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首页> 外文期刊>Neural computing & applications >Exploiting flower constancy in flower pollination algorithm: improved biotic flower pollination algorithm and its experimental evaluation
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Exploiting flower constancy in flower pollination algorithm: improved biotic flower pollination algorithm and its experimental evaluation

机译：开发花授粉算法的花恒定：改进的生物花授粉算法及其实验评价

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摘要

Recent growth of metaheuristic search strategies has brought a huge progress in the domain of computational optimization. The breakthrough started since the well-known Particle Swarm Optimization algorithm had been introduced and examined. Optimization technique presented in this contribution mimics the process of flower pollination. It is build on the foundation of the first technique of this kind-known as Flower Pollination Algorithm (FPA). In this paper, its simplified and improved version, obtained after extensive performance testing, is presented. It is based on only one natural phenomena-called flower constancy-the natural mechanism allowing pollen carrying insects to remember the positions of the best pollen sources. Modified FPA, named as Biotic Flower Pollination Algorithm (BFPA) and relying solely on biotic pollinators, outperforms original FPA, which itself proved to be very effective approach. The paper first presents a short description of original FPA and the changes leading to Biotic Flower Pollination Algorithm. It also discusses performance of the modified algorithm on a full set of CEC17 benchmark functions. Furthermore, in that aspect, the comparison between BFPA and other optimization algorithms is also given. Finally, brief exemplary application of modified algorithm in the field of probabilistic modeling, related to physics and engineering, is also presented.
机译：最近成群质主义搜索策略的增长在计算优化领域带来了巨大进展。自从众所周知的粒子群优化算法引入和检查了自突破性。本贡献中呈现的优化技术模拟了花授粉的过程。它是基于这种被称为花授粉算法（FPA）的第一技术的基础。在本文中，提出了大量性能测试后获得的简化和改进的版本。它仅基于一个被称为花恒定的一种自然现象 - 允许花粉携带昆虫的自然机制来记住最好的花粉来源的位置。修改为生物花授粉算法（BFPA）并仅依赖于生物粉粉粉，优于原始FPA，这本身证明是非常有效的方法。本文首先介绍了原始FPA的简短描述和导致生物花卉授粉算法的变化。它还讨论了修改算法在全套CEC17基准函数上的性能。此外，在该方面，还给出了BFPA和其他优化算法之间的比较。最后，还介绍了改进算法在概率建模领域的简要示例性应用，与物理和工程相关。

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