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Journal articles on the topic 'Improved Hybrid Optimisation using Genetic Algorithm'

Author: Grafiati

Published: 10 January 2023

Last updated: 28 January 2023

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1

Dharani Pragada, Venkata Aditya, Akanistha Banerjee, and Srinivasan Venkataraman. "OPTIMISATION OF NAVAL SHIP COMPARTMENT LAYOUT DESIGN USING GENETIC ALGORITHM." Proceedings of the Design Society 1 (July 27, 2021): 2339–48. http://dx.doi.org/10.1017/pds.2021.495.

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AbstractAn efficient general arrangement is a cornerstone of a good ship design. A big part of the whole general arrangement process is finding an optimized compartment layout. This task is especially tricky since the multiple needs are often conflicting, and it becomes a serious challenge for the ship designers. To aid the ship designers, improved and reliable statistical and computation methods have come to the fore. Genetic algorithms are one of the most widely used methods. Islier's algorithm for the multi-facility layout problem and an improved genetic algorithm for the ship layout design problem are discussed. A new, hybrid genetic algorithm incorporating local search technique to further the improved genetic algorithm's practicality is proposed. Further comparisons are drawn between these algorithms based on a test case layout. Finally, the developed hybrid algorithm is implemented on a section of an actual ship, and the findings are presented.

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Zhang, Han, Jibin Yang, Jiye Zhang, Pengyun Song, and Ming Li. "Optimal energy management of a fuel cell-battery-supercapacitor-powered hybrid tramway using a multi-objective approach." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 5 (May 15, 2019): 511–23. http://dx.doi.org/10.1177/0954409719849804.

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Achieving an optimal operating cost is a challenge for the development of hybrid tramways. In the past few years, in addition to fuel costs, the lifespan of the power source is being increasingly considered as an important factor that influences the operating cost of a tramway. In this work, an optimal energy management strategy based on a multi-mode strategy and optimisation algorithm is described for a high-power fuel cell hybrid tramway. The objective of optimisation is to decrease the operating costs under the conditions of guaranteeing tramway performance. Besides the fuel costs, the replacement cost and initial investment of all power units are also considered in the cost model, which is expressed in economic terms. Using two optimisation algorithms, a multi-population genetic algorithm and an artificial fish swarm algorithm, the hybrid system's power targets for the energy management strategy were acquired using the multi-objective optimisation. The selected case study includes a low-floor light rail vehicle, and experimental validations were performed using a hardware-in-the-loop workbench. The results testify that an optimised energy management strategy can fulfil the operational requirements, reduce the daily operation costs and improve the efficiency of the fuel cell system for a hybrid tramway.

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Yang, Jianglong, Li Zhou, and Huwei Liu. "Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse." PLOS ONE 16, no. 4 (April 5, 2021): e0249543. http://dx.doi.org/10.1371/journal.pone.0249543.

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The utilization of a storage space can be considerably improved by using dense mobile racks. However, it is necessary to perform an optimisation study on the order picking to reduce the time cost as much as possible. According to the channel location information that needs to be sorted, the multiple orders are divided into different batches by using hierarchical clustering. On this basis, a mathematical model for the virtual order clusters formed in the batches is established to optimize the order cluster picking and rack position movement, with the minimum picking time as the objective. For this model, a hybrid genetic algorithm is designed, and the characteristics of the different examples and solution algorithms are further analysed to provide a reference for the solution of the order picking optimisation problem in a dense mobile rack warehouse.

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Zou, Yao, and Ruifeng Wang. "Research on energy-saving operation optimization of urban rail trains based on genetic particle swarm hybrid algorithm." Journal of Physics: Conference Series 2401, no. 1 (December 1, 2022): 012075. http://dx.doi.org/10.1088/1742-6596/2401/1/012075.

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Abstract In order to effectively reduce the energy consumption of urban rail trains during station-to-station operation, an energy-saving optimisation method for train operating curves is proposed by introducing a genetic algorithm (GA) into a particle swarm algorithm (PSO).Firstly, under the premise of considering the train characteristic parameters and the speed limit conditions of the line, the energy consumption model is established with the constraints such as speed limit, running time and running distance; secondly, the particle swarm algorithm is improved by using inertia weights and learning factor, and the cross-variance operator of genetic algorithm is introduced to verify that the improved algorithm improves convergence speed, global optimization ability and local search ability. The speed-distance operation curve that uses the least amount of energy between stations is then derived by solving the energy-saving operation energy consumption model of the train using the GAPSO algorithm. According to the simulation results, the approach can successfully cut operational energy consumption by 11.46% while still ensuring punctual arrival and exact stopping, which offers a workable solution for the best-case scenario design of energy-saving train operation curves.

