Relevant bibliographies by topics / Improved Hybrid Optimisation using Genetic Algorithm

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Academic literature on the topic 'Improved Hybrid Optimisation using Genetic Algorithm'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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

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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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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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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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Dissertations / Theses on the topic "Improved Hybrid Optimisation using Genetic Algorithm"

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Brka, Adel. "Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2015. https://ro.ecu.edu.au/theses/1756.

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Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint.
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Saiprasad, Nithya. "Optimum sizing and triple bottom line analysis of integrating hybrid renewable energy systems into the micro-grid." Thesis, 2019. https://vuir.vu.edu.au/40010/.

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There have been growing concerns over global warming, and this has increased the awareness towards the reduction of Greenhouse gas (GHG) emissions. Many countries including Australia have signed the “Paris Agreement” to try and combat global climate change. This agreement aims to restrict global temperature rise under 2ºC above pre-industrial levels and further limit the temperature rise to 1.5ºC. In December 2018, the United Nations Climate Change conference was held in Katowice, Poland and a framework called the United Nations Framework Convention on Climate Change (COP24) was agreed upon to help in implementing regulations of 2015 Paris Agreement. The agreement also ensures boosting support to developing countries to counter this threat. In order to help the developing and the most vulnerable countries achieve these rather ambitious goals, this new framework will focus on technology, financial flows and capacity improvement. The global reliance on fossil fuels, which contributes approximately 80% of primary energy, has resulted in the rise of global temperatures. Several countries have begun to reduce their reliance on fossil fuels and thus GHG emissions, by shifting their focus towards Renewable Energy (RE). Thus, RE has become a “go to” energy source to solve the aforementioned global issues with a pronounced focus on the guiding energy policies Energy, economics and environment play a crucial role in ensuring the sustainability of a country. Adoption of RE would be the key to ensuring energy sustainability and also reducing the environmental impact, thus helping RE to reach the citizens. Having acknowledged these global challenges and thus relying on RE for the energy needs, sustainability can be achieved by modernising the present micro-grids by integrating RE into them. In order to integrate RE into the existing micro-grid, sizing of Distributed Energy Resources (DERs) using RE sources are investigated to improve their energy production mechanism and enhance the overall efficiency. There are several approaches to size the RE sources into a micro-grid. Two approaches are followed for sizing HRES based on analysing the electricity consumption of the area of interest relying on: (i) Hybrid Optimisation of Multiple Energy Resources (HOMER) software and (ii) improved Hybrid Optimisation using Genetic Algorithm (iHOGA) software. This study highlights the issues related to the optimal sizing of the DERs by investigating their use of the novel application in micro-grids, using both photovoltaic (PV), wind turbine (WT) as the RES for supplying power to the grid for residences and commercial building at Aralvaimozhi, India and Warrnambool, Australia. These two chosen locations are bestowed with good sunlight and wind. The average solar radiation in Warrnambool 4.16kWh/m2/day and annual average wind velocity 5.96m/s. The wind speed and the average solar radiation at Aralvaimozhi are 7.16m/s and 5.05kWh/m2/day respectively. Aralvaimozhi has been spotted as a potential wind farm location according to the Government of Tamil Nadu. India being a developing country and Australia, a developed country, their respective energy policies are scrutinised to understand their energy policies status. Suggestions to improve RE adoption by understanding the energy policies laid by other RE developed counties like Germany, USA, etc. have been conducted. Triple Bottom Line (TBL) analysis is conducted to understand the feasibility of adopting RE into a micro-grid. It focuses on the Techno-economic, environmental and social perspectives to understand the feasibility of RE adoption from the perspective of a developed country (Australia) versus a developing country (India). In this respect, a prototype model of the micro-grid is studied and used at Victoria University, Footscray Campus for various scenarios.
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Li, Jian-Ping. "Truss topology optimization using an improved species-conserving genetic algorithm." 2013. http://hdl.handle.net/10454/10309.

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The aim of this article is to apply and improve the species-conserving genetic algorithm (SCGA) to search multiple solutions of truss topology optimization problems in a single run. A species is defined as a group of individuals with similar characteristics and is dominated by its species seed. The solutions of an optimization problem will be selected from the found species. To improve the accuracy of solutions, a species mutation technique is introduced to improve the fitness of the found species seeds and the combination of a neighbour mutation and a uniform mutation is applied to balance exploitation and exploration. A real vector is used to represent the corresponding cross-sectional areas and a member is thought to be existent if its area is bigger than a critical area. A finite element analysis model was developed to deal with more practical considerations in modelling, such as the existence of members, kinematic stability analysis, and computation of stresses and displacements. Cross-sectional areas and node connections are decision variables and optimized simultaneously to minimize the total weight of trusses. Numerical results demonstrate that some truss topology optimization examples have many global and local solutions, different topologies can be found using the proposed algorithm on a single run and some trusses have smaller weights than the solutions in the literature.
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(8803250), Akshay Amarendra Kasture. "A power management strategy for a parallel through-the-road plug-in hybrid electric vehicle using genetic algorithm." Thesis, 2020.

