A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization

By Chi Keong Goh, Kay Chen Tan. IEEE Transactions on Evolutionary Computation, 2009

566 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/b2b89daf7b96cb32a578a976bfbfe42d382beba1

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Citations in the corpus

Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms (2016, background): “Goh and Tan [25] proposed a co-evolutionary multiobjective algorithm which”
A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization (2017, methodology): “This kind of strategy is often called memorybased approaches and has been extensively studied in dynamic multiobjective optimization [7], [8], [18], [22], [52].”
A survey on optimization metaheuristics (2013, background): “[109] to solve multiobjec tive optimiza tion problems in a dynamic environm ent.”
A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization (2014, methodology): “F2 [0, 1] × [−1, 1]n−1 f1(x, t) = x1, f2(x, t) = g(1 − ( f1 g ) ), dMOP1 in [54].”
Bio-inspired computation: Where we stand and what's next (2019, background): “dynamic multi-objective optimization problems [81] – but its potential has been scarcely studied [153].”
Distributed evolutionary algorithms and their models: A survey of the state-of-the-art (2015, background): “In [52], Goh and Tan further introduce a competitive rocess in the coevolution to improve the contribution of each”
Evolutionary dynamic optimization: A survey of the state of the art (2012, background): “along the idea of offline performance 427 [65, 62, 66, 67].”
Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems (2013, background): “, up to or larger than five), and those problems in dynamic [77], [78],”
A Coevolutionary Framework for Constrained Multiobjective Optimization Problems (2021, background): “…algorithms have shown effectiveness on many challenging problems, including large-scale optimization problems [30], dynamic optimization problems [31], many-objective optimization problems [32], and so on, but the development of coevolution for constraint handling is still in the infancy [18].”
Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017, background): “Some variants of these test problems can also be found in [33], [34].”
A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, methodology): “Different measurements are used when dealing with multiple objectives such as: spacing [57], hypervolume ratio [58], S-and FS-metrics [59], accuracy [60], stability [60], variable space generational distance [61] and maximum spread [61].”