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Andal C., Kothai, and Jayapal R. "Improved GA based power and cost management system in a grid-associated PV-wind system." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 4 (December 1, 2021): 2531. http://dx.doi.org/10.11591/ijpeds.v12.i4.pp2531-2544.

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Renewable hybrids play an essential part in assisting India with quickening the decarbonisation of power production and lowering power production expense in the medium term. PV and wind energy are complementary to each other, making the system to generate electricity almost throughout the year. In this paper, a grid-associated PV-wind energy system tied with a battery is analysed. PV, wind, grid and battery are the sources to be effectively scheduled for uninterrupted power and cost minimisation. Energy management controllers use optimisation strategies for effective utilisation of sources and cost minimisation. The methodologies are detailed as optimisation problems. Limiting the household energy cost is considered as objective, and the delivery ratio of power offered to the grid and utilised locally is treated as the optimisation variable. In this paper, an improved genetic algorithm is proposed to solve the formulated nonlinear optimisation problems. The time-of-use tariff is becoming popular in India; therefore, this article analyses the improved genetic algorithm based intelligent power and cost management system under time-of-use tariff. Using MATLAB, the proposed approach's performance is presented with the comparative analysis of conventional self-made for self-consumed and rest for sale mode and genetic algorithm-based energy management controller.

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Kassim, Azleena Mohd. "The The Efficiency of Hybridised Genetic Algorithm and Ant Colony Optimisation (HGA-ACO) in a Restaurant Recommendation System." ASM Science Journal 17 (November 7, 2022): 1–11. http://dx.doi.org/10.32802/asmscj.2022.1322.

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A recommendation system (RS) is used to provide recommendations to users by filtering items based on given inputs. Metaheuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Optimisation (ACO) are known to be used in many RS to provide optimal and good recommendations. Both algorithms are designed based on nature-inspired events, where GA is designed based on the natural evolution process while ACO is based on ants’ behaviour in their natural habit. In this paper, both GA and ACO algorithms were implemented in a restaurant RS and evaluated by using the restaurant’s attributes, which was then followed by a list of recommended restaurants as the output. With the highest score of 99.64% of accuracy, GA overtakes ACO in terms of recommendation accuracy while ACO computed 67.12% lesser runtime than GA. Considering the results acquired, a new hybrid framework known as the HGA-ACO algorithm was proposed. The proposed HGA-ACO has a recommendation accuracy of 99.57% and achieved a 31.37% runtime reduction from GA. Thus, the proposed framework was observed to have improved the output accuracy of ACO and improved the processing time in GA, thus, improving the overall efficiency of the RS.

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7

Jusop, Masitah, and Mohd Fadzil Faisae Ab Rashid. "Optimisation of Assembly Line Balancing Type-E with Resource Constraints Using NSGA-II." Key Engineering Materials 701 (July 2016): 195–99. http://dx.doi.org/10.4028/www.scientific.net/kem.701.195.

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Assembly line balancing of Type-E problem (ALB-E) is an attempt to assign the tasks to the various workstations along the line so that the precedence relations are satisfied and some performance measures are optimised. A majority of the recent studies in ALB-E assume that any assembly task can be assigned to any workstation. This assumption lead to higher usage of resource required in assembly line. This research studies assembly line balancing of Type-E problem with resource constraint (ALBE-RC) for a single-model. In this work, three objective functions are considered, i.e. minimise number of workstation, cycle time and number of resources. In this paper, an Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) has been proposed to optimise the problem. Six benchmark problems have been used to test the optimisation algorithm and the results are compared to multi-objective genetic algorithm (MOGA) and hybrid genetic algorithm (HGA). From the computational test, it was found NSGA-II has the ability to explore search space, has better accuracy of solution and also has a uniformly spaced solution. In future, a research to improve the solution accuracy is proposed to enhance the performance of the algorithm.

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Hassan, Azmi, Muhammad Ridwan Andi Purnomo, and Putri Dwi Annisa. "Clustering Using Genetic Algorithm-Based Self-Organising Map." Advanced Materials Research 1115 (July 2015): 573–77. http://dx.doi.org/10.4028/www.scientific.net/amr.1115.573.

