Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling

By Kalyanmoy Deb, Udaya Bhaskara, Rao N, S. Karthik. International Conference on Evolutionary Multi-Criterion Optimization, 2007

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A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization (2017, methodology): “They are the MOEA based on decomposition (MOEA/D) [51], dynamic version of NSGA-II (DNSGA-II) [12], dynamic competitive-cooperative coevolutionary evolutionary algorithm (dCOEA) [18], and population prediction strategy (PPS) [54], representing different classes of metaheuristics.”
Evolutionary dynamic optimization: A survey of the state of the art (2012, unknown): “The diversity-maintaining strategy is still the main strategy in many recent approaches, for example, see [98,97,14,95,94,52, 71,104–107].”
Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms (2016, methodology): “[23] also shares a similar idea, and this method handles the DMOPs by introducing diversity when change is detected.”
A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization (2009, methodology): “A simple extension is to illustrate the performance trend over time [ 10 ], [14], [66] by assessing the evolved solutions with respect to static metrics at different time instances.”
A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization (2014, methodology): “A change is usually detected by: a) reevaluating solutions [1], [6], [26], [29], or b) checking population statistical information [13], [19], [30].”