CONSTRAINT-HANDLING USING AN EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION TECHNIQUE

By Carlos Artemio Coello Coello. 2000

368 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/6ae249320b0e0e4e15541da5f6142ca85a96aba3

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In Computational Theory and Mathematics

Citations in the corpus

Multi-Verse Optimizer: a nature-inspired algorithm for global optimization (2015, methodology): “Some of the works that solved this problem are as follows: GA [31–33], CPSO [34], HS [35], Richardson’s random method, Simplex method, Davidon–Fletcher–Powell, and Grifﬁth and Stewart’s successive linear approximation [36].”
Grey Wolf Optimizer (2014, methodology): “Coello [64] and Deb [65, 66] employed GA, whereas Lee and Geem [67] used HS to solve this problem.”
An empirical study about the usefulness of evolution strategies to solve constrained optimization problems (2008, methodology): “To show the performance of the V(m þ 1) 2 ES when solving real-world problems, we tested it on three engineering design problems (Coello Coello 2000a).”
Comparison of the Luus–Jaakola optimization procedure and the genetic algorithm (2005, methodology): “The rank strategy used by Deb and Agrawal (1999) and Coello (2000) was adopted:”
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (2015, methodology): “We solved this problem by the MFO algorithm and compare it to GSA, GA [61] [62, 63], CPSO [64], HS [65], Richardson’s random method, Simplex method, Davidon-Fletcher-Powell, and Griffith and Stewart’s successive linear approximation [66].”
An improved harmony search algorithm for solving optimization problems (2007, methodology): “Deb [16,17] and Coello [13,21] solved this problem using GA-based methods.”
Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems (2016, methodology): “[34] C. A. C. Coello, “Theoretical and numerical constrainthandling techniques used with evolutionary algorithms: a survey of the state of the art,” Computer Methods in Applied Mechanics and Engineering, vol. 191, no. 11-12, pp. 1245–1287, 2002.”
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization (2006, methodology): “Besides, the method [23] based on Fonseca and Fleming’s”