Hybrid Differential Evolution - Particle Swarm Optimization Algorithm for Solving Global Optimization Problems

Pant, Millie; Thangaraj, Radha; Groşan, Crina; Abraham, Ajith

doi:10.1109/icdim.2008.4746766

Cited by 42 publications

(24 citation statements)

References 9 publications

(14 reference statements)

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“…DEPSO-DAK was applied to the design of beta basis function neural networks. In the DEPSO that is proposed by Pant et al (namely DEPSO-PTGA) [106], PSO will be activated to evolve an individual only if DE does not bring the improvement of its fitness.…”

Section: Previous Depsosmentioning

confidence: 99%

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

Xin

Chen

Zhang

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

163

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Abstract-Differential evolution (DE) and particle swarm optimization (PSO) are two formidable population-based optimizers (POs) that follow different philosophies and paradigms, which are successfully and widely applied in scientific and engineering research. The hybridization between DE and PSO represents a promising way to create more powerful optimizers, especially for specific problem solving. In the past decade, numerous hybrids of DE and PSO have emerged with diverse design ideas from many researchers. This paper attempts to comprehensively review the existing hybrids based on DE and PSO with the goal of collection of different ideas to build a systematic taxonomy of hybridization strategies. Taking into account five hybridization factors, i.e., the relationship between parent optimizers, hybridization level, operating order (OO), type of information transfer (TIT), and type of transferred information (TTI), we propose several classification mechanisms and a versatile taxonomy to differentiate and analyze various hybridization strategies. A large number of hybrids, which include the hybrids of DE and PSO and several other representative hybrids, are categorized according to the taxonomy. The taxonomy can be utilized not only as a tool to identify different hybridization strategies, but also as a reference to design hybrid optimizers. The tradeoff between exploration and exploitation regarding hybridization design is discussed and highlighted. Based on the taxonomy proposed, this paper also indicates several promising lines of research that are worthy of devotion in future.

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Section: Previous Depsosmentioning

confidence: 99%

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

Xin

Chen

Zhang

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

163

View full text Add to dashboard Cite

show abstract

“…The HPSDE method was reported to be able to perform an outstanding degree of accuracy when compared with the other two methods, namely DE and PSO algorithms, respectively. Another work reported from Pant and Thangaraj (2008) in tackling the feature dimension problem. In their work, a hybrid model of DE-PSO is proposed to prove the effectiveness in solving various reallife problems and other optimization problems.…”

Section: Related Workmentioning

confidence: 99%

“…Several well-known feature selection algorithms, such as particle swarm optimization (PSO) (Das et al, 2015;Prasad et al, 2015), the evolutionary algorithm (EA) (Arif & Kattan, 2015), the genetic algorithm (GA) (Ijjina & Mohan, 2014;Das et al, 2015;Prasad et al, 2015) and the Tabu search algorithm (Arif & Kattan, 2015) were utilized. Table 3 shows the performance comparison in term of accuracy and the overall time taken to build the training model.…”

mentioning

confidence: 99%

Recognizing Complex Human Activities using Hybrid Feature Selections based on an Accelerometer Sensor

Zainudin¹,

Sulaiman²,

Mustapha³

et al. 2017

IJTech

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Wearable sensor technology is evolving in parallel with the demand for human activity monitoring applications. According to World Health Organization (WHO), the percentage of health problems occurring in the world population, such as diabetes, heart problem, and high blood pressure rapidly increases from year-to-year. Hence, regular exercise, at least twice a week, is encouraged for everyone, especially for adults and the elderly. An accelerometer sensor is preferable, due to privacy concerns and the low cost of installation. It is embedded within smartphones to monitor the amount of physical activity performed. One of the limitations of the various classifications is to deal with the large dimension of the feature space. Practically speaking, a large amount of memory space is demanded along with high processor performance to process a large number of features. Hence, the dimension of the features is required to be minimized by selecting the most relevant feature before it is classified. In order to tackle this issue, the hybrid feature selection using Relief-f and differential evolution is proposed. The public domain activity dataset from Physical Activity for Ageing People (PAMAP2) is used in the experimentation to identify the quality of the proposed method. Our experimental results show outstanding performance to recognize different types of physical activities with a minimum number of features. Subsequently, our findings indicate that the wrist is the best sensor placement to recognize the different types of human activity. The performance of our work also been compared with several state-of-the-art of features for selection algorithms.

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“…However, in the case of differential evolution, that information is sampled randomly. DE-PSO is basically a differential evolution algorithm mixed with ideas of particle swarm optimization [18].…”

Section: Differentiaí Evoíution and Psomentioning

confidence: 99%

The optimal combination: Grammatical swarm, particle swarm optimization and neural networks

López

Blas

Arteta

2012

Journal of Computational Science

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Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuáis to get optimal solutions. Such co-operative model differs from competitive models in the way that individuáis die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.

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Hybrid Differential Evolution - Particle Swarm Optimization Algorithm for Solving Global Optimization Problems

Cited by 42 publications

References 9 publications

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

Recognizing Complex Human Activities using Hybrid Feature Selections based on an Accelerometer Sensor

The optimal combination: Grammatical swarm, particle swarm optimization and neural networks

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