Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction

Song, Shuangbao; Ji, Junkai; Chen, Xingqian; Gao, Shangce; Tang, Zheng; Todo, Yuki

doi:10.1016/j.asoc.2018.07.042

Cited by 31 publications

(15 citation statements)

References 65 publications

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“…In addition, the slow convergence speed and the capability of being easily trapped in local minima are the main disadvantages of BP methods [24]. In contrast, the utility of a heuristic optimization method to solve real-world problems [47,48] has aroused the interest of researchers in recent years, including training ANNs [28,34]. The heuristic optimization algorithm called the competitive swarm optimizer (CSO) is also employed to train the LDNM in this study.…”

Section: Backpropagation Algorithmmentioning

confidence: 99%

A Neuron Model with Dendrite Morphology for Classification

et al. 2021

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Recent neurological studies have shown the importance of dendrites in neural computation. In this paper, a neuron model with dendrite morphology, called the logic dendritic neuron model (LDNM), is proposed for classification. This model consists of four layers: a synaptic layer, a dendritic layer, a membrane layer, and a soma body. After training, the LDNM is simplified by proprietary pruning mechanisms and is further transformed into a logic circuit classifier. Moreover, to address the high-dimensional challenge, feature selection is employed as the dimension reduction method before training the LDNM. In addition, the effort of employing a heuristic optimization algorithm as the learning method is also undertaken to speed up the convergence. Finally, the performance of the proposed model is assessed by five benchmark high-dimensional classification problems. In comparison with the other six classical classifiers, LDNM achieves the best classification performance on two (out of five) classification problems. The experimental results demonstrate the effectiveness of the proposed model. A new perspective for solving classification problems by the proposed LDNM is provided in the paper.

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Section: Backpropagation Algorithmmentioning

confidence: 99%

A Neuron Model with Dendrite Morphology for Classification

et al. 2021

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“…In [70], the PSP problem is modeled as a multi-objective optimization problem using Particle Swarm Optimization (MOPSO). The proposal is based on a full-atom torsion angle representation and CHARMM in a three-objective optimization.…”

Section: Other Evolutionary Approachesmentioning

confidence: 99%

Ab Initio Protein Structure Prediction Using Evolutionary Approach: A Survey

Siqueira¹,

Venske²

2021

RITA

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Protein Structure Prediction (PSP) problem is to determine the three-dimensional structure of a protein only from its primary structure. Misfolding of a protein causes human diseases. Thus, the knowledge of the structure and functionality of proteins, combined with the prediction of their structure is a complex problem and a challenge for the area of computational biology. The metaheuristic optimization algorithms are naturally applicable to support in solving NP-hard problems.These algorithms are bio-inspired, since they were designed based on procedures found in nature, such as the successful evolutionary behavior of natural systems. In this paper, we present a survey on methods to approach the \textit{ab initio} protein structure prediction based on evolutionary computing algorithms, considering both single and multi-objective optimization. An overview of the works is presented, with some details about which characteristics of the problem are considered, as well as specific points of the algorithms used. A comparison between the approaches is presented and some directions of the research field are pointed out.

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“…Based on the perspective of metaphors by which these meta-heuristics are motivated, MHA can be classified into bio-inspired, physics-inspired, sociology-inspired, and other algorithms [3]. Representative bio-inspired algorithms include genetic algorithms [4], evolutionary strategies [5], differential evolution (DE) [6]- [8], spherical evolution [9], artificial immune algorithms [10], particle swarm optimization (PSO) [11], ant colony optimization [12], etc. Physics-inspired algorithms consist of simulated annealing [13], gravitational search algorithm [14], and quantum computing [15], while sociology-inspired ones usually denote imperialist competitive algorithm [16], brain storm optimization [17], culture algorithm [18], memetic algorithms [19], and so on.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

Yang

et al. 2021

IEEE Access

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As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm's convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.

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Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction

Cited by 31 publications

References 65 publications

A Neuron Model with Dendrite Morphology for Classification

A Neuron Model with Dendrite Morphology for Classification

Ab Initio Protein Structure Prediction Using Evolutionary Approach: A Survey

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

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