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This paper presents a comparative study of clustering using Artificial Intelligence (AI) techniques. There are 3 methods to be compared, two methods are pure method, called Self Organising Map (SOM) which is branch of Artificial Neural Network (ANN) and Genetic Algorithm (GA), while one method is hybrid between GA and SOM, called GA-based SOM. SOM is one of the most popular method for cluster analysis. SOM will group objects based on the nearest distance between object and updateable cluster centres. However, there are disadvantages of SOM. Solution quality is depend on initial cluster centres that are generated randomly and cluster centres update algorithm is just based on a delta value without considering the searching direction. Basically, clustering case could be modelled as optimisation case. The objective function is to minimise total distance of all data to their cluster centre. Hence, GA has potentiality to be applied for clustering. Advantage of GA is it has multi searching points in finding the solution and stochastic movement from a phase to the next phase. Therefore, possibility of GA to find global optimum solution will be higher. However, there is still some possibility of GA just find near-optimum solution. The advantage of SOM is the smooth iterative procedure to improve existing cluster centres. Hybridisation of GA and SOM believed could provide better solution. In this study, there are 2 data sets used to test the performance of the three techniques. The study shows that when the solution domain is very wide then SOM and GA-based SOM perform better compared to GA while when the solution domain is not very wide then GA performs better.

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Kahwash, Fadi, Basel Barakat, Ahmad Taha, Qammer H. Abbasi, and Muhammad Ali Imran. "Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study." Energies 14, no. 21 (October 29, 2021): 7084. http://dx.doi.org/10.3390/en14217084.

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This study focuses on improving the sustainability of electrical supply in the healthcare system in the UK, to contribute to current efforts made towards the 2050 net-zero carbon target. As a case study, we propose a grid-connected hybrid renewable energy system (HRES) for a hospital in the south-east of England. Electrical consumption data were gathered from five wards in the hospital for a period of one year. PV-battery-grid system architecture was selected to ensure practical execution through the installation of PV arrays on the roof of the facility. Selection of the optimal system was conducted through a novel methodology combining multi-objective optimisation and data forecasting. The optimisation was conducted using a genetic algorithm with two objectives (1) minimisation of the levelised cost of energy and (2) CO2 emissions. Advanced data forecasting was used to forecast grid emissions and other cost parameters at two year intervals (2023 and 2025). Several optimisation simulations were carried out using the actual and forecasted parameters to improve decision making. The results show that incorporating forecasted parameters into the optimisation allows to identify the subset of optimal solutions that will become sub-optimal in the future and, therefore, should be avoided. Finally, a framework for choosing the most suitable subset of optimal solutions was presented.

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10

LIU, YONGGUO, XIAORONG PU, YIDONG SHEN, ZHANG YI, and XIAOFENG LIAO. "CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM." International Journal on Artificial Intelligence Tools 16, no. 06 (December 2007): 919–34. http://dx.doi.org/10.1142/s021821300700362x.

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In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.

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11

Indira, K., and S. Kanmani. "Mining association rules using hybrid genetic algorithm and particle swarm optimisation algorithm." International Journal of Data Analysis Techniques and Strategies 7, no. 1 (2015): 59. http://dx.doi.org/10.1504/ijdats.2015.067701.

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12

Kala, Rahul, Anupam Shukla, and Ritu Tiwari. "Robotic path planning using hybrid genetic algorithm particle swarm optimisation." International Journal of Information and Communication Technology 4, no. 2/3/4 (2012): 89. http://dx.doi.org/10.1504/ijict.2012.048756.

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13

Ding, Lei, Yong Jun Luo, Yang Yang Wang, Zheng Li, and Bing Yin Yao. "Improved Method of Hybrid Genetic Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 4014–17. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4014.

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On account of low convergence of the traditional genetic algorithm in the late,a hybrid genetic algorithm based on conjugate gradient method and genetic algorithm is proposed.This hybrid algorithm takes advantage of Conjugate Gradient’s certainty, but also the use of genetic algorithms in order to avoid falling into local optimum, so it can quickly converge to the exact global optimal solution. Using Two test functions for testing, shows that performance of this hybrid genetic algorithm is better than single conjugate gradient method and genetic algorithm and have achieved good results.

