#### DMCA

## Research Article An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization

### Citations

3755 | A Modified Particle Swarm Optimizer
- Shi, Eberhart
- 1998
(Show Context)
Citation Context ... in diverse fields [1]. Particle swarm optimization (PSO) and differential evolution (DE) are two stochastic, population-based optimization EAs [2]. PSO was introduced by Kennedy and Eberhart in 1995 =-=[3, 4]-=-. PSO uses a simple mechanism that mimics swarm behavior in birds flocking and fish schooling to guide the particles to search for globally optimal solutions. As PSO is easy to implement, it has rapid... |

846 | The particle swarm–explosion, stability, and convergence in a multidimensional complex space - Clerc, Kennedy - 2002 |

832 |
A new optimizer using particle swarm theory
- Eberhart, Kennedy
- 1995
(Show Context)
Citation Context ... in diverse fields [1]. Particle swarm optimization (PSO) and differential evolution (DE) are two stochastic, population-based optimization EAs [2]. PSO was introduced by Kennedy and Eberhart in 1995 =-=[3, 4]-=-. PSO uses a simple mechanism that mimics swarm behavior in birds flocking and fish schooling to guide the particles to search for globally optimal solutions. As PSO is easy to implement, it has rapid... |

440 |
Differential evolution: A practical approach to global optimization
- Price, Storn, et al.
- 2005
(Show Context)
Citation Context ...trial vector generation strategy and associated parameter values used. Inappropriate choice of strategies and parameters may lead to premature convergence, which have been extensively demonstrated in =-=[27]-=-. In the past decade, DE researchers have Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 215472, 16 pages http://dx.doi.org/10.1155/2014/215472 2 The Scientific Wor... |

429 |
Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces
- Price
- 1995
(Show Context)
Citation Context ...ning PSO (CLPSO) algorithm was proposed in [14], which shows its superiority in dealing with multimodal functions. DE is a simple yet powerful EA for global optimization introduced by Storn and Price =-=[20]-=-. The DE algorithm has gradually become more popular and has been used in many practical cases, mainly because it has demonstrated good convergence properties and is principally easy to understand [21... |

362 | Parameter selection in particle swarm optimization - Shi, Eberhart - 1998 |

319 |
An empirical study of particle swarm optimization
- Shi, Eberhart
- 1999
(Show Context)
Citation Context ... and social learning parameters, and ... |

309 | Particle swarm optimization: Developments, applications and resources - Eberhart, Shi |

193 | Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
- Ratnaweera, Halgamuge, et al.
- 2004
(Show Context)
Citation Context ... and social learning parameters, and ... |

164 |
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
- Liang, Qin, et al.
- 2006
(Show Context)
Citation Context ...uning parameters including inertia weights and acceleration coefficients for PSO have been proposed to enhance PSO’s search performance. A comprehensive learning PSO (CLPSO) algorithm was proposed in =-=[14]-=-, which shows its superiority in dealing with multimodal functions. DE is a simple yet powerful EA for global optimization introduced by Storn and Price [20]. The DE algorithm has gradually become mor... |

127 | The fully informed particle swarm: Simpler, maybe better - Mendes, Kennedy, et al. - 2004 |

127 |
Selfadapting control parameters in differential evolution: A comparative study on numerical benchmark problems
- Brest, Greiner, et al.
- 2006
(Show Context)
Citation Context ...ained. 2.4. DEVariants. In order to improve the performance ofDE, some adaptive DE variants are proposed. jDE was proposed based on the self-adaptation of the scale factor ... |

124 |
Defining a Standard for Particle Swarm Optimization
- Bratton, Kennedy
(Show Context)
Citation Context ...nuous Rastrigin [−5, 5] 0 0 ... |

123 | Differential evolution algorithm with strategy adaptation for global numerical optimization
- Qin, Huang, et al.
- 2009
(Show Context)
Citation Context ...cussed in detail. 1. Introduction Evolutionary algorithms (EAs), inspired by the natural evolution of species, have been successfully applied to solve numerous optimization problems in diverse fields =-=[1]-=-. Particle swarm optimization (PSO) and differential evolution (DE) are two stochastic, population-based optimization EAs [2]. PSO was introduced by Kennedy and Eberhart in 1995 [3, 4]. PSO uses a sim... |

