Multiobjective optimization using a Pareto differential evolution approach

By Nateri K. Madavan. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2002

318 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/8fb8ce6b6df07887df365a99e5f14edf11c60116

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. Xin-She Yang
2. Xingshi He
3. Mehmet Karamanoglu
4. Suash Deb
5. Seyedali Mirjalili
6. Qingfu Zhang
7. Arpan Kumar Kar
8. Zhihua Cai

In Computational Theory and Mathematics

1. Xin-She Yang
2. Xingshi He
3. Mehmet Karamanoglu
4. Qingfu Zhang
5. Suash Deb
6. Yaochu Jin
7. Zhihua Cai
8. Wenyin Gong

Citations in the corpus

• An efficient multiobjective differential evolution algorithm for engineering design (2009, methodology): “Such as Pareto DE (PDE) (Abbass et al. 2001), Pareto DE Approach ( Madavan 2002 ), Multiobjective DE (Xue et al. 2003), DEMO (Robiy ca nd Filipiy c 2005), GDE3 (Kukkonen and Lampinen 2005), and � -MyDE (Santana-Quintero and Coello 2005), which uses the� -dominance (Deb et al. 2003) to get a good dis”
• Multiobjective cuckoo search for design optimization (2013, methodology): “In particular, we have used other methods for comparison, including vector evaluated genetic algorithm (VEGA) [37], NSGA-II [12], multiobjective differential evolution (MODE) [41,3], differential evolution for multiobjective optimization (DEMO) [35], multiobjective bees algorithms (Bees) [30], and s”
• Recent advances in differential evolution: a survey and experimental analysis (2010, unknown): “In Madavan (2002), a DE incorporating the elitist sorting defined in Deb et al. (2002) is presented.”
• Multi-Objective Flower Algorithm for Optimization (2014, unknown): “Higher dimensional problems can have extremely complex hypersurface as its Pareto front [16, 17, 29].”
• Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II (2009, methodology): “Several variants of NSGA-II with DE [42], [44]–[46] have been proposed for dealing with rotated MOPs or MOPs with nonlinear variable linkages.”
• GDE3: the third evolution step of generalized differential evolution (2005, background): “Later on, Self-adaptive PDE (SPDE) [1], the Pareto DE Approach (PDEA) [26], Adaptive Pareto DE (APDE) [37], Multi-Objective DE (MODE) [13], and Vector Evaluated DE (VEDE) [29] have been proposed.”
• DEMO: Differential Evolution for Multiobjective Optimization (2005, methodology): “This is in contradiction with the recommendations by the authors of DE [10] and confirms Madavan’s findings in [8].”
• Flower pollination algorithm: A novel approach for multiobjective optimization (2014, background): “…differential evolution (MODE) (Babu 2007; Xue 2004), differential evolution for multiobjective optimization (DEMO) (Robiˇc and Filipiˇc 2005), multiobjective bees algorithms (Bees) (Pham and Ghanbarzadeh, 2007), and strength pareto evolutionary algorithm (SPEA) (Deb et al. 2002; Madavan 2002).”