A novel multi-parent order crossover in genetic algorithm for combinatorial optimization problems

Arram, Anas; Ayob, Masri

doi:10.1016/j.cie.2019.05.012

“…A crossover operation combines two chromosomes to produce a new offspring. In the proposed work, chromosomes selection by crossover is based on the roulette wheel probabilities 39 . This greatly increases the likelihood of optimal solution selection and is based on fitness quality.…”

Section: Nsga-ii Based Nds Optimization (Nsgaii-nds)mentioning

confidence: 99%

Genetic algorithm optimized node deployment in IEEE 802.15.4 potato and wheat crop monitoring infrastructure

Pal

¹

,

Sharma

²

,

Tripathi

³

et al. 2021

Sci Rep

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This proposal investigates the effect of vegetation height and density on received signal strength between two sensor nodes communicating under IEEE 802.15.4 wireless standard. With the aim of investigating the path loss coefficient of 2.4 GHz radio signal in an IEEE 802.15.4 precision agriculture monitoring infrastructure, measurement campaigns were carried out in different growing stages of potato and wheat crops. Experimental observations indicate that initial node deployment in the wheat crop experiences network dis-connectivity due to increased signal attenuation, which is due to the growth of wheat vegetation height and density in the grain-filling and physical-maturity periods. An empirical measurement-based path loss model is formulated to identify the received signal strength in different crop growth stages. Further, a NSGA-II multi-objective evolutionary computation is performed to generate initial node deployment and is optimized over increased coverage, reduced over-coverage, and received signal strength. The results show the development of a reliable wireless sensor network infrastructure for wheat crop monitoring.

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“…(1) Crossover of OS Based on the literature [29], an improved multi-parent crossover (IMX) approach is proposed. The crossover of IMX is as follows:…”

Section: ) Crossovermentioning

confidence: 99%

Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm

Gu¹

2021

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This paper proposes a hybrid artificial bee colony (HABC) to solve the multiobjective lowcarbon flexible job-shop scheduling problem (MLFJSP). HABC algorithm uses a two-layer coding method to establish the initial population as the nectar source for the employed bees. In the optimization process, the employed bee phase and the onlooker bee phase adopt improved crossover mutation strategies and adaptive neighborhood search strategies to generate new nectar sources, and the greedy method is used to retain better solutions. The scout bee update mechanism prevents the algorithm from falling into a local optimum and enhances the convergence of the algorithm. In order to prevent the loss of the optimal solution, the optimization results of each phase are saved in the Pareto archive (PA). Finally, two sets of international standard instances are used to carry out simulation experiments. After analyzing the simulation results, it is concluded that HABC is an effective algorithm to solve the multiobjective low-carbon flexible job shop scheduling problem.INDEX TERMS artificial bee colony, multiobjective flexible job-shop problem, low-carbon, adaptive variable neighborhood search

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“…Where r is the rank of the chr Crossover and Mutation operation A crossover operation combines two chromosomes to produce a new offspring. In the proposed work, chromosomes selection in the crossover is based on the roulette wheel probabilities 34 . This greatly increases the likelihood of optimal solution selection and is based on fitness quality.…”

Section: Nsga-ii Based Nds Optimization (Nsgaii-nds)mentioning

confidence: 99%

Genetic Algorithm Optimized Node Deployment in IEEE 802.15.4 Potato and Wheat Crop Monitoring Infrastructure

Pal

¹

,

Tripathi

²

,

Kumar

³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

This proposal investigates the effect of vegetation height and density on received signal strength between two sensor nodes communicating under IEEE 802.15.4 wireless standard. With the aim of investigating the path loss coefficient of 2.4 GHz radio signal in an IEEE 802.15.4 precision agriculture monitoring infrastructure, measurement campaigns were carried out in different growing stages of potato and wheat crops. Experimental observations indicate that initial node deployment in the wheat crop experiences network dis-connectivity due to increased signal attenuation, which is due to the growth of wheat vegetation height and density in the grain-filling and physical-maturity periods. An empirical measurement-based path loss model is formulated to identify the received signal strength in different crop growth stages. Further, a NSGA-II multi-objective evolutionary computation is performed to generate initial node deployment and is optimized over increased coverage, reduced over-coverage, and received signal strength. The results show the development of a reliable wireless sensor network infrastructure for wheat crop monitoring.

show abstract

A novel multi-parent order crossover in genetic algorithm for combinatorial optimization problems

Cited by 50 publications

References 21 publications

Genetic algorithm optimized node deployment in IEEE 802.15.4 potato and wheat crop monitoring infrastructure

Genetic algorithm optimized node deployment in IEEE 802.15.4 potato and wheat crop monitoring infrastructure

Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm

Genetic Algorithm Optimized Node Deployment in IEEE 802.15.4 Potato and Wheat Crop Monitoring Infrastructure

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