Differential-Evolution-Based Coevolution Ant Colony Optimization Algorithm for Bayesian Network Structure Learning

Zhang, Xiangyin; Xue, Yuying; Lu, Xingyang; Jia, Songmin

doi:10.3390/a11110188

Cited by 12 publications

(7 citation statements)

References 26 publications

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“…It is well known that DE performs better than other popular evolutionary algorithms [24], has a quick convergence, and is robust [25]; it also performs better for learning applications [26]. At the same time, DE has simple genetic operations, such as its operator of the mutation and survival strategy based on one-on-one competition.…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution for Neural Networks Optimization

et al. 2020

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In this paper, a Neural Networks optimizer based on Self-adaptive Differential Evolution is presented. This optimizer applies mutation and crossover operators in a new way, taking into account the structure of the network according to a per layer strategy. Moreover, a new crossover called interm is proposed, and a new self-adaptive version of DE called MAB-ShaDE is suggested to reduce the number of parameters. The framework has been tested on some well-known classification problems and a comparative study on the various combinations of self-adaptive methods, mutation, and crossover operators available in literature is performed. Experimental results show that DENN reaches good performances in terms of accuracy, better than or at least comparable with those obtained by backpropagation.

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Section: Introductionmentioning

confidence: 99%

Differential Evolution for Neural Networks Optimization

et al. 2020

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“…In the past decades, researchers have proposed different heuristic approaches to learning bayesian network structure, including (but not limited to) particle swarm optimization (PSO) [9,[19][20][21][22][23][24], genetic algorithm [25], simulated annealing [26], artificial bee colony [27], artificial ant colony [28], pigeon inspired optimization [29], firefly algorithm [30]. Among all these approaches, PSO has been widely applied for the reason that it is simpler to code (and parallelize), has fast convergence speed, and has better global search ability [9].…”

Section: Introductionmentioning

confidence: 99%

BN-GEPSO: Learning Bayesian Network Structure Using Generalized Particle Swarm Optimization

Salman¹,

Almanjahie²,

Yasin³

et al. 2023

Computers, Materials &Amp; Continua

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At present Bayesian Networks (BN) are being used widely for demonstrating uncertain knowledge in many disciplines, including biology, computer science, risk analysis, service quality analysis, and business. But they suffer from the problem that when the nodes and edges increase, the structure learning difficulty increases and algorithms become inefficient. To solve this problem, heuristic optimization algorithms are used, which tend to find a near-optimal answer rather than an exact one, with particle swarm optimization (PSO) being one of them. PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds (how they search for food). PSO is employed widely because it is easier to code, converges quickly, and can be parallelized easily. We use a recently proposed version of PSO called generalized particle swarm optimization (GEPSO) to learn bayesian network structure. We construct an initial directed acyclic graph (DAG) by using the max-min parent's children (MMPC) algorithm and cross relative average entropy. This DAG is used to create a population for the GEPSO optimization procedure. Moreover, we propose a velocity update procedure to increase the efficiency of the algorithmic search process. Results of the experiments show that as the complexity of the dataset increases, our algorithm Bayesian network generalized particle swarm optimization (BN-GEPSO) outperforms the PSO algorithm in terms of the Bayesian information criterion (BIC) score.

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“…At present, some scholars have proposed the use of ant colony algorithm [11], genetic algorithm [12], artificial bee colony algorithm [13], simulated annealing algorithm [14], PSO [15] and other algorithms for BN structure learning. PSO algorithm has been widely used in the learning process of BN structure because of its simple coding, strong global search ability and fast convergence speed.…”

Section: Introductionmentioning

confidence: 99%

An Improved Particle Swarm Optimization Algorithm for Bayesian Network Structure Learning via Local Information Constraint

et al. 2021

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At present, in the application of Bayesian network (BN) structure learning algorithm for structure learning, the network scale increases with the increase of number of nodes, resulting in a large scale of structure search space, which is difficult to calculate, and the existing learning algorithms are inefficient, making BN structure learning difficulty increase. To solve this problem, a BN structure optimization method based on local information is proposed. Firstly, it proposes to construct an initial network framework with local information and uses the Max-Min Parents and Children (MMPC) algorithm to construct an undirected network framework to reduce the search space. Then the particle swarm optimization (PSO) algorithm is used to strengthen the algorithm's optimization ability by constructing a new position and velocity update rule and improve the efficiency of the algorithm. Experimental results show that under the same sample data set, the algorithm can obtain a more accurate BN structure while converging quickly, which verifies the correctness and effectiveness of the algorithm.

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Differential-Evolution-Based Coevolution Ant Colony Optimization Algorithm for Bayesian Network Structure Learning

Cited by 12 publications

References 26 publications

Differential Evolution for Neural Networks Optimization

Differential Evolution for Neural Networks Optimization

BN-GEPSO: Learning Bayesian Network Structure Using Generalized Particle Swarm Optimization

An Improved Particle Swarm Optimization Algorithm for Bayesian Network Structure Learning via Local Information Constraint

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