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Padding the dimensions for knowledge transfer in the dynamic vector evaluated particle swarm optimisation algorithm.

. SSCI, page 1-8. IEEE, (2016)

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Key challenges and future directions of dynamic multi-objective optimisation., , and . CEC, page 1256-1261. IEEE, (2016)The Effect of Quantum and Charged Particles on the Performance of the Dynamic Vector-evaluated Particle Swarm Optimisation Algorithm., and . GECCO, page 25-32. ACM, (2015)Decision-Maker's Preference-Driven Dynamic Multi-Objective Optimization., and . Algorithms, 16 (11): 504 (2023)Benchmarks for dynamic multi-objective optimisation algorithms., and . ACM Comput. Surv., 46 (3): 37:1-37:39 (2014)Comparison of Constraint Handling Approaches in Multi-objective Optimization., and . ICAISC (1), volume 10841 of Lecture Notes in Computer Science, page 345-362. Springer, (2018)Dynamic multi-objective optimization: introduction, challenges, applications and future directions.. GECCO Companion, page 818-838. ACM, (2021)Dynamic Vector-Evaluated PSO with Guaranteed Convergence in the Sub-Swarms., and . SSCI, page 1286-1293. IEEE, (2015)Solving Inverse Kinematics with Vector Evaluated Particle Swarm Optimization., and . ICSI (1), volume 10385 of Lecture Notes in Computer Science, page 225-237. Springer, (2017)Padding the dimensions for knowledge transfer in the dynamic vector evaluated particle swarm optimisation algorithm.. SSCI, page 1-8. IEEE, (2016)Using Headless Chicken Crossover for Local Guide Selection When Solving Dynamic Multi-objective Optimization., and . NaBIC, volume 419 of Advances in Intelligent Systems and Computing, page 381-392. Springer, (2015)