Synergistic fibroblast optimization: a novel nature-inspired computing algorithm

查看全文 作　　者：T T [1]DHIVYAPRABHA;P [1]SUBASHINI;M [1]KRISHNAVENI

机构地区：[1]Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women

出　　处：《Frontiers of Information Technology & Electronic Engineering》索引2018年第19卷第7期,共19页

摘　　要：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. 关键词：成纤维细胞计算算法优化算法性质进化算法自然进化优化策略行为

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Synergistic fibroblast optimization: a novel nature-inspired computing algorithm

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