Na Yang
ABSTRACT
The increasing number of objectives poses a great challenge upon many-objective optimization algorithms (MaOOAs) when solving many-objective optimization problems (MaOOPs), since it is rather difficult to obtain well-distributed solutions with tight convergence. To efficiently improve the ability of solving MaOOPs, this paper proposes a hierarchical clustering-based cooperative multi-population many-objective optimization algorithm (C2MP-MaOOA). Specifically, a hierarchical clustering-based population division strategy is proposed in C2MP-MaOOA, which is able to effectively optimize different regions of the Pareto front (PF) regardless of its shape, so as to maintain population diversity and accelerate convergence. Any single-objective optimizer can be applied in C2MP-MaOOA to optimize a subpopulation. To comprehensively evaluate the performance of C2MP-MaOOA, it was compared with eight state-of-the-art existing algorithms and two variants of C2MP-MaOOA on 63 MaOOPs selected from DTLZ, MaF, and WFG benchmark suites. The results indicate that C2MP-MaOOA has the best overall performance for each benchmark suite, which demonstrates that C2MP-MaOOA is quite competitive in solving MaOOPs.
- Md Asafuddoula, Tapabrata Ray, Ruhul Sarker, and Khairul Alam. 2012. An adaptive constraint handling approach embedded MOEA/D. In 2012 IEEE congress on evolutionary computation. IEEE, 1--8.Google Scholar
- P.A.N. Bosman and D. Thierens. 2003. The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7, 2 (2003), 174--188. Google ScholarDigital Library
- Bin Cao, Xuesong Wang, Weizheng Zhang, Houbing Song, and Zhihan Lv. 2020. A Many-Objective Optimization Model of Industrial Internet of Things Based on Private Blockchain. IEEE Network 34, 5 (2020), 78--83. Google ScholarCross Ref
- Olacir R Castro, Aurora Pozo, Jose A Lozano, and Roberto Santana. 2017. An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study. Swarm Intelligence 11, 2 (2017), 101--130.Google ScholarCross Ref
- I. Das and J. E. Dennis. 1996. Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems. Siam Journal on Optimization 8, 3 (1996), 631--657.Google ScholarDigital Library
- Kalyanmoy Deb and Himanshu Jain. 2014. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Transactions on Evolutionary Computation 18, 4 (2014), 577--601. Google ScholarCross Ref
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182--197. Google ScholarDigital Library
- Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler. 2005. Scalable test problems for evolutionary multiobjective optimization. Springer.Google Scholar
- Roman Denysiuk, Lino Costa, and Isabel Espírito Santo. 2013. Many-objective optimization using differential evolution with variable-wise mutation restriction. In Proceedings of the 15th annual conference on Genetic and evolutionary computation. 591--598.Google ScholarDigital Library
- Roman Denysiuk, Lino Costa, and Isabel Espírito Santo. 2014. Clustering-Based Selection for Evolutionary Many-Objective Optimization. In Parallel Problem Solving from Nature - PPSN XIII, Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipič, and Jim Smith (Eds.). Springer International Publishing, Cham, 538--547.Google Scholar
- Salvador García, Alberto Fernández, Julián Luengo, and Francisco Herrera. 2010. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information sciences 180, 10 (2010), 2044--2064.Google Scholar
- Y. Hua, Y. Jin, and K. Hao. 2018. A Clustering-Based Adaptive Evolutionary Algorithm for Multiobjective Optimization With Irregular Pareto Fronts. IEEE Transactions on Cybernetics PP (2018), 1--13.Google Scholar
- S. Huband, P. Hingston, L. Barone, and L. While. 2006. A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 10, 5 (2006), 477--506.Google ScholarDigital Library