77 |
JADE: Adaptive differential evolution with optional external archive
- Zhang, Sanderson
- 2009
(Show Context)
Citation Context ...chemes is generated according to either a Gaussian distribution or a Cauchy distribution [30]. JADE is another recent DE variant, in which a new mutation scheme named “/DE/current-topbest” is adopted =-=[31]-=-. 3. Proposed HPSO-DE Similar to other EAs, both of PSO andDE are the populationbased iterative algorithms. The PSO and DE can easily get trapped in the local optima when solving complex multimodal pr... |

70 |
Self-adaptive differential evolution algorithm for numerical optimization
- Qin, Suganthan
- 2005
(Show Context)
Citation Context ...ted trial vector ... |

67 | Adaptive particle swarm optimization - Zhan, Zhang, et al. - 2009 |

35 |
Padhy, “Application of particle swarm optimization technique and its variants to generation expansion planning,” Elec
- Kannan, Slochanal, et al.
- 2004
(Show Context)
Citation Context ...wo algorithms are combined together to form a new algorithm. DE is applied to each particle for a finite number of iterations to determine the best particle which is then included into the population =-=[33]-=-. DE is applied to the best particle obtained by PSO [34]. A hybrid version of PSO and DE is proposed which is named Barebones DE [35]. The evolution candidate solution is generated either by DE or by... |

28 | Tasgetiren, “Differential evolution algorithm with ensemble of parameters and mutation strategies - Mallipeddi, Suganthan, et al. - 2011 |

27 | Self-adaptive differential evolution with neighborhood search
- Yang, Tang, et al.
- 2008
(Show Context)
Citation Context ...ame way as SaDE except that only twomutation schemes are used, and the scale factor ... |

26 | Fitness-Distance-Ratio Based Particle Swarm Optimization,”
- Peram, Veeramachaneni, et al.
- 2003
(Show Context)
Citation Context ...thm Parameters Reference SPSO ... |

22 |
Population set-based global optimization algorithms: some modifications and numerical studies
- Ali, Törn
- 2004
(Show Context)
Citation Context ...PSO ... |

21 | Particle swarm optimization with recombination and dynamic linkage discovery - Chen, Peng, et al. - 2007 |

20 | Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients”,
- Tripathi, Bandyopadhyay, et al.
- 2007
(Show Context)
Citation Context ...wards the global optima is encouraged [3].With this view, a novel strategy in which time-varying acceleration coefficients are employed by changing the acceleration coefficients with time is proposed =-=[16, 38]-=-. With a large cognitive component and small social component at the beginning, particles are allowed to move around the search space, instead of moving toward the population best. On the other hand, ... |

16 | Differential evolution design of an IIRfilter - Storn - 1996 |

14 |
A particle swarm optimization algorithm with differential evolution
- Hao, Guo, et al.
- 2007
(Show Context)
Citation Context ...34]. A hybrid version of PSO and DE is proposed which is named Barebones DE [35]. The evolution candidate solution is generated either by DE or by PSO according to some fixed probability distribution =-=[36]-=-. A hybrid metaheuristic is designed so as to preserve the strengths of both algorithms [32]. However, it is worth mentioning that, in almost all the hybrid works mentioned above, the convergence rate... |

13 |
Hybrid particle swarm with differential evolution for multimodal image registration
- Talbi, Batouche
- 2004
(Show Context)
Citation Context ...hm. DE is applied to each particle for a finite number of iterations to determine the best particle which is then included into the population [33]. DE is applied to the best particle obtained by PSO =-=[34]-=-. A hybrid version of PSO and DE is proposed which is named Barebones DE [35]. The evolution candidate solution is generated either by DE or by PSO according to some fixed probability distribution [36... |

