An improved memetic algorithm using ring neighborhood topology for constrained optimization

Hu, Zhenzhou; Cai, Xinye; Fan, Zhun

doi:10.1007/s00500-013-1183-7

Cited by 19 publications

(8 citation statements)

References 46 publications

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“…To balance the exploration and exploitation, an adaptive memetic DE was proposed (Piotrowski 2013), where the ring topology was used to construct the neighborhood of individuals for local model. In Hu et al (2014), the ring neighborhood topology was employed to improve a memetic DE algorithm. Recently, several population topologies, e.g.…”

Section: Related Workmentioning

confidence: 99%

Cellular direction information based differential evolution for numerical optimization: an empirical study

et al. 2015

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Differential evolution (DE) is a well-known evolutionary algorithm which has been successfully applied in many scientific and engineering fields. In most DE algorithms, the base and difference vectors for mutation are randomly selected from the current population. That is, the useful population information cannot be fully exploited to guide the search of DE through mutation. Furthermore, the selection of parents in mutation has been verified to be critical for the DE performance. Therefore, to alleviate this drawback and improve the performance of DE, a novel DE algorithm with a directional mutation based on cellular topology is proposed in this study. This proposed algorithm is named as Cellular Direction Information based DE (DE-CDI). In DE-CDI, the cellular topology is employed first to define a neighborhood for each individual of population and then the direction information based on the neighborhood is incorporated into the mutation operator. In this way, DE-CDI not only utilizes the neighborhood information to exploit the regions of better individuals and accelerate convergence, but also introduces the direction information to guide the search to the promising area. To evaluate the performance of the proposed method, DE-CDI is applied to the original DE algorithms, as well as several advanced DE variants. Experimental results demonstrate the high performance of DE-CDI for most DE algorithms studied.

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Section: Related Workmentioning

confidence: 99%

Cellular direction information based differential evolution for numerical optimization: an empirical study

et al. 2015

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“…A weak trade-off between exploration and exploitation and limitation of population diversity are two major challenges in PSO [14] and work has been done to address them in terms of parameter setting, neighborhood topology, learning approaches, and hybridized methods [15]. For example, some works tried to fine tune and regulate the parameters through memory adaptation [16], [17], Gaussian adaptation [18], or fuzzy-based methods [19], while other works attempted to avoid premature convergence by utilizing a neighborhood strategy like fully informed [20], self-adaptive [21] or ring topology [22], or a combination strategy through Lévy distribution such as LFPSO [23] and PSOLF [24]. The No Free Lunch (NFL) theorem asserts that no optimization methods can defeat all optimizers in solving all problems [25], [26], which motivated us to further extend PSO in order to better avoid local minimum and create a more balanced trade-off between exploration and exploitation in training MLP ANNs.…”

Section: Introductionmentioning

confidence: 99%

Training of feedforward neural networks for data classification using hybrid particle swarm optimization, Mantegna Lévy flight and neighborhood search

Tarkhaneh

Shen

2019

Heliyon

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Artificial Neural networks (ANNs) are often applied to data classification problems. However, training ANNs remains a challenging task due to the large and high dimensional nature of search space particularly in the process of fine-tuning the best set of control parameters in terms of weight and bias. Evolutionary algorithms are proved to be a reliable optimization method for training the parameters. While a number of conventional training algorithms have been proposed and applied to various applications, most of them share the common disadvantages of local optima stagnation and slow convergence. In this paper, we propose a new evolutionary training algorithm referred to as LPSONS, which combines the velocity operators in Particle Swarm Optimization (PSO) with Mantegna Lévy distribution to produce more diverse solutions by dividing the population and generation between different sections of the algorithm. It further combines Neighborhood Search with Mantegna Lévy distribution to mitigate premature convergence and avoid local minima. The proposed algorithm can find optimal results and at the same time avoid stagnation in local optimum solutions as well as prevent premature convergence in training Feedforward Multi-Layer Perceptron (MLP) ANNs. Experiments with fourteen standard datasets from UCI machine learning repository confirm that the LPSONS algorithm significantly outperforms a gradient-based approach as well as some well-known evolutionary algorithms that are also based on enhancing PSO.

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“…To solve CMOPs, constraint-handling mechanisms are important. In recent years, many different constraint-handling mechanisms have been proposed [15,16]. According to [17], constraint-handling techniques can be generally classified into (1) penalty functions; (2) special representations and operators; (3) repair algorithms; (4) separate objectives and constraints; and (5) hybrid methods.…”

Section: Introductionmentioning

confidence: 99%

MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems

Fan

Fang

et al. 2019

Applied Soft Computing

Self Cite

124

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This paper proposes a novel constraint-handling mechanism named angle-based constrained dominance principle (ACDP) embedded in a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multiobjective optimization problems (CMOPs). To maintain the diversity of the working population, ACDP utilizes the information of the angle of solutions to adjust the dominance relation of solutions during the evolutionary process. This paper uses 14 benchmark instances to evaluate the performance of the MOEA/D with ACDP (MOEA/D-ACDP). Additionally, an engineering optimization problem (which is I-beam optimization problem) is optimized. The proposed MOEA/D-ACDP, and four other decomposition-based CMOEAs, including C-MOEA/D, MOEA/D-CDP, MOEA/D-Epsilon and MOEA/D-SR are tested by the above benchmarks and the engineering application. The experimental results manifest that MOEA/D-ACDP is significantly better than the other four CMOEAs on these test instances and the real-world case, which indicates that ACDP is more effective for solving CMOPs.

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An improved memetic algorithm using ring neighborhood topology for constrained optimization

Cited by 19 publications

References 46 publications

Cellular direction information based differential evolution for numerical optimization: an empirical study

Cellular direction information based differential evolution for numerical optimization: an empirical study

Training of feedforward neural networks for data classification using hybrid particle swarm optimization, Mantegna Lévy flight and neighborhood search

MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems

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