Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms

Botzheim, János; Gal, Laszlo; Kóczy, László T.

doi:10.1007/978-3-642-02187-9_3

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…(1) • Test function with 6 input variables (6iv); 500 samples [18] ( ) 5 Comparing the simulation results, in case of both test functions, it is shown that the new PBA method provides the favorable behavior: low model error compared to the other algorithms from the first stage and high model accuracy in the final stage of the optimization. However, in the initial phase of the optimization the BEA still provides higher convergence speed.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Progressive bacterial algorithm

Gal

Lovassy

Kóczy

2012

2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI)

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The purpose of this paper is to present a new version of the Bacterial Algorithms used for fuzzy rule base extraction called Progressive Bacterial Algorithm. In order to explore high quality models with very good speed of convergence towards the optimal rule base, we develop an improved version of the Bacterial Evolutionary and former Bacterial Memetic Algorithms. It is shown, in case of multidimensional reference problems, by comparing with existing methods, that an efficient and fast convergent tool is obtained.I.

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Section: Simulation Resultsmentioning

confidence: 99%

“…To obtain a high quality model with a good convergence speed, in our previous papers [5], we proposed the MBMA algorithm. The simulation results proved that this method is superior to IBMA and BMA algorithms.…”

Section: E Modified Bacterial Memetic Algorithm (Mbma)mentioning

confidence: 99%

Progressive bacterial algorithm

Gal

Lovassy

Kóczy

2012

2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI)

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“…The pseudo-bacterial genetic algorithm is a practical evolutionary algorithm that offers fast convergence and improvement in the results [30], without detrimental effects in the exploration. In the most elementary form, the pseudobacterial genetic algorithm is modeled for computer simulation employing the difference equation presented in (5).…”

Section: Pseudo-bacterial Genetic Algorithmmentioning

confidence: 99%

Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots

2019

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A hybrid path planning algorithm based on membrane pseudo-bacterial potential field (MemPBPF) is proposed. Membrane-inspired algorithms can reach an evolutionary behavior based on biochemical processes to find the best parameters for generating a feasible and safe path. The proposed MemPBPF algorithm uses a combination of the structure and rules of membrane computing. In that sense, the proposed MemPBPF algorithm contains dynamic membranes that include a pseudo-bacterial genetic algorithm for evolving the required parameters in the artificial potential field method. This hybridization between membrane computing, the pseudo-bacterial genetic algorithm, and the artificial potential field method provides an outperforming path planning algorithm for autonomous mobile robots. Computer simulation results demonstrate the effectiveness of the proposed MemPBPF algorithm in terms of path length considering collision avoidance and smoothness. Comparisons with two different versions employing a different number of elementary membranes and with other artificial potential field based algorithms are presented. The proposed MemPBPF algorithm yields improved performance in terms of time execution by using a parallel implementation on a multi-core computer. Therefore, the MemPBPF algorithm achieves high performance yielding competitive results for autonomous mobile robot navigation in complex and real scenarios. INDEX TERMS Artificial potential field, autonomous mobile robots, membrane computing, path planning, pseudo-bacterial genetic algorithm.

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“…This algorithm introduced a genetic operator called bacterial mutation that has demonstrated to be useful in environments with a weak relationship between the parameters of a system. It is a simple algorithm that presents a fast convergence and improvement in the solutions [23], without being detrimental in landscape exploration. Together with the basic idea of achieving a high performance system and faster processes using the PBGA, the divide and conquer approach is also used in this paper; so, a parallelized PBGA version based on the master-slave approach is applied to a single population, where a master node executes the PBGA operators (bacterial mutation, selection, and crossover) working on the population P ′( t ), and the fitness evaluation of the individuals in P ′( t ) is divided in P i ( t ) sub-populations distributed on the slave processors.…”

Section: Theoretical Fundamentals Of the Pseudo-bacterial Potential Fmentioning

confidence: 99%

“…The PBGA is a particular type of GA in which the bacterial mutation operator is incorporated. This operator is inspired by the biological model of bacterial cells, which mimics the phenomenon of microbial evolution [23]. To find the global optimum, it is necessary to explore different regions in the search space that have not been covered with the current population.…”

Section: Bacterial Mutation Operatormentioning

confidence: 99%

Pseudo-Bacterial Potential Field Based Path Planner for Autonomous Mobile Robot Navigation

Orozco-Rosas

Montiel

Sepúlveda

2015

International Journal of Advanced Robotic Systems

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This paper introduces the pseudo-bacterial potential field (PBPF) as a new path planning method for autonomous mobile robot navigation. The PBPF allows us to obtain an optimal and safe path, in contrast to the classical potential field approach which is not suitable for path planning because it lacks a means of obtaining the optimal proportional gains. The PBPF uses the pseudo-bacterial genetic algorithm (PBGA) and a fitness function based on the potential field concepts to construct viable paths in dynamical environments to mostly result in the optimal path being obtained. Comparative experiments of sequential and parallel implementations of the PBPF for off-line and online in structured and unstructured conditions are presented; the results are contrasted with the artificial potential field (APF) method to demonstrate how the PBPF proposal overcomes the traditional method.

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Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms

Cited by 9 publications

References 18 publications

Progressive bacterial algorithm

Progressive bacterial algorithm

Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots

Pseudo-Bacterial Potential Field Based Path Planner for Autonomous Mobile Robot Navigation

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