Improved particle swarm optimization algorithm using design of experiment and data mining techniques

Liu, Zhao; Zhu, Ping; Chen, Wei; Yang, Ren‐Jye

doi:10.1007/s00158-015-1271-7

Cited by 23 publications

(6 citation statements)

References 27 publications

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“…Similarly, the improved swarm optimized functional link artificial neural network (ISO-FLANN) was proposed by Dehuri in [ 30 ] using random number initialization following uniform distribution. Optimal Latin Hypercube Design (OLHD) initialization approach was proposed by the authors in [ 31 ] and evaluated on several data mining problems with the other quasirandom sequences, such as Faure, Halton, and Sobol sequences. The proposed OLHD was better than quasirandom sequences in terms of efficiency measures.…”

Section: Related Workmentioning

confidence: 99%

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

Bangyal

Hameed

Alosaimi

et al. 2021

Computational Intelligence and Neuroscience

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Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique used to search for food with the intrinsic manner of bee swarming. PSO is widely used to solve the diverse problems of optimization. Initialization of population is a critical factor in the PSO algorithm, which considerably influences the diversity and convergence during the process of PSO. Quasirandom sequences are useful for initializing the population to improve the diversity and convergence, rather than applying the random distribution for initialization. The performance of PSO is expanded in this paper to make it appropriate for the optimization problem by introducing a new initialization technique named WELL with the help of low-discrepancy sequence. To solve the optimization problems in large-dimensional search spaces, the proposed solution is termed as WE-PSO. The suggested solution has been verified on fifteen well-known unimodal and multimodal benchmark test problems extensively used in the literature, Moreover, the performance of WE-PSO is compared with the standard PSO and two other initialization approaches Sobol-based PSO (SO-PSO) and Halton-based PSO (H-PSO). The findings indicate that WE-PSO is better than the standard multimodal problem-solving techniques. The results validate the efficacy and effectiveness of our approach. In comparison, the proposed approach is used for artificial neural network (ANN) learning and contrasted to the standard backpropagation algorithm, standard PSO, H-PSO, and SO-PSO, respectively. The results of our technique has a higher accuracy score and outperforms traditional methods. Also, the outcome of our work presents an insight on how the proposed initialization technique has a high effect on the quality of cost function, integration, and diversity aspects.

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Section: Related Workmentioning

confidence: 99%

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

Bangyal

Hameed

Alosaimi

et al. 2021

Computational Intelligence and Neuroscience

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“…Similarly for the classification EMG signals Subasi [46] used Simple random number generator in PSO hybridized with support vector machines. Optimal Latin Hypercube Design OLHD based initialization method for population initialization is introduced by Zhao Liu [47] and analysed it with quasirandom sequence, Faure, Halton and Sobol on various reallife data mining problems. The above presented study is an evidence to reveal that selection of right starting configuration performs important job in terms of reinforce the swarm diversity and convergence rate.…”

Section: Related Workmentioning

confidence: 99%

Particle Swarm Optimization With Probability Sequence for Global Optimization

et al. 2020

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Particle Swarm Optimization (PSO) has been frequently employed to solve diversified optimization problems. Choosing initial placement for population plays an important role in meta-heuristic methods since they can significantly converge. In this study, probability distribution has been introduced to enhance the diversity of swarm and convergence speed. Population initialization method based on uniform distribution is normally used when there is no preceding knowledge available regarding the candidate solution. In this paper, a new approach to initialize population is proposed using probability sequence Weibull marked as (WI-PSO) that applies the probability distribution to generate numbers at random locations for swarm initialization. The proposed method (WI-PSO) is tested on sixteen well-known unimodal and multi-modal benchmark test functions broadly adopted by the research community and its encouraging performance is investigated and compared with the Exponential distribution based PSO (E-PSO), Beta distribution based PSO (BT-PSO), Gamma distribution based PSO (GA-PSO) and Log-normal distribution based PSO (LN-PSO). Artificial Neural Networks (ANNs) have become the most powerful tool for classification of complex benchmark problems. We have experimented the proposed method (WI-PSO) for weight optimization of a feed-forward neural network to ensure its purity and have compared with conventional back-propagation algorithm (BPA), E-PSO, BT-PSO, GA-PSO and LN-PSO. Due to flexible behaviour in the degree of freedom, the experimental results infer the perfection and dominance of the Weibull based population initialization. The result exhibits the anticipation of influence exerted by the proposed technique on all sixteen objective functions and eight real-world benchmark data sets.

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“…In order to compare the proposed HS-based OED with other approaches, various optimization are compared in the inner iteration loop, where the fitness function denote estimation error. Heuristic optimization approaches including particle swarm optimization(PSO) and genetic algorithm(GA) are able to perform the task of optimal experimental design with suitable fitness functions [41, 42]. Under the framework of deterministic modeling, GA and PSO algorithms have been applied to minimize the fitness function value, thus estimating optimal parameter vectors.…”

Section: Experimental Outcomes and Analysismentioning

confidence: 99%

Optimal parameter identification of synthetic gene networks using harmony search algorithm

Zhang

et al. 2019

PLoS ONE

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Computational modeling of engineered gene circuits is an important while challenged task in systems biology. In order to describe and predict the response behaviors of genetic circuits using reliable model parameters, this paper applies an optimal experimental design(OED) method to obtain input signals. In order to obtain informative observations, this study focuses on maximizing Fisher information matrix(FIM)-based optimal criteria and to provide optimal inputs. Furthermore, this paper designs a two-stage optimization with the modified E-optimal criteria and applies harmony search(HS)-based OED algorithm to minimize estimation errors. The proposed optimal identification methodology involves estimation errors and the sample size to pursue a trade-off between estimation accuracy and measurement cost in modeling gene networks. The designed cost function takes two major factors into account, in which experimental costs are proportional to the number of time points. Experiments select two types of synthetic genetic networks to validate the effectiveness of the proposed HS-OED approach. Identification outcomes and analysis indicate the proposed HS-OED method outperforms two candidate OED approaches, with reduced computational effort.

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Improved particle swarm optimization algorithm using design of experiment and data mining techniques

Cited by 23 publications

References 27 publications

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

Particle Swarm Optimization With Probability Sequence for Global Optimization

Optimal parameter identification of synthetic gene networks using harmony search algorithm

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