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14

Tang, Xin Lai, and Xiao Rong Wang. "Text Summarization Using Hybrid Parallel Genetic Algorithm." Advanced Materials Research 271-273 (July 2011): 154–57. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.154.

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This paper proposes a special Chinese automatic summarization method based on Concept-Obtained and Improved K-means Algorithm. The idea of our approach is to obtain concepts of words based on HowNet, and use concept as feature, instead of word. We use conceptual vector space model and Improved K-means Algorithm to form a summarization. Experimental results indicate a clear superiority of the proposed method over the traditional method under the proposed evaluation scheme.

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Wang, Zheng, Yuze Sun, Xiaopeng Yang, and Shuai Li. "Hybrid optimisation method of improved genetic algorithm and IFT for linear thinned array." Journal of Engineering 2019, no. 20 (October 1, 2019): 6457–60. http://dx.doi.org/10.1049/joe.2019.0296.

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16

Maheshwar, Keshav Kaushik, and Vikram Arora. "A Hybrid Data Clustering Using Firefly Algorithm Based Improved Genetic Algorithm." Procedia Computer Science 58 (2015): 249–56. http://dx.doi.org/10.1016/j.procs.2015.08.018.

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17

Abdulrahim, Hassan K., and Fuad N. Alasfour. "Multi-Objective Optimisation of hybrid MSF RO desalination system using Genetic Algorithm." International Journal of Exergy 7, no. 3 (2010): 387. http://dx.doi.org/10.1504/ijex.2010.031991.

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18

Cheng, Lin. "Optimisation of surrounding layout of enterprise building using an improved genetic algorithm." International Journal of Information and Communication Technology 16, no. 3 (2020): 275. http://dx.doi.org/10.1504/ijict.2020.10027486.

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Cheng, Lin. "Optimisation of surrounding layout of enterprise building using an improved genetic algorithm." International Journal of Information and Communication Technology 16, no. 3 (2020): 275. http://dx.doi.org/10.1504/ijict.2020.106320.

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20

Tabakov, Pavel Y. "Multi-dimensional design optimisation of laminated structures using an improved genetic algorithm." Composite Structures 54, no. 2-3 (November 2001): 349–54. http://dx.doi.org/10.1016/s0263-8223(01)00109-x.

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21

Pinthong, Panuwat, Ashim Das Gupta, Mukand Singh Babel, and Sutat Weesakul. "Improved Reservoir Operation Using Hybrid Genetic Algorithm and Neurofuzzy Computing." Water Resources Management 23, no. 4 (July 29, 2008): 697–720. http://dx.doi.org/10.1007/s11269-008-9295-z.

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22

Ji, Wei Dong, and Ke Qi Wang. "An Improved Hybrid Genetic Algorithm and Performance Study." Advanced Materials Research 482-484 (February 2012): 95–98. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.95.

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Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

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Wang, K., A. Salhi, and E. S. Fraga. "Process design optimisation using embedded hybrid visualisation and data analysis techniques within a genetic algorithm optimisation framework." Chemical Engineering and Processing: Process Intensification 43, no. 5 (May 2004): 657–69. http://dx.doi.org/10.1016/j.cep.2003.01.001.

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Leow, Edmund Kwong Wei, Binh P. Nguyen, and Matthew Chin Heng Chua. "Robo-advisor using genetic algorithm and BERT sentiments from tweets for hybrid portfolio optimisation." Expert Systems with Applications 179 (October 2021): 115060. http://dx.doi.org/10.1016/j.eswa.2021.115060.

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Esmailzadeh, Ebrahim, and Fereydoon Diba. "Components sizing optimisation of hybrid electric heavy duty truck using multi-objective genetic algorithm." International Journal of Heavy Vehicle Systems 27, no. 3 (2020): 387. http://dx.doi.org/10.1504/ijhvs.2020.10030667.

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Diba, Fereydoon, and Ebrahim Esmailzadeh. "Components sizing optimisation of hybrid electric heavy duty truck using multi-objective genetic algorithm." International Journal of Heavy Vehicle Systems 27, no. 3 (2020): 387. http://dx.doi.org/10.1504/ijhvs.2020.108734.

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Kumari, V. S. R., and P. R. Kumar. "Classification of cardiac arrhythmia using hybrid genetic algorithm optimisation for multi-layer perceptron neural network." International Journal of Biomedical Engineering and Technology 20, no. 2 (2016): 132. http://dx.doi.org/10.1504/ijbet.2016.074199.

