#### DMCA

## SEE PROFILE (2009)

### Citations

1906 |
Multi-Objective Optimization using Evolutionary Algorithms
- Deb
- 2001
(Show Context)
Citation Context ...lti-objective optimizationsproblems by using evolutionary algorithms [12],smulti-objective evolutionary algorithms (MOEAs) havesbeen much researched and are widely used to solvesnumerous applications =-=[13]-=-[14] in recent years. MOEAssbenefit from the evolutionary algorithm’s ability to generatesa set of solutions concurrently in a single run therebysyielding several trade-off solutions.sDifferential Evo... |

843 |
Evolutionary Algorithms for Solving Multi-Objective Problems
- Coello, Veldhuizen, et al.
- 2002
(Show Context)
Citation Context ...objective optimizationsproblems by using evolutionary algorithms [12],smulti-objective evolutionary algorithms (MOEAs) havesbeen much researched and are widely used to solvesnumerous applications [13]=-=[14]-=- in recent years. MOEAssbenefit from the evolutionary algorithm’s ability to generatesa set of solutions concurrently in a single run therebysyielding several trade-off solutions.sDifferential Evoluti... |

471 |
Multiple Objective Optimization With Vector Evaluated Genetic Algorithms
- Schaffer
- 1984
(Show Context)
Citation Context ...ONsANY real worldsproblems can be formulated assoptimization problems with multiplesobjectives. Sincesthe first attempt to solve multi-objective optimizationsproblems by using evolutionary algorithms =-=[12]-=-,smulti-objective evolutionary algorithms (MOEAs) havesbeen much researched and are widely used to solvesnumerous applications [13][14] in recent years. MOEAssbenefit from the evolutionary algorithm’s... |

428 |
Differential evolution–A simple and efficient heuristic for global optimization over continuous spaces
- Storn, Price
- 1997
(Show Context)
Citation Context ...nt years. MOEAssbenefit from the evolutionary algorithm’s ability to generatesa set of solutions concurrently in a single run therebysyielding several trade-off solutions.sDifferential Evolution (DE) =-=[8]-=- is one of the mostscommonly used EAs. It is a simple and powerfulspopulation-based stochastic direct search method for solvingsnumerical optimization problems in continuous search space.sIn DE, one c... |

123 | Differential evolution algorithm with strategy adaptation for global numerical optimization
- Qin, Huang, et al.
- 2009
(Show Context)
Citation Context ... be learned and applied to efficiently evolvesthe population at different stages. The SaDE algorithm hassdemonstrated promising performance when solving differentstypes of optimization problems [1][2]=-=[3]-=-[5].sIn this work, we improve the MOSaDE algorithm [5] withsV. L. Huang, S. Zhao, R. Mallipeddi and P. N. Suganthan are with School ofsElectrical and Electronic Engineering, Nanyang Technological Univ... |

70 |
Self-adaptive differential evolution algorithm for numerical optimization
- Qin, Suganthan
- 2005
(Show Context)
Citation Context ...rs can be learned and applied to efficiently evolvesthe population at different stages. The SaDE algorithm hassdemonstrated promising performance when solving differentstypes of optimization problems =-=[1]-=-[2][3][5].sIn this work, we improve the MOSaDE algorithm [5] withsV. L. Huang, S. Zhao, R. Mallipeddi and P. N. Suganthan are with School ofsElectrical and Electronic Engineering, Nanyang Technologica... |

53 | PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems
- Abbas, Sarker, et al.
(Show Context)
Citation Context ...xclusivesintegers randomly generated within the range [1, NP], thesesindices are randomly generated once for each mutant vector.sThese mutation strategies have been commonly used whenssolving MOPs [4]=-=[6]-=-[7][10][11].sThe main steps of OW-MOSaDE are described below:sStep 1. Randomly initialize a population of NP individuals.sInitialize n_obj number of vectors each corresponding to onesobjective. Each v... |

26 |
Control of population diversity and adaptation in differential evolution algorithms
- Zaharie
- 2003
(Show Context)
Citation Context ...ngsfactor F was generated for each variable from a Gaussiansdistribution (0,s1)N [7]. Zaharie proposed a parametersadaptation for DE (ADE) based on the concept of controllingsthe population diversity =-=[10]-=-. Following the same ideas,sZaharie and Petcu designed an adaptive Pareto DE algorithmsfor solving MOPs and analyzed its parallel implementations[11]. Xue et al. used a fuzzy logic controller (FLC) to... |

15 |
Adaptive pareto differential evolution and its parallelization
- Zaharie, Petcu
- 2004
(Show Context)
Citation Context ...he concept of controllingsthe population diversity [10]. Following the same ideas,sZaharie and Petcu designed an adaptive Pareto DE algorithmsfor solving MOPs and analyzed its parallel implementations=-=[11]-=-. Xue et al. used a fuzzy logic controller (FLC) tosdynamically adjust the parameters of multi-objectivesdifferential evolution [9].sIII. OBJECTIVE-WISE LEARNING IN MULTI-OBJECTIVEsSADEsThe earlier Mu... |

7 | Multiobjective differential evolution with external archive and harmonic distance-based diversity measure
- Huang, Suganthan, et al.
- 2005
(Show Context)
Citation Context ...y exclusivesintegers randomly generated within the range [1, NP], thesesindices are randomly generated once for each mutant vector.sThese mutation strategies have been commonly used whenssolving MOPs =-=[4]-=-[6][7][10][11].sThe main steps of OW-MOSaDE are described below:sStep 1. Randomly initialize a population of NP individuals.sInitialize n_obj number of vectors each corresponding to onesobjective. Eac... |

5 |
Abbass, “The self-adaptive Pareto differential evolution algorithm
- A
- 2002
(Show Context)
Citation Context ...f PDE, bysencoding the crossover rate into each individual andssimultaneously evolving with other parameters. The scalingsfactor F was generated for each variable from a Gaussiansdistribution (0,s1)N =-=[7]-=-. Zaharie proposed a parametersadaptation for DE (ADE) based on the concept of controllingsthe population diversity [10]. Following the same ideas,sZaharie and Petcu designed an adaptive Pareto DE alg... |

5 |
Fuzzy logic controlled multi-objective differential evolution,” The
- Xue, Sanderson, et al.
(Show Context)
Citation Context ...algorithmsfor solving MOPs and analyzed its parallel implementations[11]. Xue et al. used a fuzzy logic controller (FLC) tosdynamically adjust the parameters of multi-objectivesdifferential evolution =-=[9]-=-.sIII. OBJECTIVE-WISE LEARNING IN MULTI-OBJECTIVEsSADEsThe earlier Multi-objective SaDE algorithm (MOSaDE)s[5] is an extension of our recently developed SaDE [3] tosoptimize problems with multiple obj... |

3 |
Wudong Liu and Santosh Tiwari, “Multiobjective Optimization Test Instances for the CEC2009 Special Session and Competition,” Special Session on Performance Assessment of Multi-Objective Optimization Algorithms
- Zhang, Zhou, et al.
- 2008
(Show Context)
Citation Context ...l.ntu.edu.sg,sepnsugan@ntu.edu.sg)sobjective-wise learning strategies (called as OW-MOSaDE)sto solve problems with multiple conflicting objectives andsevaluate the performance on the 13 test problems =-=[15]-=-. Thesoriginal MOSaDE learns just one set of parameters for all thesobjectives. In MOPs, different objective functions mayspossess different properties. Hence, it can be beneficial toslearn one set of... |