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## Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution

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3520 | Particle swarm optimization
- Kennedy, Eberhart
- 1995
(Show Context)
Citation Context ...ation among members of the same species. Moreover, the behavior of the individuals of a flock corresponds to fundamental rules, such as nearest-neighbor velocity matching and acceleration by distance =-=[2]-=-, [3]. The PSO algorithm is capable of handling non-differentiable, discontinuous and multimodal objective functions and has gained increasing popularity in recent years due to its ability to efficien... |

2749 | Dubes. Algorithms for Clustering Data - Jain, C - 1988 |

812 | The particle swarm-explosion, stability, and convergence in a multidimensional complex space
- Clerc, Kennedy
- 2002
(Show Context)
Citation Context ... ring topology, the neighborhood of each particle consists of particles with neighboring indices [24], [25]. In the present investigation, we consider the version of PSO proposed by Clerc and Kennedy =-=[26]-=-, which incorporates the parameter χ, known as the constriction factor. The main role of the constriction factor is to control the magnitude of the velocities and alleviate the “swarm explosion” effec... |

783 |
A new optimizer using particle swarm theory, in
- Eberchart, Kennedy
- 1995
(Show Context)
Citation Context ...algorithm. I. INTRODUCTION The Particle Swarm Optimization (PSO) algorithm is an Evolutionary Computation method, which belongs to the broad class of Swarm Intelligence methods. PSO was introduced in =-=[1]-=-, is inspired by the social behavior of bird flocking and fish schooling, and is based on a socialpsychological model of social influence and social learning. The fundamental hypothesis to the develop... |

413 |
Differential Evolution: A Practical Approach to Global Optimization
- Price, Storn, et al.
- 2005
(Show Context)
Citation Context ...t the respective original PSO variants, we utilized the F1 − F6 50-dimensional benchmark functions to calculated two performance measures: the Success Rate (SR) and the Success Performance (SP) [28], =-=[29]-=-, [37]. The SR is defined as the fraction of the number of times the algorithm has reached the global optimum during a pre-specified budget of function evaluations over the total number of simulations... |

408 | Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces
- Storn, Price
- 1997
(Show Context)
Citation Context ...timization algorithm, several variations and hybrid approaches have been proposed [6]–[11]. One class of variations include hybrids that combine the PSO and the Differential Evolution (DE) algorithms =-=[12]-=-. These approaches aim to aggregate the advantages of both methods to efficiently tackle the optimization problem at hand. The PSO–DE hybrids usually combine the evolution schemes of both algorithms t... |

327 | Evolutionary programming made faster
- Yao, Liu, et al.
- 1999
(Show Context)
Citation Context ...veness of the proposed approach we have used twelve widely known high dimensional benchmark functions with different characteristics. These function are from two recently proposed benchmark test sets =-=[35]-=-, [36]. The first set of six test functions (F1 −F6) are high dimensional multimodal functions, where the number of local minima increases exponentially with their dimensionality [35]. The remaining s... |

214 | A cooperative coevolutionary approach to function optimization. See Davidor and Schwefel - Potter, Jong - 1994 |

200 | Recent approaches to global optimization problems through particle swarm optimization. - Parsopoulos, Vrahatis - 2002 |

169 |
J.:Evolutionary optimization versus particle swarm optimization: Philosophy and the performance difference
- Angeline
- 1998
(Show Context)
Citation Context ...he main role of the constriction factor is to control the magnitude of the velocities and alleviate the “swarm explosion” effect that sometimes prevented the convergence of the original PSO algorithm =-=[27]-=-. As stated in [26], the dynamic behavior of the particles in the swarm is manipulated using the following equations: ( ( Vi(t + 1) = χ Vi(t) + c1r1 Pi(t) − Xi(t) ) ( +c2r2 Pbest(t) − Xi(t) )) , (1) X... |

152 |
Computational intelligence: an introduction
- Engelbrecht
- 2007
(Show Context)
Citation Context ...le, discontinuous and multimodal objective functions and has gained increasing popularity in recent years due to its ability to efficiently and effectively tackle several real-world applications [4], =-=[5]-=-. To improve the performance and the convergence behavior of Particle Swarm Optimization algorithm, several variations and hybrid approaches have been proposed [6]–[11]. One class of variations includ... |

