A novel bionic algorithm inspired by plant root foraging behaviors

Ma, Lianbo; Zhu, Yuhao; Liu, Yang; Lan, Tian; Chen, Hanning

doi:10.1016/j.asoc.2015.08.014

Cited by 43 publications

(22 citation statements)

References 37 publications

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“…1) CEC 2014 Benchmarks: In order to fully evaluate the optimization performance of proposed method, a set of 30 scalable shifted and rotated benchmarks from CEC 2014 competitions on static real parameter optimization [53]- [55]. The dimensions, initialization ranges, global optimum of each function ( f 1 -f 30 ) are listed in Table II.…”

Section: A Test Functionsmentioning

confidence: 99%

Artificial Bee Colony Optimizer Based on Bee Life-Cycle for Stationary and Dynamic Optimization

Chen

et al. 2016

IEEE Trans. Syst. Man Cybern, Syst.

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This paper proposes a novel optimization scheme by hybridizing an artificial bee colony optimizer (HABC) with a bee life-cycle mechanism, for both stationary and dynamic optimization problems. The main innovation of the proposed HABC is to develop a cooperative and population-varying scheme, in which individuals can dynamically shift their states of birth, foraging, death, and reproduction throughout the artificial bee colony life cycle. That is, the bee colony size can be adjusted dynamically according to the local fitness landscape during algorithm execution. This new characteristic of HABC helps to avoid redundant search and maintain diversity of population in complex environments. A comprehensive experimental analysis is implemented that the proposed algorithm is benchmarked against several state-of-the-art bio-inspired algorithms on both stationary and dynamic benchmarks. Then the proposed HABC is applied to the real-world applications including data clustering and image segmentation problems. Statistical analysis of all these tests highlights the significant performance improvement due to the life-cycle mechanism and shows that the proposed HABC outperforms the reference algorithms.

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Section: A Test Functionsmentioning

confidence: 99%

Artificial Bee Colony Optimizer Based on Bee Life-Cycle for Stationary and Dynamic Optimization

Chen

et al. 2016

IEEE Trans. Syst. Man Cybern, Syst.

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“…(1) Criterion-1: Auxin-regulated mechanism. Auxin plays an important role in the root development and its transport and signaling essentially control different stages of the root growth [27,28]. Here the auxin concentration is defined to reflect this effect, dynamically reallocated after each growth generation.…”

Section: Root System Growth Algorithm (Rsga)mentioning

confidence: 99%

A novel evolutionary root system growth algorithm for solving multi-objective optimization problems

Huang

Zhang

et al. 2017

Applied Soft Computing

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This paper proposes a novel multi-objective root system growth optimizer (MORSGO) for the copper strip burdening optimization. The MORSGO aims to handle multi-objective problems with satisfactory convergence and diversity via implementing adaptive root growth operators with a pool of multi-objective search rules and strategies. Specifically, the single-objective root growth operators including branching, regrowing and auxin-based tropisms are deliberately designed. They have merits of appropriately balancing exploring & exploiting and self-adaptively varying population size to reduce redundant computation. The effective multi-objective strategies including the fast non-dominated sorting and the farthest-candidate selection are developed for saving and retrieving the Pareto optimal solutions with remarkable approximation as well as uniform spread of Pareto-optimal solutions. With comprehensive evaluation against a suit of benchmark functions, the MORSGO is verified experimentally to be superior or at least comparable to its competitors in terms of the IGD and HV metrics. The MORSGO is then validated to solve the real-world copper strip burdening optimization with different elements. Computation results verifies the potential and effectiveness of the MORSGO to resolve complex industrial process optimization.

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“…This section briefly describes the classical ARFO proposed in [ 22 ], which simulates the intelligent foraging behaviors of plant roots. As depicted in [ 22 ], in order to idealize biological plant root growth behaviors, some criteria are presented as follows.…”

Section: Hybrid Artificial Root Foraging Optimizermentioning

confidence: 99%

“…However, terrestrial plants have prominent adaptability and sensing ability to use environmental information as a basis for governing their growth orientation and root system development [ 19 ]. Logically, such adaptive growth processes can provide novel insights into new computing paradigm for global optimization [ 20 – 22 ]. References [ 23 , 24 ] have proposed and developed the novel and effective EA variants by using a hybridization of life-cycle and optimal search strategies and obtain significant performance improvement, which shows a novel and effective computation framework for related scientists.…”

Section: Introductionmentioning

confidence: 99%

“…References [ 23 , 24 ] have proposed and developed the novel and effective EA variants by using a hybridization of life-cycle and optimal search strategies and obtain significant performance improvement, which shows a novel and effective computation framework for related scientists. How to deliberately design novel evolutionary computation model and algorithm is increasingly becoming an area of active research; taking a promising example, a representative ARFO algorithm is proposed by Ma et al in [ 22 ] and has received a surge of attention [ 23 , 24 ]. Essentially, the ARFO provides an open and extensible biocomputation framework and model for scientists in the field of optimization theory to exploit new bioinspired algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation

Liu

Lan

et al. 2016

Computational Intelligence and Neuroscience

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This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm the new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search by combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth operators are guided systematically. With root-to-root communication, individuals exchange information in different efficient topologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population driven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well maintained. The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed HARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational results of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization accuracy computation efficiency.

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A novel bionic algorithm inspired by plant root foraging behaviors

Cited by 43 publications

References 37 publications

Artificial Bee Colony Optimizer Based on Bee Life-Cycle for Stationary and Dynamic Optimization

Artificial Bee Colony Optimizer Based on Bee Life-Cycle for Stationary and Dynamic Optimization

A novel evolutionary root system growth algorithm for solving multi-objective optimization problems

Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation

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