Many new bio-inspired algorithms are recently being proposed, artificialtree (AT) algorithm, inspired by the growth of trees and the update behavior of branches, is one of them. There are also some improved AT algori...
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Many new bio-inspired algorithms are recently being proposed, artificialtree (AT) algorithm, inspired by the growth of trees and the update behavior of branches, is one of them. There are also some improved AT algorithms being proposed to improve their calculation accuracy. However, the main challenges of AT algorithms lie in the insufficiencies in the design of update operators as well as the position interaction between branches and the capture of key information and the performance of AT algorithms needs to be enhanced. This work proposes an improved AT algorithm with two populations (IATTP). In IATTP, the update strategies of branches are redesigned, and a variety of efficient update operators are designed and applied. The branch population is changed from one to two, and the competition mechanism between populations is proposed. Through the migration of branches between populations, the scale of population with better efficiency is expanded and the size of population with lower efficiency is reduced, thus a reasonable interaction between populations and branches is realized. With above strategies, the efficiency and accuracy of IATTP are significantly improved. The results of IATTP are proved to be advantageous when the performance of IATTP is compared with AT algorithm, improved artificialtree (IAT) algorithm and feedback artificialtree (FAT) algorithm through typical test problems. Meanwhile, the results of IATTP in current state are also preferable when IATTP is compared with other improved algorithms in high dimensional problems. The experimental results prove that IATTP is competitive in solving optimization problems.
Inspired by the transport of organic matters and the update theories of branches, the artificialtree (AT) algorithm was proposed recently. This work presents an improved version of AT algorithm that is called the fee...
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Inspired by the transport of organic matters and the update theories of branches, the artificialtree (AT) algorithm was proposed recently. This work presents an improved version of AT algorithm that is called the feedback artificialtree (FAT) algorithm. In FAT, besides the transfer of organic matters, the feedback mechanism of moistures is introduced. Meanwhile, the self-propagating operator and dispersive propagation operator are also put forward. Some typical benchmark problems are applied to test the performance of FAT. The experimental results have clearly demonstrated the higher performance of FAT compared with AT over the tested set of problems. In addition, some well-known heuristic algorithms and their improved algorithms are also applied to validate the performance of FAT, and the computational results of FAT listed in this study are the best among these algorithms. In addition, sensitive analyses on the specific parameters of FAT algorithm are carried out, and the performance of FAT is validated.
Bionic intelligence algorithms have many advantages compared with traditional optimization algorithms. In this paper, inspired by the growth law of trees, a new bionic algorithm, named artificialtree (AT) algorithm i...
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Bionic intelligence algorithms have many advantages compared with traditional optimization algorithms. In this paper, inspired by the growth law of trees, a new bionic algorithm, named artificialtree (AT) algorithm is developed. In the proposed AT, the branch position is considered as the design variable. In addition, the branch is the solution, and the branch thickness is the indicator of the solution. The computing process of AT is achieved by simulating the transport of organic matters and the update of tree branches. The comparative analysis using thirty typical benchmark problems between AT algorithm and some well-known bionic intelligent methods is also performed. Based on numerical results, AT is found to be very effective in dealing with various problems. (C) 2017 Elsevier Ltd. All rights reserved.
In this paper, five successful nature inspired algorithms;the artificial tree algorithm (AT), the particle swarm optimization (PSO), the genetic algorithm (GA), the cultural algorithm (CA), and the cuckoo search algor...
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In this paper, five successful nature inspired algorithms;the artificial tree algorithm (AT), the particle swarm optimization (PSO), the genetic algorithm (GA), the cultural algorithm (CA), and the cuckoo search algorithm (CS) have been compared on multilevel image thresholding. The segmentation process is based on the Levine and Nazif intra class uniformity criterion which is seen as an optimization problem. The comparison performances are in terms of the value of the objectif function, the peak signal to noise ratio (PSNR) and the computation time. Empirical results over different benchmark images for different threshold numbers reveal the robustness, the reliability and the rapidity of the cultural algorithm (CA).
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