149 |
A Cooperative Approach to Particle Swarm Optimization
- Bergh, Engelbrecht
- 2004
(Show Context)
Citation Context ...ral real-world applications [4], [5]. To improve the performance and the convergence behavior of Particle Swarm Optimization algorithm, several variations and hybrid approaches have been proposed [6]–=-=[11]-=-. One class of variations include hybrids that combine the PSO and the Differential Evolution (DE) algorithms [12]. These approaches aim to aggregate the advantages of both methods to efficiently tack... |

149 | Comprehensive learning particle swarm optimizer for global optimization of multimodal functions - Liang, Qin, et al. - 2006 |

129 | Particle Swarm Optimization
- Clerc
- 2006
(Show Context)
Citation Context ...ntiable, discontinuous and multimodal objective functions and has gained increasing popularity in recent years due to its ability to efficiently and effectively tackle several real-world applications =-=[4]-=-, [5]. To improve the performance and the convergence behavior of Particle Swarm Optimization algorithm, several variations and hybrid approaches have been proposed [6]–[11]. One class of variations i... |

120 |
Defining a standard for particle swarm optimization
- Bratton, Kennedy
- 2007
(Show Context)
Citation Context ... the PSO variants utilize the ring topology which is the most common topology in the literature. In the ring topology, the neighborhood of each particle consists of particles with neighboring indices =-=[24]-=-, [25]. In the present investigation, we consider the version of PSO proposed by Clerc and Kennedy [26], which incorporates the parameter χ, known as the constriction factor. The main role of the cons... |

110 | The fully informed particle swarm: Simpler, maybe better
- Mendes, Kennedy, et al.
- 2004
(Show Context)
Citation Context ...SO variants utilize the ring topology which is the most common topology in the literature. In the ring topology, the neighborhood of each particle consists of particles with neighboring indices [24], =-=[25]-=-. In the present investigation, we consider the version of PSO proposed by Clerc and Kennedy [26], which incorporates the parameter χ, known as the constriction factor. The main role of the constricti... |

110 |
Experimental research in evolutionary computation
- Bartz-Beielstein, Preuss
- 2007
(Show Context)
Citation Context ...respective original PSO variants, we utilized the F1 − F6 50-dimensional benchmark functions to calculated two performance measures: the Success Rate (SR) and the Success Performance (SP) [28], [29], =-=[37]-=-. The SR is defined as the fraction of the number of times the algorithm has reached the global optimum during a pre-specified budget of function evaluations over the total number of simulations and t... |

110 | Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems - Brest, Greiner, et al. |

107 | Differential evolution algorithm with strategy adaptation for global numerical optimization - Qin, Huang, et al. |

94 | Practical Nonparametric Statistics, Third Edition - Conover - 1999 |

90 | Multiswarms, exclusion, and anticonvergence in dynamic environments - Blackwell, Branke |

89 | Differential evolution: A survey of the state-of-the-art - Das, Suganthan |

79 | Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power - Garćıa, Fernández, et al. - 2010 |

73 | On the computation of all global minimizers through particle swarm optimization
- Parsopoulos, Vrahatis
- 2004
(Show Context)
Citation Context ... < f(xig ) otherwise . x i g , IV. STUDYING THE COGNITIVE AND SOCIAL EXPERIENCE Numerous PSO variations have been proposed to improve the accuracy of solutions and PSO convergence behavior [7], [25], =-=[32]-=-. In [11], [26] has been formally proven that each particle converges to a weighted average of its personal best and neighborhood best positions. Motivated by this finding new variants have been intro... |

69 | Self-adaptive differential evolution algorithm for numerical optimization, in: Proc - Qin, Suganthan |

67 |
A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems
- Vesterstrom, Thomsen
(Show Context)
Citation Context ...EAs). The DE method requires few control parameters and several experimental studies have shown that DE has good convergence properties and outperforms other well known and widely used EAs [12], [28]–=-=[30]-=-. More specifically, DE is a population–based stochastic algorithm that exploits a population of potential solutions, individuals, to effectively probe the search space. Like PSO, the population of in... |

66 | Scalability of a heterogeneous particle swarm optimizer - Engelbrecht - 2011 |

65 |
Bare bones particle swarm
- Kennedy
- 2003
(Show Context)
Citation Context ...weighted average of its personal best and neighborhood best positions. Motivated by this finding new variants have been introduced that incorporate knowledge which exploit the best personal positions =-=[33]-=-. Moreover, the exploitation of the best personal experience has been incorporated in several PSO variants with multiple different methodologies. Specifically, some variants adapt the best personal po... |