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28

Hoseini, Pourya, and Mahrokh G. Shayesteh. "Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing." Digital Signal Processing 23, no. 3 (May 2013): 879–93. http://dx.doi.org/10.1016/j.dsp.2012.12.011.

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Nivas, M. L. Brabin, and T. Prabaharan. "Multi-Objective Criteria in Hybrid Flow Shop Scheduling Using Improved Genetic Algorithm." Applied Mathematics & Information Sciences 11, no. 2 (March 1, 2017): 537–44. http://dx.doi.org/10.18576/amis/110225.

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30

Nhleko, Adeodatus S., and Cuthbert Musingwini. "Optimisation of Three-Dimensional Stope Layouts Using a Dual Interchange Algorithm for Improved Value Creation." Minerals 12, no. 5 (April 19, 2022): 501. http://dx.doi.org/10.3390/min12050501.

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Optimisation of three-dimensional (3D) underground stope layouts is a computationally complex process since it involves the modelling of many variables and constraints. As the number of variables and constraints increases to reflect the actual mining practice, the model complexity and solution time tend to increase exponentially, making the optimisation problem intractable. Metaheuristic approaches have therefore been used predominantly to solve the problem, but do not guarantee ‘true’ optimality. To minimise this limitation, a dual interchange algorithm (DIA) was developed by combining the strengths of two metaheuristic generic algorithms, namely the particle swarm optimisation (PSO) and genetic algorithm (GA). The DIA performance was compared to that of the Mineable Shape Optimizer (MSO) on four different design scenarios. The DIA generated stope layout economic values (SLEV) for three scenarios which were 0.3%, 3.4%, and 8.3% higher than for MSO under fixed and variable stope width conditions, while MSO produced a SLEV which was 9.7% higher than the DIA for a fixed stope width. This paper demonstrates that the DIA is a novel way of solving 3D optimisation of stope layouts under variable stope widths as encountered in actual mining practice.

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Marichelvam, M. K., and T. Prabaharan. "Solving realistic industrial scheduling problems using a multi-objective improved hybrid particle swarm optimisation algorithm." International Journal of Operational Research 23, no. 1 (2015): 94. http://dx.doi.org/10.1504/ijor.2015.068752.

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Ascoli, Davide, Giorgio Vacchiano, Renzo Motta, and Giovanni Bovio. "Building Rothermel fire behaviour fuel models by genetic algorithm optimisation." International Journal of Wildland Fire 24, no. 3 (2015): 317. http://dx.doi.org/10.1071/wf14097.

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A method to build and calibrate custom fuel models was developed by linking genetic algorithms (GA) to the Rothermel fire spread model. GA randomly generates solutions of fuel model parameters to form an initial population. Solutions are validated against observations of fire rate of spread via a goodness-of-fit metric. The population is selected for its best members, crossed over and mutated within a range of model parameter values, until a satisfactory fitness is reached. We showed that GA improved the performance of the Rothermel model in three published custom fuel models for litter, grass and shrub fuels (root mean square error decreased by 39, 19 and 26%). We applied GA to calibrate a mixed grass–shrub fuel model, using fuel and fire behaviour data from fire experiments in dry heathlands of Southern Europe. The new model had significantly lower prediction error against a validation dataset than either standard or custom fuel models built using average values of inventoried fuels, and predictions of the Fuel Characteristics Classification System. GA proved a useful tool to calibrate fuel models and improve Rothermel model predictions. GA allows exploration of a continuous space of fuel parameters, making fuel model calibration computational effective and easily reproducible, and does not require fuel sampling. We suggest GA as a viable method to calibrate custom fuel models in fire modelling systems based on the Rothermel model.

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Dahiya, Tripti, and Deepika Garg. "Reliability optimization using hybrid genetic and particle swarm optimization algorithm." Yugoslav Journal of Operations Research 32, no. 4 (2022): 439–52. http://dx.doi.org/10.2298/yjor220316020d.

