Particle swarm optimization algorithm with asymmetric time varying acceleration coefficients

Bao, Guangqing; Mao, Kai

doi:10.1109/robio.2009.5420504

Cited by 54 publications

(28 citation statements)

References 7 publications

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“…To fully investigate the performance of the CEDGO algorithm, we compare the CEDGO algorithm and the latest variant PSO algorithms, including autonomous particles group particle swarm optimization (AGPSO) [20], improved particle swarm optimization (IPSO) [21], time varying acceleration particle swarm optimization (TACPSO) [22] and modified particle swarm optimization (MPSO) [23]. Table 5 lists the results of the comparison.…”

Section: Comparison With the Latest Variant Pso Algorithmsmentioning

confidence: 99%

Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm

Tang¹,

Fong²,

Dey

et al. 2017

Entropy

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Abstract:Recently, a new algorithm named dynamic group optimization (DGO) has been proposed, which lends itself strongly to exploration and exploitation. Although DGO has demonstrated its efficacy in comparison to other classical optimization algorithms, DGO has two computational drawbacks. The first one is related to the two mutation operators of DGO, where they may decrease the diversity of the population, limiting the search ability. The second one is the homogeneity of the updated population information which is selected only from the companions in the same group. It may result in premature convergence and deteriorate the mutation operators. In order to deal with these two problems in this paper, a new hybridized algorithm is proposed, which combines the dynamic group optimization algorithm with the cross entropy method. The cross entropy method takes advantage of sampling the problem space by generating candidate solutions using the distribution, then it updates the distribution based on the better candidate solution discovered. The cross entropy operator does not only enlarge the promising search area, but it also guarantees that the new solution is taken from all the surrounding useful information into consideration. The proposed algorithm is tested on 23 up-to-date benchmark functions; the experimental results verify that the proposed algorithm over the other contemporary population-based swarming algorithms is more effective and efficient.

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Section: Comparison With the Latest Variant Pso Algorithmsmentioning

confidence: 99%

Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm

Tang¹,

Fong²,

Dey

et al. 2017

Entropy

View full text Add to dashboard Cite

show abstract

“…As a result, several researchers were attracted towards its tuning [56,73,22,70]. Further, researchers also focused on tuning other parameters [53,2,20,74]. A complete survey on different parameter selection schemes for PSO is available in [24].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems

Tanweer

Sundaram

Sundararajan

2016

Information Sciences

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“…In 2009, Ziyu and Dingxue [22] introduced an exponentially time-varying acceleration function for adjusting both cognitive and social coefficients in order to control the global search ability and convergence to the global best solution. In 2009, Bao and Mao suggested an asymmetric time-varying acceleration coefficient adjustment strategy [23]. They tried to utilize this strategy to balance local search and global search.…”

Section: Related Workmentioning

confidence: 99%

“…Using dynamic parameter tuning is a method that increases the performance of PSO without suffering from high computational cost [19][20][21][22][23][24]. The main parameters of PSO are the weighting factor (w), cognitive coefficient (c1) and social coefficient (c 2 ).…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Particles Groups for Particle Swarm Optimization

2014

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In this paper a modified Particle Swarm Optimization (PSO) algorithm called Autonomous Groups Particles Swarm Optimization (AGPSO) is proposed to further alleviate the two problems of trapping in local minima and slow convergence rate in solving high dimensional problems. The main idea of AGPSO algorithm is inspired by individuals' diversity in bird flocking or insect swarming. In natural colonies, individuals are not basically quite similar in terms of intelligence and ability, but they all do their duties as members of a colony. Each individual's ability can be useful in a particular situation. In this paper a mathematical model of diverse particles groups called autonomous groups is proposed. In other words different functions with diverse slopes, curvatures, and interception points are employed to tune the social and cognitive parameters of the PSO algorithm to give particles different behaviors as in natural colonies. The results show that PSO with autonomous groups of particles outperforms the conventional and some recent modifications of PSO in terms of escaping local minima and convergence speed. The results also indicate that dividing particles in groups and allowing them to have different individual and social thinking can improve the performance of PSO significantly.

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Particle swarm optimization algorithm with asymmetric time varying acceleration coefficients

Cited by 54 publications

References 7 publications

Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm

Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm

Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems

Autonomous Particles Groups for Particle Swarm Optimization

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