- Siwei Jiang, Jie Zhang, Yew-Soon Ong, Allan N Zhang, and Puay Siew Tan. 2014. A simple and fast hypervolume indicator-based multiobjective evolutionary algorithm. IEEE Transactions on Cybernetics 45, 10 (2014), 2202--2213.Google ScholarCross Ref
- Chenyang Li, Jun Li, and Huiling Chen. 2020. A Meta-Heuristic-Based Approach for Qos-Aware Service Composition. IEEE Access PP (04 2020), 1--1. Google ScholarCross Ref
- Qiuzhen Lin, Wu Lin, Zexuan Zhu, Maoguo Gong, Jianqiang Li, and Carlos A. Coello Coello. 2021. Multimodal Multiobjective Evolutionary Optimization With Dual Clustering in Decision and Objective Spaces. IEEE Transactions on Evolutionary Computation 25, 1 (2021), 130--144. Google ScholarCross Ref
- Qiuzhen Lin, Songbai Liu, Ka-Chun Wong, Maoguo Gong, Carlos A. Coello Coello, Jianyong Chen, and Jun Zhang. 2019. A Clustering-Based Evolutionary Algorithm for Many-Objective Optimization Problems. IEEE Transactions on Evolutionary Computation 23, 3 (2019), 391--405. Google ScholarCross Ref
- H. Liu, F. Gu, and Q. Zhang. 2014. Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems. IEEE Transactions on Evolutionary Computation 18, 3 (2014), 450--455.Google ScholarCross Ref
- S. Liu, Q. Lin, K. C. Wong, Cac Coello, and J. Zhang. 2020. A Self-Guided Reference Vector Strategy for Many-Objective Optimization. IEEE Transactions on Cybernetics PP, 99 (2020), 1--15.Google Scholar
- Si-Chen Liu, Zong-Gan Chen, Zhi-Hui Zhan, Sang-Woon Jeon, Sam Kwong, and Jun Zhang. 2021. Many-objective job-shop scheduling: a multiple populations for multiple objectives-based genetic algorithm approach. IEEE Transactions on Cybernetics (2021).Google Scholar
- Fabio López-Pires. 2016. Many-objective resource allocation in cloud computing datacenters. In 2016 IEEE international conference on cloud engineering workshop (IC2EW). IEEE, 213--215.Google ScholarCross Ref
- Gladston Moreira and Luís Paquete. 2019. Guiding under uniformity measure in the decision space. In 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI). 1--6. Google ScholarCross Ref
- C. Ran, M. Li, T. Ye, X. Zhang, and S. Yang. 2017. A benchmark test suite for evolutionary many-objective optimization. Complex & Intelligent Systems 3, 1 (2017), 67--81.Google ScholarCross Ref
- Rihab Said, Slim Bechikh, Ali Louati, Abdulaziz Aldaej, and Lamjed Ben Said. 2020. Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes. IEEE Access 8 (2020), 141674--141695. Google ScholarCross Ref
- Deepak Sharma, Devang Agarwal, and Santosh Kumar. 2021. Reference-lines steered guide assignment and update for pareto-based many-objective particle swarm optimization. Evolutionary Intelligence (2021), 1--26.Google Scholar
- Yanan Sun, Bing Xue, Mengjie Zhang, and Gary G Yen. 2018. A new two-stage evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation 23, 5 (2018), 748--761.Google ScholarCross Ref
- Y. Sun, G. G. Yen, and Y. Zhang. 2018. IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems. IEEE Transactions on Evolutionary Computation PP, 99 (2018), 1--1.Google Scholar
- Ryoji Tanabe and Hisao Ishibuchi. 2019. A niching indicator-based multi-modal many-objective optimizer. Swarm and Evolutionary Computation 49 (2019), 134--146.Google ScholarCross Ref
- Ye Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin. 2017. PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization. IEEE Computational Intelligence Magazine 12 (11 2017), 73--87. Google ScholarDigital Library
- Ye Tian, Ran Cheng, Xingyi Zhang, Yansen Su, and Yaochu Jin. 2018. A strengthened dominance relation considering convergence and diversity for evolutionary many-objective optimization. IEEE Transactions on Evolutionary Computation 23, 2 (2018), 331--345.Google ScholarCross Ref
- Chao Wang, Ziqiong Wang, Ye Tian, Xingyi Zhang, and Jianhua Xiao. 2021. A dual-population based evolutionary algorithm for multi-objective location problem under uncertainty of facilities. IEEE Transactions on Intelligent Transportation Systems 23, 7 (2021), 7692--7707.Google ScholarDigital Library
