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Volume 1, Issue 1
  • ISSN: 2666-7827
  • E-ISSN: 2666-7835

Abstract

The main purpose of this paper is to achieve good convergence and distribution in different Pareto fronts.

Research in recent decades has shown that evolutionary multi-objective optimization can effectively solve multi-objective optimization problems with no more than 3 targets. However, when solving MaOPs, the traditional evolutionary multi-objective optimization algorithm is difficult to effectively balance convergence and diversity. In order to solve these problems, many algorithms have emerged, which can be roughly divided into the following three types: decomposition-based, index-based, and dominance relationship-based. In addition, there are many algorithms that introduce the idea of clustering into the environment. However, there are some disadvantages to solving different types of MaOPs. In order to take advantage of the above algorithms, this paper proposes a many-objective optimization algorithm based on two-phase evolutionary selection.

In order to verify the comprehensive performance of the algorithm on the testing problem of different Pareto front, 18 examples of regular PF problems and irregular PF problems are used to test the performance of the algorithm proposed in this paper.

This paper proposes a two-phase evolutionary selection strategy. The evolution process is divided into two phases to select individuals with good quality. In the first phase, the convergence area is constructed by indicators to accelerate the convergence of the algorithm. In the second phase, the parallel distance is used to map the individuals to the hyperplane, and the individuals are clustered according to the distance on the hyperplane, and then the smallest fitness in each category is selected.

For regular Pareto front testing problems, MaOEA/TPS performed better than RVEA, PREA, CAMOEA and One by one EA in 19, 21, 30, 26 cases, respectively, while it was only outperformed by RVEA, PREA, CAMOEA and One by one EA in 8, 5, 1, and 6 cases. For the irregular front testing problem, MaOEA/TPS performed better than RVEA, PREA, CAMOEA and One by one EA in 20, 17, 25, and 21 cases, respectively, while it was only outperformed by RVEA, PREA, CAMOEA and One by one EA in 6, 8, 1, and 6 cases.

The paper proposes a many-objective evolutionary algorithm based on two phase selection, termed MaOEA/TPS, for solving MaOPs with different shapes of Pareto fronts. The results show that MaOEA/TPS has quite a competitive performance compared with the several algorithms on most test problems.

Although the algorithm in this paper has achieved good results, the optimization problem in the real environment is more difficult, therefore, applying the algorithm proposed in this paper to real problems will be the next research direction.

© 2022 Bentham Science Publishers
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/content/journals/cjai/10.2174/2666782701666210707144755
2022-04-01
2024-11-26