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With the upsurge of greenhouse gas emissions and rapid depletion of fossil fuels, the pressure on the transportation industry to develop new vehicles with improved fuel economy without sacrificing performance is on the rise. Hybrid Electric Vehicles (HEVs), which employ an internal combustion engine as well as an electric motor as power sources, are becoming increasingly popular alternatives to traditional engine only vehicles. However, the presence of multiple power sources makes HEVs more complex. A significant task in developing an HEV is designing a power management strategy, defined as a control system tasked with the responsibility of efficiently splitting the power/torque demand from the separate energy sources. Five different types of power management strategies, which were developed previously, are reviewed in this work, including dynamic programming, equivalent consumption minimization strategy, proportional state-of-charge algorithm, regression modeling and long short term memory modeling. The effects of these power management strategies on the vehicle performance are studied using a simplified model of the vehicle. This work also proposes an original power management strategy development using a genetic algorithm. This power management strategy is compared to dynamic programming and several similarities and differences are observed in the results of dynamic programming and genetic algorithm. For a particular drive cycle, the implementation of the genetic algorithm strategy on the vehicle model leads to a vehicle speed profile that almost matches the original speed profile of that drive cycle.
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Book chapters on the topic "Improved Hybrid Optimisation using Genetic Algorithm"

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Chimatapu, R., H. Hagras, A. J. Starkey, and G. Owusu. "Enhancing Human Decision Making for Workforce Optimisation Using a Stacked Auto Encoder Based Hybrid Genetic Algorithm." In Lecture Notes in Computer Science, 63–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04191-5_5.

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Nguyen, Tung Linh, Ngoc Sang Dinh, Viet Anh Truong, Thanh Long Duong, Dao Huy Du, and Do Anh Tuan. "Enhancing Total Transfer Capability via Optimal Location of Energy Storage Systems Using a Hybrid Improved Min-Cut Algorithm and Genetic Algorithm." In Advances in Engineering Research and Application, 512–24. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22200-9_57.

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Shboul, Bashar, Ismail Al-Arfi, Stavros Michailos, Derek Ingham, Godfrey T. Udeh, Lin Ma, Kevin Hughes, and Mohamed Pourkashanian. "Multi-Objective Optimal Performance of a Hybrid CPSD-SE/HWT System for Microgrid Power Generation." In Applications of Nature-Inspired Computing in Renewable Energy Systems, 166–210. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8561-0.ch009.

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A new integrated hybrid solar thermal and wind-based microgrid power system is proposed. It consists of a concentrated parabolic solar dish Stirling engine, a wind turbine, and a battery bank. The electrical power curtailment is diminished, and the levelised cost of energy is significantly reduced. To achieve these goals, the present study conducts a dynamic performance analysis over one year of operation. Further, a multi-objective optimisation model based on a genetic algorithm is implemented to optimise the techno-economic performance. The MATLAB/Simulink® software was used to model the system, study the performance under various operating conditions, and optimise the proposed hybrid system. Finally, the model has been implemented for a specific case study in Mafraq, Jordan. The system satisfies a net power output of 1500 kWe. The developed model has been validated using published results. In conclusion, the obtained results reveal that the optimised model of the microgrid can substantially improve the overall efficiency and reduce the levelised cost of electricity.
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Karuppiah, Kalaivani, Uma Maheswari N., Balamurugan N., and Venkatesh R. "Diagnosis of Heart Disease Using Improved Genetic Algorithm-Based Naive Bayes Classifier." In Advances in Medical Technologies and Clinical Practice, 117–40. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5741-2.ch008.

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Heart disease is one of the most common diseases all over the world. The primary objective of this investigation is to diagnosis heart disease using hybrid classification based on NaN prediction and ANOVA test (NAN-ANOVA). The anticipated system comprises of two subsets: hybrid accelerated artificial bee colony and chicken swarm optimization algorithm (AABC-CSO) for effectual feature selection, followed by a classification technique with genetic algorithm based naive bayes classifier (GA-NBC). The first system in co-operates three stages: (i) loading the numerical value from the dataset (ii) evaluating the NaN value (iii) performing ANOVA test for efficient selection using AABC-CSO optimization algorithm. In second method, GA-NBC is proposed. The heart data set obtained from UCI machine repository, and was utilized for performing the computation. An accuracy of 61.0777%, sensitivity of 31.5868%, specificity of 67.8467%, precision of 17.9505, F-measure of 23.4050, G-mean of 46.6928 and loss of about 0.4480 was achieved according to the validation scheme.
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Singh, Varimna, L. Ganapathy, and Ashok K. Pundir. "An Improved Genetic Algorithm for Solving Multi Depot Vehicle Routing Problems." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 375–402. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch020.