62 | DEPSO: Hybrid Particle Swarm with Differential Evolution Operator
- Zhang, Xie
(Show Context)
Citation Context ...ed sum of best personal positions [25]. Other variants incorporate update schemes that utilize information of the best personal positions by means of an average of two or more best personal positions =-=[19]-=-, [34]. Fig. 1. local PSO population’s positions after 1, 5, 10, and 20 generations Fig. 2. local PSO population’s best personal positions after 1, 5, 10, and 20 generations The aforementioned approac... |

61 | Dynamic multi-swarm particle swarm optimizer - Liang, Suganthan - 2005 |

60 | A fuzzy adaptive differential evolution algorithm - Liu, Lampinen |

51 | A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms - Derrac, García, et al. - 2011 |

45 |
A trigonometric mutation operation to differential evolution
- Fan, LAMPINEN
- 2003
(Show Context)
Citation Context ... Therefore, an inappropriate mutation constant value can cause deceleration of the algorithm and decrease of the population diversity. Furthermore, here we utilize the trigonometric mutation operator =-=[31]-=-, which performs a mutation according to the following equation, with probability τµ: 6) “TDE/rand/1” v i g+1 =(xr1 g +xr2 g +xr3 g )/3+(p2 −p1)(x r1 g −xr2 g )+ +(p3 −p2)(x r2 g −x r3 g )+(p1 −p3)(x ... |

39 | On Stagnation of the Differential Evolution Algorithm - Lampinen, Zelinka - 2000 |

39 | Parallel evolutionary training algorithms for “hardware–friendly” neural networks - Plagianakos, Vrahatis |

38 | A new method for determining the type of distribution of plant individuals - Hopkins - 1954 |

35 | Differential evolution using a neighborhood-based mutation operator - Das, Abraham, et al. |

35 | UPSO: A unified particle swarm optimization scheme - Parsopoulos, Vrahatis - 2004 |

34 | Improvised music with swarms - Blackwell, Bentley, et al. - 2002 |

31 | Particle Swarm, Genetic Algorithm, and their Hybrids: Optimization of a Profiled Corrugated Horn Antenna - Robinson, Sinton, et al. |

30 | Population Topologies and Their Influence in Particle Swarm Performance - Mendes - 2004 |

29 |
Application of particle swarm optimization technique and its variants to generation expansion problem
- Kannan, Slochanal, et al.
- 2004
(Show Context)
Citation Context ...onal Intelligence Laboratory (CILab), Department of Mathematics, University of Patras, Greece. scheme [13]–[17], apply one of the two algorithms as local search to evolve some pre-specified particles =-=[18]-=-–[20], or evolve the control parameters with one of the evolutionary approaches to produce a parameter-free hybrid [13], [21]– [23]. The current study has been motivated by the behavior and the proxim... |

27 | Stretching technique for obtaining global minimizers through particle swarm optimization - Parsopoulos, Plagianakos, et al. - 2001 |

26 |
Benchmark functions for the CEC’2008 special session and competition on large scale global optimization,” Nature Inspired Computat
- Tang, Yao, et al.
(Show Context)
Citation Context ... of the proposed approach we have used twelve widely known high dimensional benchmark functions with different characteristics. These function are from two recently proposed benchmark test sets [35], =-=[36]-=-. The first set of six test functions (F1 −F6) are high dimensional multimodal functions, where the number of local minima increases exponentially with their dimensionality [35]. The remaining six tes... |

26 | ch. Memetic Algorithms - Moscato, Cotta, et al. - 2004 |

24 | Fitness-distance-ratio based particle swarm optimization - Peram, Veeramachaneni, et al. |

24 | Parallel differential evolution - Tasoulis, Pavidis, et al. - 2004 |

23 | Cooperatively coevolving particle swarms for large scale optimization - Li, Yao - 2012 |

22 | Recent advances in differential evolution: A survey and experimental analysis - Neri, Tirronen - 2009 |

21 | Particle Swarm Optimization and Differential Evolution Algorithms - Das, Abraham, et al. - 2008 |

19 | Frankenstein’s PSO: A composite particle swarm optimization algorithm - Oca, A, et al. - 2009 |

18 |
Computational intelligence pc tools
- Eberhart, Simpson, et al.
- 1996
(Show Context)
Citation Context ... among members of the same species. Moreover, the behavior of the individuals of a flock corresponds to fundamental rules, such as nearest-neighbor velocity matching and acceleration by distance [2], =-=[3]-=-. The PSO algorithm is capable of handling non-differentiable, discontinuous and multimodal objective functions and has gained increasing popularity in recent years due to its ability to efficiently a... |

