Fuzzy Inference System Optimization by Evolutionary Approach for Mobile Robot Navigation

This paper introduces a new approach to enhance performance in performing logic programming in the Hopfield neural network by using agent-based modeling. Hopfield networks have been broadly utilized to solve problems of combinatorial optimization. However, this network yielded a satisfiability problem because the network has grown larger, and it is more complex. Therefore, an improved algorithm has been proposed to enhance the Hopfield network's capability by using the technique of fuzzy logic to provide more efficient energy relaxation and to avoid the local minimum solutions. Agent-based modeling has been introduced in this paper to conduct computer simulations, which aim at verifying and validating the introduced approach. By applying the technique of fuzzy Hopfield neural network clustering in the system, better quality solutions are produced, and the network is handled better despite the increasing complexity. Also, the solutions converged faster by the system. Accordingly, this technique of the fuzzy Hopfield neural network clustering in the system has produced better-quality solutions.

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“…Processing time is defined as a time acquired by a model to complete the training phase and retrieval phase [21].…”

Section: Processing Timementioning

confidence: 99%

Agent-based Modeling in doing Logic Programming in Fuzzy Hopfield Neural Network

Alzaeemi¹,

Sathasivam²,

Velavan³

2021

IJMECS

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“…The decision process is an inference process. FIS is an efficient tool to build such an expert system [27,28]. It maps the input (performance data with respect to each indicator) to the output (crisp value representing the overall performance) through a set of IF-THEN rules.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Undergraduate Course based on a Fuzzy AHP-FIS Model

Liu¹,

Zhang²

2020

IJMECS

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“…, * ) ∈ verifying ( * ) = min ∈ ( ). There are in general, many fields of swarm approach application in resolving combinatorial optimization problems [7][8][9][10][11], and variants of ant colony algorithms, in neural network [12], telecommunication network [13], computer science engineering [14][15], robotic [16], energetic efficiency [17], and other general fields [18][19].…”

Section: A Combinatorial Optimization Problems (Cop)mentioning

confidence: 99%

A New Approach for Dynamic Parametrization of Ant System Algorithms

Masrour¹,

Rhazzaf²

2018

IJISA

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Abstract-This paper proposes a learning approach for dynamic parameterization of ant colony optimization algorithms. In fact, the specific optimal configuration for each optimization problem using these algorithms, whether at the level of preferences, the level of evaporation of the pheromone, or the number of ants, makes the dynamic approach an interested one. The new idea suggests the addition of a knowledge center shared by the colony members, combining the optimal evaluation of the configuration parameters proposed by the colony members during the experiments. This evaluation is based on qualitative criteria explained in detail in the article. Our approach indicates an evolution in the quality of the results over the course of the experiments and consequently the approval of the concept of machine learning.

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Fuzzy Inference System Optimization by Evolutionary Approach for Mobile Robot Navigation

Cited by 9 publications

References 16 publications

Agent-based Modeling in doing Logic Programming in Fuzzy Hopfield Neural Network

Agent-based Modeling in doing Logic Programming in Fuzzy Hopfield Neural Network

Evaluating the Undergraduate Course based on a Fuzzy AHP-FIS Model

A New Approach for Dynamic Parametrization of Ant System Algorithms

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