Deterministic Parameter Selection of Artificial Bee Colony Based on Diagonalization
Deterministic Parameter Selection of Artificial Bee Colony Based on Diagonalization
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DOI：
10.1007/978-3-030-14347-3_9
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
M. A. F. Mollinetti;M. T. R. S. Neto;Takahito Kuno
中科院分区：
文献类型：
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作者：
M. A. F. Mollinetti;M. T. R. S. Neto;Takahito Kuno
人工蜂群（ABC）是一种蜜蜂启发的群体智能（SI）算法，以其通用性和简单性而闻名。在算法的关键步骤中，采用和侦察蜜蜂阶段，以随机方式选择参数（决策变量）。虽然这种随机性可能显然不会影响算法的整体性能，但它可能会导致过早收敛到糟糕的局部最优值，或者在具有崎岖表面的多峰问题中缺乏探索。在本研究中，一个确定性的选择方法的基础上康托的有理数的不可数性证明的决策变量，提出了上述步骤中使用。该方法旨在消除随机性，通过验证所有可能的变量来增强算法的探索能力，并提供一种更好的机制来取代局部最优解，为解决方案引入更多的新奇。为了分析所提出的方法对ABC的整体性能带来的潜在好处，设计了三个变体，在这项工作中讨论的修改，在效率和稳定性方面进行比较，对原始ABC的15个无约束优化问题的实例。
Artificial Bee Colony (ABC) is a bee inspired swarm intelligence (SI) algorithm well-known for its versatility and simplicity. In crucial steps of the algorithm, employed and scout bees phase, parameters (decision variables) are chosen in a random fashion. Although this randomness may apparently not influence the overall performance of the algorithm, it may contribute to premature convergence towards bad local optima or lack of exploration in multimodal problems featuring rugged surfaces. In this study, a deterministic selection method for decision variables based on Cantor’s proof of uncountability of rational numbers is proposed to be used in the aforementioned steps. The approach seeks to eliminate stochasticity, enhance the exploratory capabilities of the algorithm by verifying all possible variables, and provide a better mechanism to displace solutions out of local optima, introducing more novelty to solutions. In order to analyze potential benefits brought by the proposed approach to the overall performance of the ABC, three variants featuring modifications discussed in this work were designed to be compared in terms of efficiency and stability against the original ABC on 15 instances of unconstrained optimization problems.