18 | The Lifecycle Model: Combining Particle Swarm Optimization, Genetic Algorithms and Hill-climbers - Krink, Løvbjerg |

18 |
A Combined Swarm Differential Evolution Algorithm for Optimization Problems
- Hendtlass
(Show Context)
Citation Context ...Intelligence Laboratory (CILab), Department of Mathematics, University of Patras, Greece. scheme [13]–[17], apply one of the two algorithms as local search to evolve some pre-specified particles [18]–=-=[20]-=-, or evolve the control parameters with one of the evolutionary approaches to produce a parameter-free hybrid [13], [21]– [23]. The current study has been motivated by the behavior and the proximity c... |

17 |
Empirical analysis of selfadaptive differential evolution
- Salman, Engelbrecht, et al.
- 2007
(Show Context)
Citation Context ... the two algorithms as local search to evolve some pre-specified particles [18]–[20], or evolve the control parameters with one of the evolutionary approaches to produce a parameter-free hybrid [13], =-=[21]-=-– [23]. The current study has been motivated by the behavior and the proximity characteristics of the personal experience of each particle during the evolution process. Each particle interacts with th... |

17 | Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima, in - Tasoulis, Plagianakos, et al. |

16 | Adaptive encoding: How to render search coordinate system invariant - Hansen - 2008 |

14 | A particle swarm optimization algorithm with differential evolution - Hao, Guo, et al. - 2007 |

14 |
Hybrid particle swarm with differential evolution for multimodal image registration
- Talbi, Batouche
(Show Context)
Citation Context ... of best personal positions [25]. Other variants incorporate update schemes that utilize information of the best personal positions by means of an average of two or more best personal positions [19], =-=[34]-=-. Fig. 1. local PSO population’s positions after 1, 5, 10, and 20 generations Fig. 2. local PSO population’s best personal positions after 1, 5, 10, and 20 generations The aforementioned approaches an... |

14 | Scale factor local search in differential evolution, Memetic Computing 1 (2 - Neri, Tirronen - 2009 |

14 | Parameter selection and adaptation in unified particle swarm optimization - Parsopoulos, Vrahatis |

12 | Self-adaptive learning based particle swarm optimization - Wang, Li, et al. - 2011 |

11 |
A novel hybrid differential evolution and particle swarm optmization algorithm for unconstrained optimization
- Zhang, Ning, et al.
- 2009
(Show Context)
Citation Context ... University of Central Greece, Greece. email: vpp@ucg.gr All authors are members of Computational Intelligence Laboratory (CILab), Department of Mathematics, University of Patras, Greece. scheme [13]–=-=[17]-=-, apply one of the two algorithms as local search to evolve some pre-specified particles [18]–[20], or evolve the control parameters with one of the evolutionary approaches to produce a parameter-free... |

11 | Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization - Liu, Cai, et al. |

11 | Particle swarm optimization: hybridization perspectives and experimental illustrations - Thangaraj, Pant, et al. |

10 |
Advances in Differential Evolution
- Chakraborty
- 2008
(Show Context)
Citation Context ...hms (EAs). The DE method requires few control parameters and several experimental studies have shown that DE has good convergence properties and outperforms other well known and widely used EAs [12], =-=[28]-=-–[30]. More specifically, DE is a population–based stochastic algorithm that exploits a population of potential solutions, individuals, to effectively probe the search space. Like PSO, the population ... |

10 | Differential evolution with local neighbourhood - Chakraborthy, Das, et al. - 2006 |

10 | Improving particle swarm optimization with differentially perturbed velocity - Das, Konar, et al. |

10 | Particle swarm optimization and intelligence - Parsopoulos, Vrahatis - 2010 |

9 | Balancing the exploration and exploitation capabilities of the differential evolution algorithm - Epitropakis, Plagianakos, et al. - 2008 |

9 | Improving the performance and scalability of differential evolution - Iorio, Li - 2008 |

8 |
Differential evolution based particle swarm optimization
- Omran, Engelbrecht, et al.
- 2007
(Show Context)
Citation Context ...tics, University of Central Greece, Greece. email: vpp@ucg.gr All authors are members of Computational Intelligence Laboratory (CILab), Department of Mathematics, University of Patras, Greece. scheme =-=[13]-=-–[17], apply one of the two algorithms as local search to evolve some pre-specified particles [18]–[20], or evolve the control parameters with one of the evolutionary approaches to produce a parameter... |