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References

  1. WangR. PurshouseR.C. FlemingP.J. Preference-inspired co-evolutionary algorithm using adaptively generated goal vectors.IEEE Congress on Evolutionary Computation2013 June 20-23Cancun, Mexico IEEE 201310.1109/CEC.2013.6557665
     [Google Scholar]
  2. ZhouY. WangJ. “A local search-based multiobjective optimization algorithm for multiobjective vehicle routing problem with time windows,” IEEE Syst. J., vol. 9, no. 3, pp. 1100-1113, Sep. 201.
  3. DebK. JainH. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints.IEEE Trans. Evol. Comput.201318457760110.1109/TEVC.2013.2281535
     [Google Scholar]
  4. SundaramE. GunasekaranM. KrishnanR. Genetic algorithm based reference current control extraction based shunt active power filter.Intern Trans on Elec Energy Sys2021311e1262310.1002/2050‑7038.12623
     [Google Scholar]
  5. SunJ. GongD.W. Recent advances in evolutionary many-objective optimization.Contr Theory App20183507928938
     [Google Scholar]
  6. GuoX.T. LiL.Y. ZhuC.Y. Two phase many-objective optimization algorithm based on pareto dominance relationship.J Front Comput Sci Technol2018120813501360
     [Google Scholar]
  7. BiX.J. ZhangY.J. ChenC.Y. A many-objective evolutionary algorithm based on fuzzy dominance: MFEA.Tien Tzu Hsueh Pao201442081653165910.3969/j.issn.0372‑2112.2014.08.031
     [Google Scholar]
  8. YangS. LiM. LiuX. A grid-based evolutionary algorithm for many-objective optimization.IEEE Trans. Evol. Comput.201317572173610.1109/TEVC.2012.2227145
     [Google Scholar]
  9. YuanY. XuH. WangB. A new dominance relation-based evolutionary algorithm for many-objective optimization.IEEE Trans. Evol. Comput.2015201163710.1109/TEVC.2015.2420112
     [Google Scholar]
  10. LiangZ. LuoT. HuK. MaX. ZhuZ. An indicator-based many-objective evolutionary algorithm with boundary protection.IEEE Trans. Cybern.20205194553456610.1109/TCYB.2019.2960302 31940581
     [Google Scholar]
  11. HeX. ZhouY. ChenZ. Evolutionary many-objective optimization based on dynamical decomposition.IEEE Trans. Evol. Comput.201823336137510.1109/TEVC.2018.2865590
     [Google Scholar]
  12. ZhangQ. LiH. MOEA/D: A multiobjective evolutionary algorithm based on decomposition.IEEE Trans. Evol. Comput.200711671273110.1109/TEVC.2007.892759
     [Google Scholar]
  13. HoareC A R. Algorithm 65: find.Commun. ACM196147321322
     [Google Scholar]
  14. WuY WangX FuY Many-objective brain storm optimization algorithm. IEEE Access 2019; 7: 186572-86.10.1109/ACCESS.2019.2960874
  15. YuanJ. LiuH.L. GuF. Investigating the properties of indicators and an evolutionary many-objective algorithm based on a promising region.IEEE Trans. Evol. Comput.20202517586
     [Google Scholar]
  16. Hernandez GomezR. Coello CoelloC.A. Improved metaheuristic based on the R2 indicator for many-objective optimization.Proc Ann Conf Gen Evol Comput20156798610.1145/2739480.2754776
     [Google Scholar]
  17. SunY. YenG.G. YiZ. IGD indicator-based evolutionary algorithm for many-objective optimization problems.IEEE Trans. Evol. Comput.2018232173187
     [Google Scholar]
  18. LiJ. ChenG. LiM. An enhanced-indicator based many-objective evolutionary algorithm with adaptive reference point.Swarm Evol. Comput.20205510066910.1016/j.swevo.2020.100669
     [Google Scholar]
  19. HuaY. JinY. HaoK. A clustering-based adaptive evolutionary algorithm for multiobjective optimization with irregular Pareto fronts.IEEE Trans. Cybern.2018497275827702999434210.1109/TCYB.2018.2834466
     [Google Scholar]
  20. LinQ. LiuS. WongK.C. A clustering-based evolutionary algorithm for many-objective optimization problems.IEEE Trans. Evol. Comput.201823339140510.1109/TEVC.2018.2866927
     [Google Scholar]
  21. DaiG. ZhouC. WangM. Indicator and reference points co-guided evolutionary algorithm for many-objective optimization problems.Knowl. Base. Syst.2018140506310.1016/j.knosys.2017.10.025
     [Google Scholar]
  22. LiuY. GongD. SunJ. JinY. A many-objective evolutionary algorithm using a one-by-one selection strategy.IEEE Trans. Cybern.20174792689270210.1109/TCYB.2016.2638902 28092588
     [Google Scholar]
  23. XiangY. ZhouY. YangX. A Many-objective evolutionary algorithm with Pareto-adaptive reference points.IEEE Trans. Evol. Comput.20192419911310.1109/TEVC.2019.2909636
     [Google Scholar]
  24. ChengR. JinY. OlhoferM. SendhoffB. A reference vector guided evolutionary algorithm for many-objective optimization.IEEE Trans. Evol. Comput.201620577379110.1109/TEVC.2016.2519378
     [Google Scholar]
  25. TianY. ChengR. ZhangX.Y. JinY.C. PlatEMO: A MATLAB platform for evolutionary multi-objective optimization.IEEE Comput. Intell. Mag.2017124738710.1109/MCI.2017.2742868
     [Google Scholar]
  26. DebK. ThieleL. LaumannsM. ZitzlerE. “Scalable test problems for evolutionary multiobjective optimization,” in evolutionary multiobjective optimization.New York, USASpringer200510514510.1007/1‑84628‑137‑7_6
     [Google Scholar]
  27. HubandS. HingstonP. BaroneL. WhileL. A review of multiobjective test problems and a scalable test problem toolkit.IEEE Trans. Evol. Comput.200610547750610.1109/TEVC.2005.861417
     [Google Scholar]
  28. ChengR. LiM. TianY. ZhangX. YangS. JinY. YaoX. A benchmark test suite for evolutionary many-objective optimization.Complex & Intelligent Systems2017316781
     [Google Scholar]
  29. Coello CoelloC.A. LamontG.B. VeldhuizenD.A.V. Evolutionary algorithms for solving multi-objective problems. 2nd ed. New York, NY: Springer Science + Business Media, LLC 2007.
  30. ZhouA. JinY. ZhangQ. SendhoffB. TsangE. Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence crite-rion IEEE Congr Evolution Comput (CEC 2006). 892-9.
  31. XiangY ZhouY LiM A vector angle-based evolutionary algorithm for unconstrained many-objective optimization. IEEE Trans Evol Comput 2016; 21(1): 131-52.10.1109/TEVC.2016.2587808
/content/journals/cjai/10.2174/2666782701666210707144755
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A Many-objective Evolutionary Algorithm Based on Two-phase Selection
Chin J Arti Intel 1, E070721194579 (2022); https://doi.org/10.2174/2666782701666210707144755
/content/journals/cjai/10.2174/2666782701666210707144755
/content/journals/cjai/10.2174/2666782701666210707144755
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  • Article Type:     Research Article
Keyword(s): clusters; convergence; diversity; MaOEA/TPS; MaOPs; parallel distance similarity

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