Adedoyin A. Hussain^1,2,*, Fadi Al-Turjman^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1345-1369, 2023, DOI:10.32604/cmes.2023.022783 - 27 October 2022

Abstract The internet of medical things (IoMT) empowers patients to get adaptable, and virtualized gear over the internet. Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on it. Thus, a proposition is being made for a distinct scheduling technique to suitably meet these solicitations. To manage the scheduling issue, an artificial intelligence (AI) method known as a hybrid genetic algorithm (HGA) is proposed. The proposed AI method will be justified by contrasting it with other traditional optimization and AI scheduling approaches. The CloudSim is utilized to quantify its effect More >

Qi Zhou^1,2, Jinghua Li^1,3, Ruipu Dong^1,*, Qinghua Zhou^3,*, Boxin Yang³

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1263-1281, 2023, DOI:10.32604/cmes.2022.020744 - 31 August 2022

Abstract Offshore engineering construction projects are large and complex, having the characteristics of multiple execution modes and multiple resource constraints. Their complex internal scheduling processes can be regarded as resourceconstrained project scheduling problems (RCPSPs). To solve RCPSP problems in offshore engineering construction more rapidly, a hybrid genetic algorithm was established. To solve the defects of genetic algorithms, which easily fall into the local optimal solution, a local search operation was added to a genetic algorithm to defend the offspring after crossover/mutation. Then, an elitist strategy and adaptive operators were adopted to protect the generated optimal solutions, More >

Yizhi Liu^1,2, Rutian Qing^1,2,*, Liangran Wu^1,2, Min Liu^1,2, Zhuhua Liao^1,2, Yijiang Zhao^1,2

Journal on Artificial Intelligence, Vol.3, No.1, pp. 33-43, 2021, DOI:10.32604/jai.2021.016565 - 02 April 2021

Abstract In the large-scale logistics distribution of single logistic center, the method based on traditional genetic algorithm is slow in evolution and easy to fall into the local optimal solution. Addressing at this issue, we propose a novel approach of exploring hybrid genetic algorithm based large-scale logistic distribution for BBG supermarket. We integrate greedy algorithm and hillclimbing algorithm into genetic algorithm. Greedy algorithm is applied to initialize the population, and then hill-climbing algorithm is used to optimize individuals in each generation after selection, crossover and mutation. Our approach is evaluated on the dataset of BBG Supermarket More >

Hong Zhu^1,2,*

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 741-748, 2020, DOI:10.32604/iasc.2020.010108

Abstract In this paper, a human resource allocation method based on the multi-objective hybrid genetic algorithm is proposed, which uses the multi-stage decision model to resolve the problem. A task decision is the result of an interaction under a set of conditions. There are some available decisions in each stage, and it is easy to calculate their immediate effects. In order to give a set of optimal solutions with limited submissions, a multi-objective hybrid genetic algorithm is proposed to solve the combinatorial optimization problems, i.e. using the multiobjective hybrid genetic algorithm to find feasible solutions at… More >

Xing Wei^1,2, Wen Chen^1,3, Bin Chen¹

CMC-Computers, Materials & Continua, Vol.48, No.1, pp. 1-23, 2015, DOI:10.3970/cmc.2015.048.001

Abstract The thermal residual stress in the fabrication of functionally graded material (FGM) systems can give rise to various mechanical failures. For a FGM system under a given fabrication environment, the thermal residual stresses are determined by the spatial distribution of its constituent components. In this study, we optimize a Ni/Al₂O₃ FGM plate aiming at minimizing the thermal residual stresses through controlling its compositional distribution. Material properties are graded in the thickness direction following a power law distribution in terms of the volume fractions of constituents (P-FGM). An analytical model and a hybrid genetic algorithm with the More >

R. Mathur¹, S. G. Advani², B. K. Fink³

CMES-Computer Modeling in Engineering & Sciences, Vol.4, No.5, pp. 587-602, 2003, DOI:10.3970/cmes.2003.004.587

Abstract Real number-coded hybrid genetic algorithms for optimal design of resin injection locations for the resin transfer molding process are evaluated in this paper. Resin transfer molding (RTM) is widely used to manufacture composite parts with material and geometric complexities, especially in automotive and aerospace sectors. The sub-optimal location of the resin injection locations (gates) can leads to the formation of resin starved regions and require long mold fill times, thus affecting the part quality and increasing manufacturing costs. There is a need for automated design algorithms and software that can determine the best gate and… More >