Particle Swarm Optimization Using Adaptive Mutation

Pant, M.; Thangaraj, Radha; Abraham, Alice

doi:10.1109/dexa.2008.70

Cited by 44 publications

(16 citation statements)

References 5 publications

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“…Pant [8] used an adaptive mutation operator in PSO, which was based on the idea of FEP. The particles are mutated at the end of each iteration according to the following rule:…”

Section: B Pso With Mutationsmentioning

confidence: 99%

“…When a particle has no change in a fixed number of generations, the particle should jump to a new point by adding a random number generated by Cauchy distribution. Pant [8] used an adaptive Cauchy mutation operator in PSO, which was based on the idea of FEP. Chen [9] presented a Gaussian mutation operator with adaptive mutation probability.…”

Section: Introductionmentioning

confidence: 99%

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Particle Swarm Optimization with Adaptive Mutation

Tang

Zhao

2009

2009 WASE International Conference on Information Engineering

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Particle swarm optimization (PSO) has shown its good performance in many optimization problems. However, PSO could often easily fall into local minima because the particles could quickly converge to a position by the attraction of the best particles. Under this circumstance, all the particles could hardly be improved. This paper presents a hybrid PSO (AMPSO) to solve this problem by applying a novel adaptive mutation operator. Experimental results on 8 well-known benchmark functions show that the AMPSO achieves better results than the standard PSO, PSO with Gaussian mutation and PSO with Cauchy mutation on most test cases. key words-Particle swarm optimization (PSO); mutation; function optimization.I. WASE International Conference on Information Engineering978-0-7695-3679-8/09 $25.00

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“…Pant [8] used an adaptive mutation operator in PSO, which was based on the idea of FEP. The particles are mutated at the end of each iteration according to the following rule:…”

Section: B Pso With Mutationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization with Adaptive Mutation

Tang

Zhao

2009

2009 WASE International Conference on Information Engineering

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“…There is evidence that has shown that PSO can outperform generic algorithm for difficult problem classes [6], [7], [8]. Many successful applications of the PSO algorithm have been reported for solving biological problems [9], [10], [11] and numerous improvements to the algorithm have been proposed [12], [13], [14], [15], [16], [17], [18], [19], [20]. Refer to [21], [22] for reviews of PSO.…”

Section: Introductionmentioning

confidence: 99%

A Multi-State Optimization Framework for Parameter Estimation in Biological Systems

2016

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Parameter estimation is a key concern for reliable and predictive models of biological systems. In this paper, we propose a multi-objective, multi-state optimization framework that allows multiple data sources to be incorporated into the parameter estimation process. This enables the model to better represent a diverse range of data from both within and outwith the training set; and to determine more biologically relevant parameter values for the model parameters. The framework is based on a multi-objective PSwarm implementation (MoPSwarm) and is validated via a case study on the ERK signalling pathway, in which significant advantages over the conventional single-state approach are demonstrated. Several variants of the framework are analyzed to determine the optimal configuration for convergence and solution quality.

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“…Subsequently they investigate several mutation operators (Cauchy, Gaussian and Levy mutation) based on the global best particles, an adaptive mutation operator is then formulated by integrating the three mutation operators described above together at different stages for the best optimization performance [8]. In the meanwhile, Pant et al [9] present two variants of PSO with adaptive mutation, in which both the personal best position and the global best swarm position are mutated by Beta distribution respectively. In addition, the opposition-based learning algorithm is introduced into PSO as so to accelerate the learning and searching process [10], in which the mutation threshold is automatically adapted in terms of the evolutionary information of the global best particles.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization with Chaotic Maps and Gaussian Mutation for Function Optimization

Tian¹

2015

IJGDC

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Particle swarm optimization (PSO) is a population-based stochastic optimization that has been widely applied to a variety of problems. However, it is easily trapped into the local optima and appears premature convergence during the search process. To address these problems, we propose a new particle swarm optimization by introducing chaotic maps (tent map and logistic map) and Gaussian mutation into the PSO algorithm. On the one hand, the chaotic map is employed to initialize uniform distributed particles so as to improve the quality of the initial population, which is a simple yet very efficient method to improve the quality of initial population. On the other hand, the Gaussian mutation mechanism based on the maximal focus distance is adopted to help the algorithm escape from the local optima and make the particles proceed with searching in other regions of the solution space until the global optimal or the closer-to-optimal solutions can be found. Experimental results on two benchmark functions demonstrate the effectiveness and efficiency of the PSO algorithm proposed in this paper.

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Particle Swarm Optimization Using Adaptive Mutation

Abstract: Abstract

Cited by 44 publications

References 5 publications

Particle Swarm Optimization with Adaptive Mutation

Particle Swarm Optimization with Adaptive Mutation

A Multi-State Optimization Framework for Parameter Estimation in Biological Systems

Particle Swarm Optimization with Chaotic Maps and Gaussian Mutation for Function Optimization

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