RETRACTED ARTICLE: A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity

Sun, Na; Lu, Yong

doi:10.1007/s00521-018-3438-9

Cited by 29 publications

(8 citation statements)

References 26 publications

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“…Currently, the research of AGA has been relatively mature and extensive [31], but few of them have been combined with trust mechanisms. Genetic manipulation involves four main processes: coding, selection, crossover, and mutation.…”

Section: B Routing Based On Agamentioning

confidence: 99%

Energy-Aware and Trust-Based Secure Routing Protocol for Wireless Sensor Networks Using Adaptive Genetic Algorithm

Han

Guo

2022

IEEE Access

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Due to their working environments, limited resources and communication characteristics, wireless sensor networks face some challenges including energy optimization and security enhancement to extend the network lifetime and guarantee the network security. Therefore, an energy-aware and trust-based routing protocol for wireless sensor networks using adaptive genetic algorithm called TAGA is proposed to not only resist common routing attacks and special trust attacks, but also minimize the energy consumption caused by data transmission. To this end, TAGA constructs the nodes' comprehensive trust values based on their direct trust values considering the volatilization and adaptive penalty factors, and indirect trust values with the filtering mechanisms. In addition, a novel threshold function is presented to select the optimal cluster heads, which considers the dynamic changes of the nodes' comprehensive trust values and residual energy. Finally, a genetic algorithm with adaptive crossover probability and mutation probability is applied to find the optimal secure routing for the cluster heads. The simulation results show that TAGA can reduce the number of packets discarded by malicious nodes when facing common attacks and special trust attacks, and effectively improve the energy efficiency compared to the relative secure routing protocols EOSR and IASR.INDEX TERMS Wireless sensor networks, secure routing, comprehensive trust, direct trust.

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Section: B Routing Based On Agamentioning

confidence: 99%

Energy-Aware and Trust-Based Secure Routing Protocol for Wireless Sensor Networks Using Adaptive Genetic Algorithm

Han

Guo

2022

IEEE Access

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“…The second category is to improve the crossover operator [9] or mutation operator [10] in GA, which improves the optimization performance of GA to some extent, but the improvement is not significant [11].…”

Section: Introductionmentioning

confidence: 99%

Optimization Performance Analysis for Adaptive Genetic Algorithm with Nonlinear Probabilities

Sun¹,

Su²,

Yuan³

et al. 2022

IJACSA

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Genetic Algorithm (GA) has been proven to be easy in falling into local optimal value due to its fixed crossover probability and mutation probability, while Adaptive Genetic Algorithm (AGA) has strong global search capability because the two probabilities adjust adaptively. There are two categories of AGA according to the different adjustment methods for crossover and mutation probabilities: probabilistic linear adjustment AGA and probabilistic non-linear adjustment AGA. AGA with linear adjustment of probability values cannot solve the problems of local optimal value and premature convergence. The nonlinear adaptive probability adjustment strategy can avoid premature convergence, poor stability and slow convergence speed. The typical AGA with nonlinear adjustment of probabilities are compared and analyzed through benchmark functions. The optimization performance of typical AGA algorithms is compared and analyzed by 10 benchmark functions. Compared with traditional GA and other AGA algorithms, AGA with crossover and mutation probabilities adjusted nonlinearly at both ends of the average fitness value has higher computational stability and is easy to find the global optimal solution, which provides ideas for the application of adaptive genetic algorithm.

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“…Recording and adapting to the population diversity value has lately become a popular component of many metaheuristic optimizers [1,6,11,12,13]. There are numerous measures for quantifying the population diversity [8,9,11]; however, usually, the diversity change rate is more important for understanding the inner dynamic of the algorithm than any specific value of the used numerical measure.…”

Section: Introductionmentioning

confidence: 99%

Relation of Neighborhood Size and Diversity Loss Rate in Particle Swarm Optimization With Ring Topology

Pluháček

Kazíková

Kadavý

et al. 2021

mendel

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Measuring the population diversity in metaheuristics has become a common practice for adaptive approaches, aiming mainly to address the issue of premature convergence. Understanding the processes leading to a diversity loss in a metaheuristic algorithm is crucial for designing successful adaptive approaches. In this study, we focus on the relation of the neighborhood size and the rate of diversity loss in the Particle Swarm Optimization algorithm with local topology (also known as LPSO). We argue that the neighborhood size is an important input to consider when designing any adaptive approach based on the change of population diversity. We used the extensive benchmark suite of the IEEE CEC 2014 competition for experiments.

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RETRACTED ARTICLE: A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity

Cited by 29 publications

References 26 publications

Energy-Aware and Trust-Based Secure Routing Protocol for Wireless Sensor Networks Using Adaptive Genetic Algorithm

Energy-Aware and Trust-Based Secure Routing Protocol for Wireless Sensor Networks Using Adaptive Genetic Algorithm

Optimization Performance Analysis for Adaptive Genetic Algorithm with Nonlinear Probabilities

Relation of Neighborhood Size and Diversity Loss Rate in Particle Swarm Optimization With Ring Topology

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