector Associationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the performance of MO-RV-GOMEA on problems with many objectives using tchebycheff scalarizations

Luong

Alderliesten

Bosman

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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show abstract

“…After several decades of effort to the field of evolutionary computation, a huge number of MOEAs are available to date. They can be classified into three main groups: Pareto-based approaches (Deb et al, 2002;Knowles and Corne, 1999;Zitzler et al, 2002;Deb and Jain, 2014), indicator-based approaches (Bader and Zitzler, 2011;Beume et al, 2007;Ishibuchi et al, 2010;Zitzler and Kunzli, 2004), and decomposition-based approaches (Zhang and Li, 2007;Li and Zhang, 2009;Asafuddoula et al, 2015). The MOEA based on decomposition (MOEA/D) is the most well-known representative of decomposition-based approaches.…”

Section: Introductionmentioning

confidence: 99%

“…This way, MOEA/D can quickly approximate the Pareto front and provide a set of diverse solutions. Recently, various versions of MOEA/D have been proposed in the literature (Zhang and Li, 2007;Li and Zhang, 2009;Asafuddoula et al, 2015;Li et al, 2015a;Jiang and Yang, 2015), and the idea of decomposition has been exploited in a number of studies Li et al, 2014bLi et al, ,c, 2015aLiu et al, 2014;Yuan et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

Yang

Jiang

2016

Soft Comput

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It has been increasingly reported that the multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) is promising for handling multiobjective optimization problems (MOPs). MOEA/D employs scalarizing functions to convert an MOP into a number of single-objective subproblems. Among them, penalty boundary intersection (PBI) is one of the most popular decomposition approaches and has been widely adopted for dealing with MOPs. However, the original PBI uses a constant penalty value for all subproblems and has difficulties in achieving a good distribution and coverage of the Pareto front for some problems. In this paper, we investigate the influence of the penalty factor on PBI, and suggest two new penalty schemes, i.e., adaptive penalty scheme (APS) and subproblem-based penalty scheme (SPS), to enhance the spread of Pareto optimal solutions. The new penalty schemes are examined on several complex MOPs, showing that PBI with the use of them is able to provide a better approximation of the Pareto front than the original one. The SPS is further integrated into two recently developed MOEA/D variants to help balance the population diversity and convergence. Experimental results show that it can significantly enhance the algorithm's performance.

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A many‐objective evolutionary algorithm incorporating decision maker's preference and its application to management of the electricity distribution network

Sekizaki

Nishizaki

Hayashida

2019

Multi Criteria Decision Anal

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This paper proposes a step‐by‐step decision making approach that incorporates preferences of a decision maker into a many‐objective evolutionary algorithm. The algorithm is applied to a challenging many‐objective optimization problem (MaOP) involving integer objective values, tight constraints, heavy computational burden, and limited knowledge about the true Pareto front. The proposed method consists of five steps. (a) The entire Pareto front with high dimensions is roughly approximated by a small set of nondominated solutions. (b) On the basis of the approximated Pareto front, the decision maker specifies a preference. (c) Reference points are distributed through part of the objective space using the preference information. (d) The approximation accuracy of the Pareto front in the region of interest is improved by local search using the reference points. (e) The most preferred solution from a set of the nondominated solutions is chosen. The evolutionary algorithms employed in Steps (a) and (d) have an improved normalization procedure and selection mechanism for the MaOP involving integer objective value(s) and the tight constraints. Steps (b) and (e) use a graphical‐user‐interface‐based interactive tool we developed to efficiently narrow down to the preferred solution(s) among the high‐dimensional, nondominated solutions. This alleviates the cognitive burden on the decision maker and therefore aids the decision maker in handling MaOPs. In the computational experiment, we choose the real‐world application of reconfiguring an electricity distribution network. We demonstrate that the proposed approach can provide satisfying solutions to a decision maker.

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A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization

Cited by 343 publications

References 38 publications

Improving the performance of MO-RV-GOMEA on problems with many objectives using tchebycheff scalarizations

Improving the performance of MO-RV-GOMEA on problems with many objectives using tchebycheff scalarizations

Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

A many‐objective evolutionary algorithm incorporating decision maker's preference and its application to management of the electricity distribution network

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