10 |
On setting the control parameter of the differential evolution method,”
- Liu, Lampinen
- 2002
(Show Context)
Citation Context ...20]. The DE algorithm has gradually become more popular and has been used in many practical cases, mainly because it has demonstrated good convergence properties and is principally easy to understand =-=[21]-=-. DE has been successfully applied in diverse fields of engineering [22–26]. The performance of the conventional DE algorithm highly depends on the chosen trial vector generation strategy and associat... |

9 | Bare bones differential evolution - Omran, Engelbrecht, et al. - 2009 |

8 | Krohling and L. dos Santos Coelho, “Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems,” - A - 2006 |

7 | A cooperative approach to participle swam optimization - Engelbrecht - 2004 |

7 | Identifying controlling nodes in neuronal networks in different scales,” - Tang, Gao, et al. - 2012 |

7 | Automatic image pixel clustering with an improved differential evolution - Das, Konar |

5 | A constrained evolutionary computation method for detecting controlling regions of cortical networks,” - Tang, Wang, et al. - 2012 |

5 | Controller Design for Synchronization of an Array of Delayed Neural Networks Using a Controllable Probabilistic PSO,” - Tang, Wang, et al. - 2011 |

4 | Differential evolution algorithm with ensemble of parameters and mutation strategies - Tasgetiren - 2011 |

4 | Differential evolution approach for optimal reactive power dispatch - Varadarajan, Swarup - 2008 |

4 |
DE-PSO: a new hybrid metaheuristic for solving global optimization problems,”NewMathematics
- Pant, Thangaraj
- 2011
(Show Context)
Citation Context ...n shortcomings associated with them which sometimes deteriorate the performance of algorithms. The major problem is the lack of diversity resulting in a suboptimal solution or a slow convergence rate =-=[32]-=-. In order to improve the performance of these algorithms, one of the class of modified algorithms consists of the hybridization of algorithms, where the two algorithms are combined together to form a... |

4 |
Enhanced oppositionbased differential evolution for solving high-dimensional continuous optimization problems
- Wang, Wu, et al.
- 2011
(Show Context)
Citation Context ... to frequently reported experimental studies, DE has shown better performance than many other EAs in terms of convergence speed and robustness over several benchmark functions and real-world problems =-=[39]-=-. In DE, there are three operators: mutation, crossover, and selection. Initially, a population is generated randomly with uniform distribution; then the mutation, crossover, and selection operators a... |

4 |
Improving the performance of differential evolution algorithm using Cauchy mutation
- Pant
- 2011
(Show Context)
Citation Context ... . . , ... |

3 | Parameters identification of unknown delayed genetic regulatory networks by a switching particle swarm optimization algorithm,” Expert Systems with Applications - Tang, Wang, et al. - 2011 |

3 |
Feedback learning particle swarm optimization,”
- Tang, Wang, et al.
- 2011
(Show Context)
Citation Context ...9 and 0.4. Therefore, the particle is to use lager inertia weight during the initial exploration and gradually reduce its value as the search proceeds in further iterations. According to the research =-=[37]-=-, the inertia weight is adjusted by (4). The nonlinear descending can achieve faster convergence speed than that with linear inertia weight: ... |

2 | A hybrid particle swarm optimization and its application in neural networks,” Expert Systems with Applications - Leung, Tang, et al. - 2012 |

2 |
Differential evolution based particle swarm optimization
- Salman
- 2007
(Show Context)
Citation Context ...mine the best particle which is then included into the population [33]. DE is applied to the best particle obtained by PSO [34]. A hybrid version of PSO and DE is proposed which is named Barebones DE =-=[35]-=-. The evolution candidate solution is generated either by DE or by PSO according to some fixed probability distribution [36]. A hybrid metaheuristic is designed so as to preserve the strengths of both... |

1 |
Fitness based differential evolution,”Memetic
- Sharma, Bansal, et al.
- 2012
(Show Context)
Citation Context ...ver are used to generate the trial vectors. The selection operator is used to select the best trial vector for the next generation.The initialization and DE operators are explained briefly as follows =-=[40]-=-. DE starts with a population of NP ... |