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Non-Dominated Sorting Bidirectional Differential Coevolution
Published 25 Oct 2024 in cs.NE | (2410.19439v1)
Abstract: Constrained multiobjective optimization problems (CMOPs) are commonly found in real-world applications. CMOP is a complex problem that needs to satisfy a set of equality or inequality constraints. This paper proposes a variant of the bidirectional coevolution algorithm (BiCo) with differential evolution (DE). The novelties in the model include the DE differential mutation and crossover operators as the main search engine and a non-dominated sorting selection scheme. Experimental results on two benchmark test suites and eight real-world CMOPs suggested that the proposed model reached better overall performance than the original model.
- J. Liang, X. Ban, K. Yu, B. Qu, K. Qiao, C. Yue, K. Chen, and K. C. Tan, “A survey on evolutionary constrained multiobjective optimization,” IEEE Transactions on Evolutionary Computation, vol. 27, no. 2, pp. 201–221, 2023.
- Z.-H. Zhan, L. Shi, K. C. Tan, and J. Zhang, “A survey on evolutionary computation for complex continuous optimization,” Artificial Intelligence Review, pp. 1–52, 2022.
- R. Storn and K. Price, “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, p. 341, 1997.
- K. R. Opara and J. Arabas, “Differential evolution: A survey of theoretical analyses,” Swarm and Evolutionary Computation, vol. 44, pp. 546–558, 2019.
- K. Yu, J. Liang, B. Qu, and C. Yue, “Purpose-directed two-phase multiobjective differential evolution for constrained multiobjective optimization,” Swarm and Evolutionary Computation, vol. 60, p. 100799, 2021.
- Y. Yang, J. Liu, S. Tan, and H. Wang, “A multi-objective differential evolutionary algorithm for constrained multi-objective optimization problems with low feasible ratio,” Applied Soft Computing, vol. 80, pp. 42–56, 2019.
- Y. Yang, J. Liu, S. Tan, and Y. Liu, “A multi-objective differential evolution algorithm based on domination and constraint-handling switching,” Information Sciences, vol. 579, pp. 796–813, 2021.
- J. Wang, G. Liang, and J. Zhang, “Cooperative differential evolution framework for constrained multiobjective optimization,” IEEE Transactions on Cybernetics, vol. 49, no. 6, pp. 2060–2072, 2019.
- B.-C. Wang, H.-X. Li, J.-P. Li, and Y. Wang, “Composite differential evolution for constrained evolutionary optimization,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 7, pp. 1482–1495, 2019.
- Y. Wang, Z. Cai, and Q. Zhang, “Differential evolution with composite trial vector generation strategies and control parameters,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 55–66, 2011.
- Z.-Z. Liu and Y. Wang, “Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces,” IEEE Transactions on Evolutionary Computation, vol. 23, no. 5, pp. 870–884, 2019.
- Z.-Z. Liu, B.-C. Wang, and K. Tang, “Handling constrained multiobjective optimization problems via bidirectional coevolution,” IEEE Transactions on Cybernetics, vol. 52, no. 10, pp. 10163–10176, 2022.
- R. Angira and B. Babu, “Non-dominated sorting differential evolution (nsde): An extension of differential evolution for multi-objective optimization.,” in IICAI, pp. 1428–1443, 2005.
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: Nsga-ii,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.
- H. Li and Q. Zhang, “Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 284–302, 2009.
- K. Deb, M. Goyal, et al., “A combined genetic adaptive search (geneas) for engineering design,” Computer Science and informatics, vol. 26, pp. 30–45, 1996.
- A. Kumar, G. Wu, M. Z. Ali, Q. Luo, R. Mallipeddi, P. N. Suganthan, and S. Das, “A benchmark-suite of real-world constrained multi-objective optimization problems and some baseline results,” Swarm and Evolutionary Computation, vol. 67, p. 100961, 2021.
- Y. Tian, R. Cheng, X. Zhang, and Y. Jin, “Platemo: A matlab platform for evolutionary multi-objective optimization [educational forum],” IEEE Computational Intelligence Magazine, vol. 12, no. 4, pp. 73–87, 2017.
- Z. Fan, W. Li, X. Cai, H. Li, C. Wei, Q. Zhang, K. Deb, and E. Goodman, “Difficulty Adjustable and Scalable Constrained Multiobjective Test Problem Toolkit,” Evolutionary Computation, vol. 28, pp. 339–378, 09 2020.
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