- Tianwei Wu, Siguang An, Jianqiang Han, and Nanying Shentu. 2022. An ∊-Domination based Two-Archive 2 Algorithm for Many-Objective Optimization. Journal of Systems Engineering and Electronics 33, 1 (2022), 156--169. Google ScholarCross Ref
- Yi Xiang, Yuren Zhou, Zefeng Chen, and Jun Zhang. 2018. A many-objective particle swarm optimizer with leaders selected from historical solutions by using scalar projections. IEEE transactions on cybernetics 50, 5 (2018), 2209--2222.Google Scholar
- Yi Xiang, Yuren Zhou, Xiaowei Yang, and Han Huang. 2019. A many-objective evolutionary algorithm with Pareto-adaptive reference points. IEEE Transactions on Evolutionary Computation 24, 1 (2019), 99--113.Google ScholarDigital Library
- Yi Xiang, Yuren Zhou, Zibin Zheng, and Miqing Li. 2018. Configuring software product lines by combining many-objective optimization and SAT solvers. ACM Transactions on Software Engineering and Methodology (TOSEM) 26, 4 (2018), 1--46.Google ScholarDigital Library
- Lei Yang, Xin Hu, and Ke Li. 2021. A vector angles-based many-objective particle swarm optimization algorithm using archive. Applied Soft Computing 106 (2021), 107299.Google ScholarDigital Library
- Na Yang, Zhenzhou Tang, Xuebing Cai, Long Chen, and Qian Hu. 2022. Cooperative multi-population Harris Hawks optimization for many-objective optimization. Complex & Intelligent Systems 8, 4 (2022), 3299--3332.Google ScholarCross Ref
- Zhi-Hui Zhan, Jingjing Li, Jiannong Cao, Jun Zhang, Henry Shu-Hung Chung, and Yu-Hui Shi. 2013. Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems. IEEE Transactions on Cybernetics 43, 2 (2013), 445--463. Google ScholarCross Ref
- Q. Zhang and L. Hui. 2008. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation 11, 6 (2008), 712--731.Google ScholarDigital Library
- Xingyi Zhang, Ye Tian, Ran Cheng, and Yaochu Jin. 2018. A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization. IEEE Transactions on Evolutionary Computation 22, 1 (2018), 97--112. Google ScholarCross Ref
- Zhenan, Yen, Gary, and G. 2016. Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement. IEEE transactions on evolutionary computation: A publication of the IEEE Neural Networks Council 20, 1 (2016), 145--160.Google Scholar
- Shuwei Zhu, Lihong Xu, and Erik D Goodman. 2021. Hierarchical topology-based cluster representation for scalable evolutionary multiobjective clustering. IEEE Transactions on Cybernetics 52, 9 (2021), 9846--9860.Google ScholarCross Ref
Index Terms
- A hierarchical clustering-based cooperative multi-population many-objective optimization algorithm
Recommendations
A reference points-based evolutionary algorithm for many-objective optimization
GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary ComputationMany-objective optimization problems are common in real-world applications, few evolutionary optimization methods, however, are suitable for them up to date due to their difficulties. We proposed a reference points-based evolutionary algorithm (RPEA) to ...
A multi-population evolutionary algorithm with single-objective guide for many-objective optimization
AbstractThis paper develops a multi-population evolutionary algorithm with single-objective guide to tackle many-objective optimization problems. It exploits the merits of both multiple populations and single-objective optimization to balance ...
Directed mating in decomposition-based MOEA for constrained many-objective optimization
GECCO '18: Proceedings of the Genetic and Evolutionary Computation ConferenceThis work proposes a decomposition-based algorithm, CMOEA/D-DMA, for constrained many-objective optimization. For constrained multi-objective optimization, the TNSDM algorithm using the directed mating selecting infeasible solutions having better ...
Comments