8 | Self-Adaptive Differential Evolution - Omran, Salman, et al. - 2005 |

8 | An improved differential evolution algorithm with fitness-based adaptation of the control parameters - Ghosh, Das, et al. - 2011 |

8 | Memetic evolutionary algorithms - Hart, Krasnogor, et al. |

7 | Finding multiple global optima exploiting differential evolutions niching capability - Epitropakis, Plagianakos, et al. - 2011 |

7 | Impacts of invariance in search: when CMA-ES and PSO face ill-conditioned problems - Hansen, Ros, et al. |

7 | An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical - Islam, Das, et al. - 2012 |

6 | Enhancing differential evolution utilizing proximity-based mutation operators - Epitropakis, Tasoulis, et al. - 2011 |

6 | Exposing origin-seeking bias in pso - Monson, Seppi - 2005 |

6 | Biases in particle swarm optimization - Spears, Green, et al. - 2010 |

5 | Inter-particle communication and searchdynamics of lbest particle swarm optimizers: An analysis - Ghosh, Das, et al. |

5 | Heterogeneous particle swarm optimizers - Oca, Peña, et al. - 2009 |

5 | Disturbed Exploitation compact Differential Evolution for Limited Memory Optimization Problems - Neri, Iacca, et al. - 2011 |

5 | Human designed vs. genetically programmed differential evolution operators, in - Pavlidis, Tasoulis, et al. |

4 | Hybrid differential evolution: Particle swarm optimization algorithm for solving global optimization problems - Pant, Thangaraj, et al. - 2008 |

4 | Ockhams razor in memetic computing: Three stage optimal memetic exploration - Iacca, Neri, et al. |

4 | DE-PSO: a new hybrid metaheuristic for solving global optimization problems,”NewMathematics - Pant, Thangaraj - 2011 |

3 | Memetic computation— past, present & future [research frontier - Ong, Lim, et al. - 2010 |

3 | Differential evolution and non-separability: using selective pressure to focus search - Sutton, Lunacek, et al. - 2007 |

2 | Standard particle swarm optimization, from 2006 to 2011,” Particle Swarm Central - Clerc - 2011 |

2 | An analysis of heterogeneous cooperative algorithms, in - Olorunda, Engelbrecht |

2 | A study on scale factor in distributed differential evolution - Weber, Neri, et al. - 2011 |

1 |
Recent approaches to global 5. Empirical cumulative probability distribution of the best fitness values of the original PSO versions against the corresponding Hybrid PSO variants over all 500–dimensional benchmark functions. optimization problems through
- Parsopoulos, Vrahatis
- 2002
(Show Context)
Citation Context ...several real-world applications [4], [5]. To improve the performance and the convergence behavior of Particle Swarm Optimization algorithm, several variations and hybrid approaches have been proposed =-=[6]-=-–[11]. One class of variations include hybrids that combine the PSO and the Differential Evolution (DE) algorithms [12]. These approaches aim to aggregate the advantages of both methods to efficiently... |

1 |
A unified particle swarm optimization scheme
- “UPSO
(Show Context)
Citation Context ...ff(vi g+1 ) < f(xig ) otherwise . x i g , IV. STUDYING THE COGNITIVE AND SOCIAL EXPERIENCE Numerous PSO variations have been proposed to improve the accuracy of solutions and PSO convergence behavior =-=[7]-=-, [25], [32]. In [11], [26] has been formally proven that each particle converges to a weighted average of its personal best and neighborhood best positions. Motivated by this finding new variants hav... |

1 | Vrahatis, Evolutionary adaptation of the differential evolution control parameters, in
- Epitropakis, Plagianakos, et al.
(Show Context)
Citation Context ...wo algorithms as local search to evolve some pre-specified particles [18]–[20], or evolve the control parameters with one of the evolutionary approaches to produce a parameter-free hybrid [13], [21]– =-=[23]-=-. The current study has been motivated by the behavior and the proximity characteristics of the personal experience of each particle during the evolution process. Each particle interacts with the rest... |

1 | Tracking particle swarm optimizers: an adaptive approach through multinomial distribution tracking with exponential forgetting, in - Epitropakis, Tasoulis, et al. |

1 | Towards robust memetic algorithms, in - Krasnogor - 2005 |

1 | Eliminating drift bias from the differential evolution algorithm, in: U. Chakraborty (Ed - Price - 2008 |

1 | de Oca, Experiments on adaptive heterogeneous pso algorithms - Spanevello, Montes - 2009 |