Study on efficient channel assignment method using the genetic algorithm for mobile communication systems

Yoshino, Junichi; Ohtomo, Isao

doi:10.1007/s00500-003-0355-2

Cited by 10 publications

(4 citation statements)

References 9 publications

(8 reference statements)

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“…Genetic Algorithm(GA) was proposed in 1975 by Holland [15]. It is a search heuristic algorithm used to solve optimization in the field of artificial intelligence, which has been successfully applied to various optimization issues [19]- [21]. This paper adopt the attribute reduction algorithm based on genetic algorithm.…”

Section: ) Attribute Reduction and Extraction Of Decision Rulesmentioning

confidence: 99%

Research on Construction of Crude Set Model of Critical Fault Information for Bus Based on CAN-BUS Data

Jia

Zhang

et al. 2020

IEEE Access

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Under the high load, high frequency and high strength operating environment, the frequent occurrence of vehicle fault gradually attracts the attention of the society. The real-time monitoring and data recording function of vehicle-mounted equipment provides data support for vehicle status assessment and fault warning. In this paper, the real-time data collected by CAN-BUS system of Beijing Bus Group are preprocessed and discretized. On the basis of the traditional rough set theory, a new coding method is set up, and the dependency between conditional attributes and decision attributes is set as an adaptive function, which is reduced by genetic algorithm and cellular genetic algorithm respectively. The calculation results show that the key fault information of public transport vehicles is instrumental speed, oil pressure, percentage of torque, timing engine speed, and coolant temperature. By comparing the results of reduction, it is found that the cellular genetic algorithm has higher applicability than the genetic algorithm in terms of algorithm efficiency, stability, and convergence quality. Although the genetic algorithm attribute reduction is slightly better than the cellular genetic algorithm attribute reduction in the rule matching, the cellular genetic algorithm has a better ability to excavate information within the acceptable compatibility range. Finally, the selected key factors will be deployed on the Beijing Bus Group's big data platform and displayed in real time. The conclusion of this paper enriches the theory of bus engine fault warning and establishes an engine failure warning system, which can effectively reduce the failure rate of bus vehicles and reduce the maintenance cost expenditure. It has certain guiding significance for the bus operation work of Beijing Bus Group.

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Section: ) Attribute Reduction and Extraction Of Decision Rulesmentioning

confidence: 99%

Research on Construction of Crude Set Model of Critical Fault Information for Bus Based on CAN-BUS Data

Jia

Zhang

et al. 2020

IEEE Access

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“…GCP is applied in different engineering applications. GCP can also be specifically applied in wireless networks where the cells are properly assigned to the corresponding channels based on its interference matrix called Channel Assignment Problem (CAP) [29,[38][39][40][41]. CAP allocates bandwidth and communication channels to base stations in cellular and wireless networks.…”

Section: Introductionmentioning

confidence: 99%

A New Multi-Objective Evolutionary Approach to Graph Coloring and Channel Allocation Problems

Balakrishnan¹,

Suresh²,

Marappan³

2021

JAMC

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Recent years, a large amount of graph coloring algorithms have been applied in different disciplines and engineering. This paper exhibits a new multi-objective evolutionary algorithm using the new evolutionary operators with multi-objectives in finding the solution to graph coloring and channel allocation. Clique, a maximal connected complete subgraph of a given graph is obtained in solving channel allocation problem. The outcomes of the devised evolutionary method are compared with other recent approaches. Multiple better gene sequences are selected and are applying the subsequent crossover and mutation operations with multiple objectives. The devised operators significantly minimize the problem search space and average generations compared to the standard genetic algorithm. The proposed operators achieve better solution compared to some of the existing well known methods. The expected performance measures are also increased while minimizing the expected generations. The proposed operators can also be applied in solving special engineering applications of graph coloring.

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“…χ(G), the chromatic number of G, is defined as the minimum number of colors required for V(G) so that no two adjacent vertices are of the same color [2]. The graph coloring problem (GCP) finds the value for χ(G) and applies it to register allocation, channel assignment, image segmentation, resource utilization, and scheduling [3][4][5][6][7][8][9][10]. With the increasing values of n, the complexity of determining χ(G) also increases.…”

Section: Introductionmentioning

confidence: 99%

Complexity Analysis and Stochastic Convergence of Some Well-known Evolutionary Operators for Solving Graph Coloring Problem

Marappan

Sethumadhavan

2020

Mathematics

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The graph coloring problem is an NP-hard combinatorial optimization problem and can be applied to various engineering applications. The chromatic number of a graph G is defined as the minimum number of colors required to color the vertex set V(G) so that no two adjacent vertices are of the same color, and different approximations and evolutionary methods can find it. The present paper focused on the asymptotic analysis of some well-known and recent evolutionary operators for finding the chromatic number. The asymptotic analysis of different crossover and mutation operators helps in choosing the better evolutionary operator to minimize the problem search space and computational complexity. The choice of the right genetic operators facilitates an evolutionary algorithm to achieve faster convergence with lesser population size N through an adequate distribution of promising genes. The selection of an evolutionary operator plays an essential role in reducing the bounds for minimum color obtained so far for some of the benchmark graphs. This research also focuses on the necessary and sufficient conditions for the global convergence of evolutionary algorithms. The stochastic convergence of recent evolutionary operators for solving graph coloring is newly analyzed.

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Study on efficient channel assignment method using the genetic algorithm for mobile communication systems

Cited by 10 publications

References 9 publications

Research on Construction of Crude Set Model of Critical Fault Information for Bus Based on CAN-BUS Data

Research on Construction of Crude Set Model of Critical Fault Information for Bus Based on CAN-BUS Data

A New Multi-Objective Evolutionary Approach to Graph Coloring and Channel Allocation Problems

Complexity Analysis and Stochastic Convergence of Some Well-known Evolutionary Operators for Solving Graph Coloring Problem

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