DEMO: Differential Evolution for Multiobjective Optimization

By Tea Robic, Bogdan Filipic. International Conference on Evolutionary Multi-Criterion Optimization, 2005

645 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fa5ae33d9000097c09f04c4ab63def27f565456a

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Citations in the corpus

Multiobjective firefly algorithm for continuous optimization (2012, methodology): “For example, multiobjective genetic algorithms are widely known [15, 21], while multiobjective diﬀerential evolution algorithms are also very powerful [22, 23].”
An efficient multiobjective differential evolution algorithm for engineering design (2009, methodology): “Solution x i is strictly better than solution x j in at least one constraint, i.e., ∃E k (x i ) > E k (x j ) (10) where E k (x) = min(0, e k (x)), and k = 1, · · · , m. 5.5 Handling the Constraint of the Variables After using the DE/rand/1/bin scheme to generate a new solution , if one or more of th”
Multicriteria Optimization and Decision Making: Principles, Algorithms and Case Studies (2024, unknown): “A stronger focus on variation/sampling operators design gave rise to algorithms such as the Multiobjective Estimation of Distribution Algorithm [12], the Differential Evolutionary Multiobjective Optimization (DEMO) [70] and the Multiobjective Covariance Matrix Adaptation Evolution Strategy (MOCMA-ES”
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”
GDE3: the third evolution step of generalized differential evolution (2005, methodology): “A similar approach was also proposed in DEMO [32] without constraint handling, and DEMO does not fall back to the original DE in the case of single objective as GDE3 does.”
Recent advances in differential evolution: a survey and experimental analysis (2010, background): “In Robič and Filipič (2005) a DE for multi-objective optimization integrating Pareto-based ranking and crowding distance sorting is presented.”
Multi-Objective Flower Algorithm for Optimization (2014, unknown): “[24] Robič T. and Filipič B., DEMO: differential evolution for multiobjective optimization, in: EMO 2005 (eds.”
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, methodology): “binary crossover (SBX) [67], polynomial mutation [68], and differential evolution (DE) [21], [69], [70] can be used to generate offspring candidate solutions.”
A tutorial on multiobjective optimization: fundamentals and evolutionary methods (2018, methodology): “Finally, the Differential Evolution Multiobjective Optimization (DEMO) (Robic and Filipic 2005) algorithm combines concepts from Pareto-based MOEAs with a variation operator from differential evolution, which leads to improved efﬁciency and more precise results in particular for continuous problems.”
Differential Evolution: A Survey of the State-of-the-Art (2011, background): “Robic and Filipic presented a DE for multiobjective optimization (called DEMO) in [83].”
Flower pollination algorithm: A novel approach for multiobjective optimization (2014, background): “…(Deb et al., 2000), multiobjective 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)…”