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Redundancy-allocation problem i.e. RAP is among the reliability optimization problems which make use of non-linear programming method to improve the reliability of complex system. The objective of this research paper is reliability optimization through the application of Genetic Algorithm i.e. GA and Hybrid Genetic & Particle Swarm Optimization (H-GAPSO) on a RAP. Certain shortcomings have been seen when results are obtained by application of single algorithms. In order to get rid of these shortcomings, HGA-PSO is introduced where attractive properties of GA and PSO are combined. This hybrid method makes use of iterative process of GA after obtaining initial best population from PSO. Comparative Analysis of results of GA and H-GAPSO is done with respect to reliability and computation (CPU) time and it is observed that H-GAPSO improved system reliability up to maximum by 63.10%. MATLprogramming has been used for computation of results from GA and HGA-PSO algorithms.

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Hu, Fei Hu, Ji Ze Zhang, Bei Long Ma, and Lu Lu Liu. "Bi-Objective Power Dispatch on Micro-Grid System Using Improved Genetic Algorithms." Advanced Materials Research 614-615 (December 2012): 1738–43. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1738.

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In this paper, a micro-grid power dispatch network is built up, and a real-number-coded genetic algorithm is adapted and proposed to solve the bi-objective dispatch problem, which is to minimize both fuel cost and production cost simultaneously. By using hybrid factors, we turn the two objectives into a single one, and then solve it by the improved genetic algorithm. Test results show the efficiency of the proposed algorithm.

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Zivkovic, Miodrag, Milan Tair, Venkatachalam K, Nebojsa Bacanin, Štěpán Hubálovský, and Pavel Trojovský. "Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification." PeerJ Computer Science 8 (April 29, 2022): e956. http://dx.doi.org/10.7717/peerj-cs.956.

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The research proposed in this article presents a novel improved version of the widely adopted firefly algorithm and its application for tuning and optimising XGBoost classifier hyper-parameters for network intrusion detection. One of the greatest issues in the domain of network intrusion detection systems are relatively high false positives and false negatives rates. In the proposed study, by using XGBoost classifier optimised with improved firefly algorithm, this challenge is addressed. Based on the established practice from the modern literature, the proposed improved firefly algorithm was first validated on 28 well-known CEC2013 benchmark instances a comparative analysis with the original firefly algorithm and other state-of-the-art metaheuristics was conducted. Afterwards, the devised method was adopted and tested for XGBoost hyper-parameters optimisation and the tuned classifier was tested on the widely used benchmarking NSL-KDD dataset and more recent USNW-NB15 dataset for network intrusion detection. Obtained experimental results prove that the proposed metaheuristics has significant potential in tackling machine learning hyper-parameters optimisation challenge and that it can be used for improving classification accuracy and average precision of network intrusion detection systems.

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Wang, Chao, Qiuliang Wang, Hui Huang, Shousen Song, Yinming Dai, and Fanping Deng. "Electromagnetic optimization design of a HTS magnet using the improved hybrid genetic algorithm." Cryogenics 46, no. 5 (May 2006): 349–53. http://dx.doi.org/10.1016/j.cryogenics.2005.08.007.

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DIOŞAN, LAURA, and MIHAI OLTEAN. "EVOLVING THE UPDATE STRATEGY OF THE PARTICLE SWARM OPTIMISATION ALGORITHMS." International Journal on Artificial Intelligence Tools 16, no. 01 (February 2007): 87–109. http://dx.doi.org/10.1142/s0218213007003217.

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A complex model for evolving the update strategy of a Particle Swarm Optimisation (PSO) algorithm is described in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The Evolved PSO algorithm is compared to several human-designed PSO algorithms by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the Evolved PSO algorithm performs similarly and sometimes even better than the Standard approaches for the considered problems. The Evolved PSO is highly scalable (regarding the size of the problem's input), being able to solve problems having different dimensions.

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Alaoui, Nail, Arwa Mashat, Amel Baha Houda Adamou-Mitiche, Lahcène Mitiche, Aicha Djalab, Sara Daoudi, and Lakhdar Bouhamla. "Impulse Noise Removal Based on Hybrid Genetic Algorithm." Traitement du Signal 38, no. 4 (August 31, 2021): 1245–51. http://dx.doi.org/10.18280/ts.380436.