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The classical Vehicle Routing Problem (VRP) tries to minimise the cost of dispatching goods from depots to customers using vehicles with limited carrying capacity. As a generalisation of the TSP, the problem is known to be NP-hard and several authors have proposed heuristics and meta-heuristics for obtaining good solutions. The authors present genetic algorithm-based approaches for solving the problem and compare the results with available results from other papers, in particular, the hybrid clustering based genetic algorithm. The authors find that the proposed methods give encouraging results on all these instances. The approach can be extended to solve multi depot VRPs with heterogeneous fleet of vehicles.
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Dumitrescu, Mihaela. "Using a Hybrid System Composed of Neural Networks and Genetic Algorithms for Financial Forecasting." In Asian Business and Management Practices, 55–62. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6441-8.ch005.

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The possibility of applying artificial neural networks in different areas determined the discovery of more complex structures. This chapter describes the characteristic aspects of using a back-propagation neural network algorithm in making financial forecasting improved by a different technology: genetic algorithms. These can help build an automatic artificial neural network by two adaptive processes: first, genetic search through the data entry window, the forecast horizon, network architecture space, and control parameters to select the best performers; second, back propagation learning in individual networks to evaluate the selected architectures. Thus, network performance population increases from generation to generation. This chapter also presents how genetic algorithms can be used both to find the best network architecture and to find the right combination of inputs, the best prediction horizon and the most effective weight. Finally, this study shows how the results obtained using these technologies can be applied to obtain decision support systems that can lead to increased performance in economic activity and financial projections.
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Puyalnithi, Thendral, and Madhuviswanatham Vankadara. "A Unified Feature Selection Model for High Dimensional Clinical Data Using Mutated Binary Particle Swarm Optimization and Genetic Algorithm." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 50–64. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch003.

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This article contends that feature selection is an important pre-processing step in case the data set is huge in size with many features. Once there are many features, then the probability of existence of noisy features is high which might bring down the efficiency of classifiers created out of that. Since the clinical data sets naturally having very large number of features, the necessity of reducing the features is imminent to get good classifier accuracy. Nowadays, there has been an increase in the use of evolutionary algorithms in optimization in feature selection methods due to the high success rate. A hybrid algorithm which uses a modified binary particle swarm optimization called mutated binary particle swarm optimization and binary genetic algorithm is proposed in this article which enhanced the exploration and exploitation capability and it has been a verified with proposed parameter called trade off factor through which the proposed method is compared with other methods and the result shows the improved efficiency of the proposed method over other methods.
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Bhaskar, Tarum, and Narasimha Kamath B. "Intrusion Detection Using Modern Techniques." In Information Security and Ethics, 1611–25. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-937-3.ch110.

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Intrusion detection system (IDS) is now becoming an integral part of the network security infrastructure. Data mining tools are widely used for developing an IDS. However, this requires an ability to find the mapping from the input space to the output space with the help of available data. Rough sets and neural networks are the best known data mining tools to analyze data and help solve this problem. This chapter proposes a novel hybrid method to integrate rough set theory, genetic algorithm (GA), and artificial neural network. Our method consists of two stages: First, rough set theory is applied to find the reduced dataset. Second, the results are used as inputs for the neural network, where a GA-based learning approach is used to train the intrusion detection system. The method is characterized not only by using attribute reduction as a pre-processing technique of an artificial neural network but also by an improved learning algorithm. The effectiveness of the proposed method is demonstrated on the KDD cup data.
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Bhaskar, Tarun, and Narasimha Kamath B. "Intrusion Detection Using Modern Techniques." In Artificial Neural Networks in Real-Life Applications, 314–31. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-902-1.ch015.

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Intrusion detection system (IDS) is now becoming an integral part of the network security infrastructure. Data mining tools are widely used for developing an IDS. However, this requires an ability to find the mapping from the input space to the output space with the help of available data. Rough sets and neural networks are the best known data mining tools to analyze data and help solve this problem. This chapter proposes a novel hybrid method to integrate rough set theory, genetic algorithm (GA), and artificial neural network. Our method consists of two stages: First, rough set theory is applied to find the reduced dataset. Second, the results are used as inputs for the neural network, where a GA-based learning approach is used to train the intrusion detection system. The method is characterized not only by using attribute reduction as a pre-processing technique of an artificial neural network but also by an improved learning algorithm. The effectiveness of the proposed method is demonstrated on the KDD cup data.
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Montoya-Torres, Jairo R., and Fabián Vargas-Nieto. "Solving a Bi-Criteria Hybrid Flowshop Scheduling Problem Occurring in Apparel Manufacturing." In Management Innovations for Intelligent Supply Chains, 214–34. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2461-0.ch012.

