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TSA: Tree-seed algorithm for continuous optimization

机译：TSA：连续优化的树种子算法

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This paper presents a new intelligent optimizer based on the relation between trees and their seeds for continuous optimization. The new method is in the field of heuristic and population-based search. The location of trees and seeds on n-dimensional search space corresponds with the possible solution of an optimization problem. One or more seeds are produced from the trees and the better seed locations are replaced with the locations of trees. While the new locations for seeds are produced, either the best solution or another tree location is considered with the tree location. This consideration is performed by using a control parameter named as search tendency (ST), and this process is executed for a pre-defined number of iterations. These mechanisms provide to balance exploitation and exploration capabilities of the proposed approach. In the experimental studies, the effects of control parameters on the performance of the method are firstly examined on 5 well-known basic numeric functions. The performance of the proposed method is also investigated on the 24 benchmark functions with 2, 3, 4, 5 dimensions and multilevel thresholding problems. The obtained results are also compared with the results of state-of-art methods such as artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), harmony search (HS) algorithm, firefly algorithm (FA) and the bat algorithm (BA). Experimental results show that the proposed method named as TSA is better than the state-of-art methods in most cases on numeric function optimization and is an alternative optimization method for solving multilevel thresholding problem. (C) 2015 Elsevier Ltd. All rights reserved.
机译：本文提出了一种新的智能优化器，它基于树木和种子之间的关系进行连续优化。新方法属于启发式搜索和基于人口的搜索领域。 n维搜索空间中树木和种子的位置与优化问题的可能解决方案相对应。从树木中产生一种或多种种子，并且将更好的种子位置替换为树木的位置。在生成种子的新位置时，将最佳解决方案或另一个树位置与树位置一起考虑。通过使用名为搜索趋势（ST）的控制参数来执行此考虑，并且针对预定的迭代次数执行此过程。这些机制提供了平衡所提出方法的开发和勘探能力的机制。在实验研究中，首先在5个众所周知的基本数值函数上检查了控制参数对方法性能的影响。还对具有2、3、4、5维和多级阈值问题的24个基准函数研究了所提出方法的性能。还将获得的结果与最新方法的结果进行比较，例如人工蜂群（ABC）算法，粒子群优化（PSO），和声搜索（HS）算法，萤火虫算法（FA）和蝙蝠算法（ BA）。实验结果表明，在大多数情况下，提出的称为TSA的方法在数值函数优化方面要优于最新方法，并且是解决多级阈值问题的另一种优化方法。 （C）2015 Elsevier Ltd.保留所有权利。

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