Selection of appropriate metaheuristic algorithms for protein structure prediction in AB off-lattice model: a perspective from fitness landscape analysis

Jana, Nanda Dulal; Sil, Jaya; Das, Swagatam

doi:10.1016/j.ins.2017.01.020

Cited by 21 publications

(13 citation statements)

References 32 publications

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“…Theoretical properties unveiled by landscape analysis regarding the suitability of neighborhood operators are favorable to the inclusion of local search methods within the bio-inspired solvers. An example of this design strategy is the work in [59], where appropriate bio-inspired meta-heuristics were selected for the protein structure prediction problem based on fitness landscape analysis with random walks. However, such approaches are often problem-specific and cannot be generalized to solve other problems.…”

Section: Bio-inspired Computationmentioning

confidence: 99%

Bio-inspired computation: Where we stand and what's next

Ser

Osaba

Molina

et al. 2019

Swarm and Evolutionary Computation

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482

106

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Section: Bio-inspired Computationmentioning

confidence: 99%

Bio-inspired computation: Where we stand and what's next

Ser

Osaba

Molina

et al. 2019

Swarm and Evolutionary Computation

Self Cite

482

106

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show abstract

“…The authors in [16] determined the structural features of the PFO using Fitness Landscape Analysis (FLA) techniques based on the generated landscape path. From the results of FLA, it has been shown that the PFO has a highly rugged landscape structure containing many local optima and needle-like funnels, with no global structure that characterizes the PFO complexity.…”

Section: Related Workmentioning

confidence: 99%

“…It has been shown that the PFO has a highly rugged landscape structure containing many local optima and needle-like funnels [16], and, therefore, the algorithms that follow more attractors simultaneously are ineffective. In our recent work [4], to overcome this weakness, we proposed a Differential Evolution (DE) algorithm that uses the DE /best/1 /bin strategy.…”

Section: Introductionmentioning

confidence: 99%

Protein folding optimization using differential evolution extended with local search and component reinitialization

Bošković

Brest

2018

Information Sciences

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This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our algorithm and that the proposed algorithm is superior to other state-of-the-art algorithms. It obtained a hit ratio of 100% for sequences up to 18 monomers, within a budget of 10 11 solution evaluations. New best-known solutions were obtained for most of the sequences. The existence of the symmetric best-known solutions is also demonstrated in the paper.

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“…It has been shown that the PFO has a highly rugged landscape structure containing many local optima and needle-like funnels [14], and, therefore, the algorithms that follow more attractors simultaneously are ineffective. In our previous work [2], to overcome this weakness, we proposed the DE algorithm that uses the best/1/bin strategy.…”

Section: Metaheuristic Optimization Algorithmsmentioning

confidence: 99%

“…It has been shown that PFO has a highly rugged landscape structure, containing many local optima and needle-like funnels [14]. In order to explore this search space effectively, we already have proposed a Differential Evolution (DE) algorithm [2,4] that, in contrast to all previous methods, follows only one attractor.…”

Section: Introductionmentioning

confidence: 99%

Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model

Bošković

Brest

2016

J Mol Model

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This paper presents a differential evolution algorithm that is adapted for the protein folding optimization on a three-dimensional AB off-lattice model. The proposed algorithm is based on a self-adaptive differential evolution that improves the algorithm efficiency and reduces the number of control parameters. A mutation strategy for the fast convergence is used inside the algorithm. A temporal locality is used in order to speed up the algorithm convergence additionally and to find amino-acid conformations with the lowest free energy values. Within this mechanism a new vector is calculated when the trial vector is better than the corresponding vector from the population. This new vector is likely better than the trial vector and this accelerates convergence speed. Because of the fast convergence the algorithm has some chance to be trapped into the local optima. To mitigate this problem the algorithm includes reinitialization. The proposed algorithm was tested on amino-acid sequences that are used frequently in literature. The obtained results show that the proposed algorithm is superior to the algorithms from the literature and the obtained amino-acid sequences have significantly lower free energy values. Graphical Abstract Protein folding optimization on a three-dimensional AB off-lattice model using the differential evolution algorithm.

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Selection of appropriate metaheuristic algorithms for protein structure prediction in AB off-lattice model: a perspective from fitness landscape analysis

Cited by 21 publications

References 32 publications

Bio-inspired computation: Where we stand and what's next

Bio-inspired computation: Where we stand and what's next

Protein folding optimization using differential evolution extended with local search and component reinitialization

Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model

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