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CLC number: TP18; R329.2

On-line Access: 2018-09-04

Received: 2016-09-13

Revision Accepted: 2017-03-15

Crosschecked: 2018-06-15

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

T T Dhivyaprabha

http://orcid.org/0000-0002-8603-6826

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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.7 P.815-833

http://doi.org/10.1631/FITEE.1601553


Synergistic fibroblast optimization: a novel nature-inspired computing algorithm


Author(s):  T T Dhivyaprabha, P Subashini, M Krishnaveni

Affiliation(s):  Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore 641043, India

Corresponding email(s):   ttdhivyaprabha@gmail.com, mail.p.subashini@gmail.com, krishnaveni.rd@gmail.com

Key Words:  Synergistic fibroblast optimization (SFO), Fitness analysis, Convergence, Benchmark suite, Monk’s dataset


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T T Dhivyaprabha, P Subashini, M Krishnaveni. Synergistic fibroblast optimization: a novel nature-inspired computing algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(7): 815-833.

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Abstract: 
The evolutionary algorithm, a subset of computational intelligence techniques, is a generic population-based stochastic optimization algorithm which uses a mechanism motivated by biological concepts. Bio-inspired computing can implement successful optimization methods and adaptation approaches, which are inspired by the natural evolution and collective behavior observed in species, respectively. Although all the meta-heuristic algorithms have different inspirational sources, their objective is to find the optimum (minimum or maximum), which is problem-specific. We propose and evaluate a novel synergistic fibroblast optimization (SFO) algorithm, which exhibits the behavior of a fibroblast cellular organism in the dermal wound-healing process. Various characteristics of benchmark suites are applied to validate the robustness, reliability, generalization, and comprehensibility of SFO in diverse and complex situations. The encouraging results suggest that the collaborative and self-adaptive behaviors of fibroblasts have intellectually found the optimum solution with several different features that can improve the effectiveness of optimization strategies for solving non-linear complicated problems.

协同纤维母细胞优化

概要:作为计算智能技术的一个分支,进化算法是一种由生物学概念启发而来的基于种群的随机优化通用算法。通过生物启发式计算,可实现源于自然进化的优化方法和源于集体行为的适应方法。尽管元启发算法的灵感来源各不相同,它们的目标都是在特定问题中寻找最优解(最大或最小)。提出一种新的协同纤维母细胞优化(synergistic fibroblast optimization, SFO)算法并对其评估。该算法模拟了成纤维细胞生物在皮肤伤口愈合过程中的行为。采用基准套件的各种特性,在多种复杂情形下验证了SFO算法的鲁棒性、可靠性、普适性和可理解性,得到了令人鼓舞的结果。结果表明,凭借纤维母细胞的协作和自适应行为,SFO智能地找到了多个不同特征的最优解,从而提高了非线性复杂问题中优化策略的有效性。

关键词:协同纤维母细胞优化;适应性分析;收敛;基准套件;僧侣问题数据集

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