Evolutionary dynamic optimization: A survey of the state of the art

By Trung-Thanh Nguyen, Shengxiang Yang, J. Branke. Swarm and Evolutionary Computation, 2012

660 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/8eeb7e057b2b9b506a98ff96043bdb60c51844a9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Seyedali Mirjalili
2. Yaochu Jin
3. Ponnuthurai Nagaratnam Suganthan
4. Swagatam Das
5. Xin-She Yang
6. Ran Cheng
7. Francisco Herrera
8. Gary G. Yen

In Computational Theory and Mathematics

1. Yaochu Jin
2. Seyedali Mirjalili
3. Ponnuthurai Nagaratnam Suganthan
4. Swagatam Das
5. Gary G. Yen
6. Xin-She Yang
7. Ran Cheng
8. Shengxiang Yang

Citations in the corpus

• A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, background): “Comprehensive surveys can be found in [13, 14].”
• Bio-inspired computation: Where we stand and what's next (2019, background): “to the overall conclusion that change detection should be avoided and efforts rather invested on passive/self-adaptive schemes [77,85].”
• An easy-to-use real-world multi-objective optimization problem suite (2020, background): “Studies on unconstrained multi-objective optimization problems can be the basis of studies on more challenging problems, including constrained multi-objective optimization problems [34], dynamic multi-objective optimization problems [35], and multi-modal multi-objective optimization problems [36].”
• Recent advances in differential evolution - An updated survey (2016, background): “, optima of the problem change their locations over time and, thus, the optimizer should be able to track the optima continually by responding to the dynamic environment [135].”
• Grasshopper optimization algorithm for multi-objective optimization problems (2017, background): “Therefore, a heuristic should be equipped with suitable operators to track the changes and to not lose the global optimum [19].”
• A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization (2014, methodology): “Using evolutionary algorithms to solve dynamic optimization problems has attracted increasing attention in recent years [20], [23], [24].”
• Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms (2016, background): “Much progress [4], [13]–[15] has been made in the DMOPs”