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This paper studies the problem of production scheduling in a company belonging to the apparel industry, where textile labels are manufactured through the process of thermal transfer. The problem is modelled as a flexible flowshop with two stages. The objectives are the maximisation of system productivity (or minimisation of makespan) and the minimisation of the number of production orders with late delivery. This paper proposes a scheduling procedure based on a bi-objective genetic algorithm. An experimental study was performed using real data from the enterprise. Since validation results showed the efficiency and effectiveness of the proposed procedure, a decision-aid tool is designed. The algorithm is implemented at the enterprise and allows improved key performance metrics.
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Conference papers on the topic "Improved Hybrid Optimisation using Genetic Algorithm"

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Tamine, L., and M. Boughanem. "Query optimisation using an improved genetic algorithm." In the ninth international conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/354756.354842.

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Zhao, Fengqiang, Guangqiang Li, Hongying Hu, Jialu Du, Chen Guo, and Tao Li. "A novel improved hybrid particle swarm optimisation based genetic algorithm for the solution to layout problems." In 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2014. http://dx.doi.org/10.1109/wcica.2014.7053570.

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Deny, J., A. Sivanesh Kumar, N. Ragupathi Muthu, and B. Perumal. "Hybrid routing algorithm for wireless sensor networks by using improved genetic algorithm." In 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2017. http://dx.doi.org/10.1109/itcosp.2017.8303112.

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S, Ranjith, and P. Jesu Jayarin. "Improved Localization Algorithm Using Hybrid Firefly Genetic Algorithm in Wireless Sensor Network." In 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2022. http://dx.doi.org/10.1109/icses55317.2022.9914368.

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Ali, H. Benkaci, A. Cheriti, M. Benslima, and A. Sandali. "PDM Control Optimisation Applied to an AC/AC Converter Using an Improved Genetic Algorithm." In 2006 Canadian Conference on Electrical and Computer Engineering. IEEE, 2006. http://dx.doi.org/10.1109/ccece.2006.277820.

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Zhou, D., S. Gao, R. A. Abd-Alhameed, C. Zhang, M. S. Alkhambashi, and J. D. Xu. "Design and optimisation of compact hybrid quadrifilar helical-spiral antenna in GPS applications using Genetic Algorithm." In 2012 6th European Conference on Antennas and Propagation (EuCAP). IEEE, 2012. http://dx.doi.org/10.1109/eucap.2012.6206315.

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Satapathy, Suresh Chandra, Jvr Murthy, P. V. G. D. Prasada Reddy, Venkatesh Katari, Satish Malireddi, and V. N. K. Srujan Kollisetty. "An Efficient Hybrid Algorithm for Data Clustering Using Improved Genetic Algorithm and Nelder Mead Simplex Search." In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.183.

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"HYBRID APPROACH FOR IMPROVED PARTICLE SWARM OPTIMIZATION USING ADAPTIVE PLAN SYSTEM WITH GENETIC ALGORITHM." In International Conference on Evolutionary Computation Theory and Applications. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003626202670272.

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Trabia, Mohamed B. "A Hybrid Fuzzy Simplex Genetic Algorithm." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/dac-14231.

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Abstract Nelder and Mead Simplex (NMS) algorithm is an effective nonlinear programming technique. Trabia and Lu (1999) recently presented a novel algorithm, Fuzzy Simplex (FS), which improved the efficiency of Nelder and Mead Simplex by using fuzzy logic to determine the orientation and size of the simplex. While Fuzzy Simplex algorithm can be successfully used to search a wide variety of functions, it suffers, as other simplex algorithms, from its dependence on the initial guess and the original simplex size. This paper addresses this problem by combining the Fuzzy Simplex with Genetic Algorithm (GA) in a hybrid algorithm. Standard test problems are used to evaluate the efficiency of the algorithm. The algorithm is also applied successfully to several engineering design problems. The Hybrid GA Fuzzy Simplex algorithm generally results in a faster convergence.
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Panizo, A., G. Bello-Orgaz, A. Ortega, and D. Camacho. "Community finding in dynamic networks using a genetic algorithm improved via a hybrid immigrants scheme." In Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018). WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813273238_0076.

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Reports on the topic "Improved Hybrid Optimisation using Genetic Algorithm"

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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0739.

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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.
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