PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems
By Hussein A. Abbass, Ruhul Amin Sarker, Charles S. Newton. Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2001
585 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/5791ce2be17ee3bf3c4e867a41ba3d3990195696
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In Artificial Intelligence
- Seyedali Mirjalili
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In Computational Theory and Mathematics
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- Ponnuthurai Nagaratnam Suganthan
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- Wenyin Gong
- Shahrzad Saremi
- Saku Kukkonen
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In Statistics and Probability
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- Ponnuthurai Nagaratnam Suganthan
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Citations in the corpus
- Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms (2003, methodology): “The PDE [ 1 ] algorithm uses a nearest neighbor method to do niching.”
- Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems (2016, methodology): “Between the stochastic population-based algorithms, which is the focus of this work, the most well-regarded ones are: StrengthPareto Evolutionary Algorithm (SPEA) [26, 27], Nondominated Sorting Genetic Algorithm [28], Non-dominated sorting Genetic Algorithm version 2 (NSGA-II) [16], MultiObjective P”
- Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes (2006, background): “These solutions are optimal in the wide sense that no other solution in the search space is superior to them when all optimization objectives are simultaneously considered (Abbass et al., 2001).”
- DEMO: Differential Evolution for Multiobjective Optimization (2005, methodology): “Abbass [5, 6] was the first to apply DE to MOPs in the so-called Pareto Differential Evolution (PDE) algorithm.”
- Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, methodology): “DIVACE originates from memetic Pareto ANN (MPANN) [113], [114] employing Pareto differential evolution [115], and NCL algorithm.”
- An efficient multiobjective differential evolution algorithm for engineering design (2009, background): “Such as Pareto DE (PDE) [1], Pareto DE Approach (PDEA) [18], Multiobjective DE (MODE) [28], DE for Multiobjective Optimization (DEMO) [23], GDE3 [14], and -MyDE [24], which uses the -dominance [9] to get a good distribution of solutions retained.”
- A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm (2004, background): “There have been some studies to increase the convergence speed of the DE algorithm [8, 9].”
- GDE3: the third evolution step of generalized differential evolution (2005, methodology): “After these, the Pareto(-frontier) Differential Evolution (PDE) algorithm [2] and a first version of Generalized Differential Evolution (GDE) [22] were introduced.”