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In this paper, we introduce a new method, impulse noise removal based on hybrid genetic algorithm (INRHGA) to remove impulse noise at different noise densities of noise while preserving the main features of the image. The proposed approach merges the genetic algorithm and methods for filtering images that are combined into the population as essential solutions to create a developed and improved population. A set of individuals is developed into a number of iterations using factors of crossover and mutation. Our method develops a group of images instead of a set of parameters from the filters. We then introduced some of the concepts and steps of it. The proposed algorithm is compared with some image denoising algorithm. By using Peak Signal to Noise Ratio (PSNR), structural similarity (SSIM). For example, for Lenna image with 60% salt and pepper noise density, PSNR, SSIM results of AMF, MDBUTMFG and NAFSM methods are 20,39/ 28.74/ 29.85 and 0.5679/ 0.8312/ 0.8818 respectively, while PSNR, SSIM results of the proposed algorithm are 29.92 and 0.8838, respectively. Experimental results indicate that INRHGA is very effective and visually comparable with the above-mentioned methods at different levels of noise.

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Rostami, Sahar Mojaver. "Portfolio optimisation based on minimum total risk acceptance level and its components using improved genetic algorithm." International Journal of Computational Systems Engineering 4, no. 4 (2018): 248. http://dx.doi.org/10.1504/ijcsyse.2018.095587.

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Rostami, Sahar Mojaver. "Portfolio optimisation based on minimum total risk acceptance level and its components using improved genetic algorithm." International Journal of Computational Systems Engineering 4, no. 4 (2018): 248. http://dx.doi.org/10.1504/ijcsyse.2018.10016542.

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Wang, Hao, and Shunhuai Chen. "An Approach to Ship Deck Arrangement Optimization Problem Using an Improved Multiobjective Hybrid Genetic Algorithm." Mathematical Problems in Engineering 2021 (August 27, 2021): 1–24. http://dx.doi.org/10.1155/2021/8784923.

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Ship deck arrangement design is about determining the positions and dimensions of arranged objects. This paper presents the mathematical model for the ship deck arrangement optimization problem statement and how the individual’s objective and constraint functions are computed. Moreover, an improved multiobjective hybrid genetic algorithm is redesigned to solve this complex nondeterministic problem and generate a set of diverse and rational deck arrangements in the early stage of ship design. An adaptive crossover operator and a novel topological replace operator invoked in this algorithm are described. Finally, the proposed algorithm is tested on a main deck arrangement optimization of an underwater detection ship. In the validation tests, the proposed algorithm is compared to the standard NSGA-II to determine its ability to produce a set of diverse and rational deck arrangements. Subsequently, the performance tests are used to determine the ability of the algorithm to work with the highly constrained arrangement problems and the efficiency of the adaptive crossover and topological replace operators.

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Al-Janan, Dony Hidayat, and Tung-Kuan Liu. "Path optimization of CNC PCB drilling using hybrid Taguchi genetic algorithm." Kybernetes 45, no. 1 (January 11, 2016): 107–25. http://dx.doi.org/10.1108/k-03-2015-0069.

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Purpose – In this study, the hybrid Taguchi genetic algorithm (HTGA) was used to optimize the computer numerical control-printed circuit boards drilling path. The optimization was performed by searching for the shortest route for the drilling path. The number of feasible solutions is exponentially related to the number of hole positions. The paper aims to discuss these issues. Design/methodology/approach – Therefore, a traveling cutting tool problem (TCP), which is similar to the traveling salesman problem, was used to evaluate the drilling path; this evaluation is considered an NP-hard problem. In this paper, an improved genetic algorithm embedded in the Taguchi method and a neighbor search method are proposed for improving the solution quality. The classical TCP problems proposed by Lim et al. (2014) were used for validating the performance of the proposed algorithm. Findings – Results showed that the proposed algorithm outperforms a previous study in robustness and convergence speed. Originality/value – The HTGA has not been used for optimizing the drilling path. This study shows that the HTGA can be applied to complex problems.

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Li, Z. Q., X. Liu, L. S. Duan, and L. Liu. "An improved hybrid genetic algorithm for holes machining path optimization using helical milling operation." Journal of Physics: Conference Series 1798, no. 1 (February 1, 2021): 012035. http://dx.doi.org/10.1088/1742-6596/1798/1/012035.

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Azamipour, Vahid, Mehdi Assareh, and Georg Martin Mittermeir. "An improved optimization procedure for production and injection scheduling using a hybrid genetic algorithm." Chemical Engineering Research and Design 131 (March 2018): 557–70. http://dx.doi.org/10.1016/j.cherd.2017.11.022.

