A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning
A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning
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DOI：
10.1109/tsmcb.2012.2222373
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发表时间：
2013-06-01
影响因子：
11.8
通讯作者：
Huang, Ling-ling
中科院分区：
文献类型：
--
作者：
Gao, Wei-feng;Liu, San-yang;Huang, Ling-ling
人工蜂群（ABC）算法是一种较新的优化技术，与其他基于群体的算法相比具有一定的竞争力。然而，ABC在解搜索方程方面存在不足，擅长探索，不擅长开发。为了解决这一问题，我们首先提出了一种改进的ABC方法，称为CABC，其中使用修改的搜索方程来生成候选解，以提高ABC的搜索能力。在此基础上，利用正交实验设计（OED）对不同的abc形成正交学习（OL）策略，从搜索经验中发现更多有用的信息。由于OED的良好特点是采样少量具有代表性的井组合进行测试，因此OL策略可以构建更有希望和有效的候选解决方案。本文将OL策略应用于三个版本的ABC，即标准ABC、全局最佳引导ABC （GABC）和CABC，后者分别产生OABC、OGABC和OCABC。在22个基准函数上的实验结果证明了改进的搜索方程和OL策略的有效性和高效性。与其他ABC算法和几种最新算法的比较表明，本文提出的算法显著提高了ABC的性能。此外，OCABC在几乎所有测试功能中提供了最高的解决方案质量，最快的全局收敛性和最强的鲁棒性。
The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, ABC has an insufficiency regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose an improved ABC method called as CABC where a modified search equation is applied to generate a candidate solution to improve the search ability of ABC. Furthermore, we use the orthogonal experimental design (OED) to form an orthogonal learning (OL) strategy for variant ABCs to discover more useful information from the search experiences. Owing to OED's good character of sampling a small number of well representative combinations for testing, the OL strategy can construct a more promising and efficient candidate solution. In this paper, the OL strategy is applied to three versions of ABC, i.e., the standard ABC, global-best-guided ABC (GABC), and CABC, which yields OABC, OGABC, and OCABC, respectively. The experimental results on a set of 22 benchmark functions demonstrate the effectiveness and efficiency of the modified search equation and the OL strategy. The comparisons with some other ABCs and several state-of-the-art algorithms show that the proposed algorithms significantly improve the performance of ABC. Moreover, OCABC offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all the test functions.