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Fatemi Aghda, Seyed Ali, and Mahdi Mirfakhraei. "Improved routing in dynamic environments with moving obstacles using a hybrid Fuzzy-Genetic algorithm." Future Generation Computer Systems 112 (November 2020): 250–57. http://dx.doi.org/10.1016/j.future.2020.05.024.

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Berchiolli, Guarda, Walsh, and Pesyridis. "Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation." Applied Sciences 9, no. 13 (June 30, 2019): 2679. http://dx.doi.org/10.3390/app9132679.

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In a previous paper [1], a preliminary design methodology was proposed for the design of an axial turbine, replacing a conventional radial turbine used in automotive turbochargers, to achieve improved transient response, due to the intrinsically lower moment of inertia. In this second part of the work, the focus is on the optimisation of this preliminary design to improve on the axial turbine efficiency using a genetic algorithm in order to make the axial turbine a more viable proposition for turbocharger turbine application. The implementation of multidisciplinary design optimisation is essential to the aerodynamic shape optimisation of turbocharger turbines, as changes in blade geometry lead to variations in both structural and aerodynamics performance. Due to the necessity to have multiple design objectives and a significant number of variables, genetic algorithms seem to offer significant advantages. However, large generation sizes and simulation run times could result in extensively long periods of time for the optimisation to be completed. This paper proposes a dimensioning of a multi-objective genetic algorithm, to improve on a preliminary blade design in a reasonable amount of time. The results achieved a significant improvement on safety factor of both blades whilst increasing the overall efficiency by 2.55%. This was achieved by testing a total of 399 configurations in just over 4 h using a cluster network, which equated to 2.73 days using a single computer.

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Zhan, Yong, Chang Hua Qiu, and Kai Xue. "A Hybrid Genetic Algorithm for Hybrid Flow Shop Scheduling with Load Balancing." Key Engineering Materials 392-394 (October 2008): 250–55. http://dx.doi.org/10.4028/www.scientific.net/kem.392-394.250.

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This paper considers the practical manufacturing environment of the hybrid flow shop (HFS) with non-identical machines in parallel. In order to significantly enhance the performance level of manufacturing, maintaining load balancing among parallel machines is very important. The aim of this paper is to minimize makespan with load balancing in a non-identical parallel machine environment by using hybrid genetic algorithm (HGA). In the HGA, the neighborhood search-based method is used together with genetic algorithm as local optimization method to balance the exploration and exploitation abilities. The representation of chromosome used in this paper is composed of two layers: allocation layer and sequencing layer, which can be encode and decoded easily. In generating initial population, a special constraint of load balancing between parallel machines is used to reduce the number of individuals. And particular crossover operation is used, which generates multiple offspring at a time, so that the efficiency of the algorithm can be well improved. At last, the proposed algorithm is tested on a benchmark, and numerical example shows good result.

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Li, Wentao, Yongqiang Hei, Jing Yang, and Xiaowei Shi. "Optimisation of non‐uniform time‐modulated conformal arrays using an improved non‐dominated sorting genetic‐II algorithm." IET Microwaves, Antennas & Propagation 8, no. 4 (March 2014): 287–94. http://dx.doi.org/10.1049/iet-map.2013.0240.

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Parinam, Sunita, Mukesh Kumar, Neelam Kumari, Vinod Karar, and Amit L. Sharma. "An improved optical parameter optimisation approach using Taguchi and genetic algorithm for high transmission optical filter design." Optik 182 (April 2019): 382–92. http://dx.doi.org/10.1016/j.ijleo.2018.12.189.

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Xu, Zhuo, Rui Wang, and Zhong Min Wang. "A Hybrid 2-Population Genetic Algorithm for the Job Shop Scheduling Problem." Advanced Materials Research 989-994 (July 2014): 2609–12. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2609.

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In this paper, an analysis of a hybrid two-population genetic algorithm (H2PGA) for the job shop scheduling problem is presented. H2PGA is composed of two populations that constitute of similar fit chromosomes. These two branches implement genetic operation separately using different evolutionary strategy and exchange excellent chromosomes using migration strategy which is acquired by experiments. Improved adaptive genetic algorithm (IAGA) and simulated annealing genetic algorithm (SAGA) are applied in two branches respectively. By integrating the advantages of two techniques, this algorithm has comparatively solved the two major problems with genetic algorithm which are low convergence velocity and potentially to be plunged into local optimum. Experimental results show that the H2PGA outperforms the other three methods for it has higher convergence velocity and higher efficiency.

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