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Selection Method of Multi-Objective Problems Using Genetic Algorithm in Motion Plan of AUV 被引量:3
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作者 ZHANG Ming-jun , ZHENG Jin-xing , ZHANG Jing College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001 ,China College of Computer and Information Science, Harbin Engineering University, Harbin 150001 , China 《Journal of Marine Science and Application》 2002年第1期81-86,共6页
To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as... To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as an object. A changing weight value method is put forward and a selection formula is modified. Some experiments were implemented on an AUV, TwinBurger. The results shows that this method is effective and feasible. 展开更多
关键词 AUV multi objective optimization genetic algorithm selection method
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Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm 被引量:12
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作者 Chandan Guria Kiran K Goli Akhilendra K Pathak 《Petroleum Science》 SCIE CAS CSCD 2014年第1期97-110,共14页
A multi-objective optimization of oil well drilling has been carried out using a binary coded elitist non-dominated sorting genetic algorithm.A Louisiana offshore field with abnormal formation pressure is considered f... A multi-objective optimization of oil well drilling has been carried out using a binary coded elitist non-dominated sorting genetic algorithm.A Louisiana offshore field with abnormal formation pressure is considered for optimization.Several multi-objective optimization problems involving twoand three-objective functions were formulated and solved to fix optimal drilling variables.The important objectives are:(i) maximizing drilling depth,(ii) minimizing drilling time and (iii) minimizing drilling cost with fractional drill bit tooth wear as a constraint.Important time dependent decision variables are:(i) equivalent circulation mud density,(ii) drill bit rotation,(iii) weight on bit and (iv) Reynolds number function of circulating mud through drill bit nozzles.A set of non-dominated optimal Pareto frontier is obtained for the two-objective optimization problem whereas a non-dominated optimal Pareto surface is obtained for the three-objective optimization problem.Depending on the trade-offs involved,decision makers may select any point from the optimal Pareto frontier or optimal Pareto surface and hence corresponding values of the decision variables that may be selected for optimal drilling operation.For minimizing drilling time and drilling cost,the optimum values of the decision variables are needed to be kept at the higher values whereas the optimum values of decision variables are at the lower values for the maximization of drilling depth. 展开更多
关键词 Drilling performance rate of penetration abnormal pore pressure genetic algorithm multi-objective optimization
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Sequencing Mixed-model Production Systems by Modified Multi-objective Genetic Algorithms 被引量:5
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作者 WANG Binggang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2010年第5期537-546,共10页
As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers.However,these two problems should be considered simul... As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers.However,these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly systems.By far,little research effort is devoted to sequencing problems for mixed-model fabrication/assembly systems.This paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate buffers.Two objectives are considered simultaneously:minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly system.The integrated optimization framework,mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are presented.Since the above problems are non-deterministic polynomial-hard(NP-hard),a modified multi-objective genetic algorithm is proposed for solving the models,in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward,new selection,crossover and mutation operators are designed,and Pareto ranking method and sharing function method are employed to evaluate the individuals' fitness.The feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing algorithm.The sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm. 展开更多
关键词 mixed-model production system SEQUENCING parallel machine BUFFERS multi-objective genetic algorithm multi-objective simulated annealing algorithm
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Multi-objective optimization of membrane structures based on Pareto Genetic Algorithm 被引量:7
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作者 伞冰冰 孙晓颖 武岳 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2010年第5期622-630,共9页
A multi-objective optimization method based on Pareto Genetic Algorithm is presented for shape design of membrane structures from a structural view point.Several non-dimensional variables are defined as optimization v... A multi-objective optimization method based on Pareto Genetic Algorithm is presented for shape design of membrane structures from a structural view point.Several non-dimensional variables are defined as optimization variables,which are decision factors of shapes of membrane structures.Three objectives are proposed including maximization of stiffness,maximum uniformity of stress and minimum reaction under external loads.Pareto Multi-objective Genetic Algorithm is introduced to solve the Pareto solutions.Consequently,the dependence of the optimality upon the optimization variables is derived to provide guidelines on how to determine design parameters.Moreover,several examples illustrate the proposed methods and applications.The study shows that the multi-objective optimization method in this paper is feasible and efficient for membrane structures;the research on Pareto solutions can provide explicit and useful guidelines for shape design of membrane structures. 展开更多
关键词 membrane structures multi-objective optimization Pareto solutions multi-objective genetic algorithm
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Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades 被引量:27
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作者 王珑 王同光 罗源 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2011年第6期739-748,共10页
The non-dominated sorting genetic algorithm (NSGA) is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an exa... The non-dominated sorting genetic algorithm (NSGA) is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines. 展开更多
关键词 wind turbine multi-objective optimization Pareto-optimal solution non-dominated sorting genetic algorithm (NSGA)-II
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Hydraulic Optimization of a Double-channel Pump's Impeller Based on Multi-objective Genetic Algorithm 被引量:11
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作者 ZHAO Binjuan WANG Yu +2 位作者 CHEN Huilong QIU Jing HOU Duohua 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2015年第3期634-640,共7页
Computational fluid dynamics(CFD) can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to impro... Computational fluid dynamics(CFD) can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm(MOGA) and artificial neural networks(ANN) for a double-channel pump's impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of vortex and intensity of vortex decreases in the whole flow channel. This paper provides a promising tool to solve the hydraulic optimization problem of pumps' impellers. 展开更多
关键词 double-channel pump's impeller multi-objective genetic algorithm artificial neural network computational fluid dynamics(CFD) UNI
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Multi-objective Collaborative Optimization for Scheduling Aircraft Landing on Closely Spaced Parallel Runways Based on Genetic Algorithms 被引量:1
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作者 Zhang Shuqin Jiang Yu Xia Hongshan 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2016年第4期502-509,共8页
A scheduling model of closely spaced parallel runways for arrival aircraft was proposed,with multi-objections of the minimum flight delay cost,the maximum airport capacity,the minimum workload of air traffic controlle... A scheduling model of closely spaced parallel runways for arrival aircraft was proposed,with multi-objections of the minimum flight delay cost,the maximum airport capacity,the minimum workload of air traffic controller and the maximum fairness of airlines′scheduling.The time interval between two runways and changes of aircraft landing order were taken as the constraints.Genetic algorithm was used to solve the model,and the model constrained unit delay cost of the aircraft with multiple flight tasks to reduce its delay influence range.Each objective function value or the fitness of particle unsatisfied the constrain condition would be punished.Finally,one domestic airport hub was introduced to verify the algorithm and the model.The results showed that the genetic algorithm presented strong convergence and timeliness for solving constraint multi-objective aircraft landing problem on closely spaced parallel runways,and the optimization results were better than that of actual scheduling. 展开更多
关键词 air transportation runway scheduling closely spaced parallel runways genetic algorithm multi-objections
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Multi-Objective Optimization Using Genetic Algorithms of Multi-Pass Turning Process 被引量:1
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作者 Abdelouahhab Jabri Abdellah El Barkany Ahmed El Khalfi 《Engineering(科研)》 2013年第7期601-610,共10页
In this paper we present a multi-optimization technique based on genetic algorithms to search optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Tow objecti... In this paper we present a multi-optimization technique based on genetic algorithms to search optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Tow objective functions are simultaneously optimized under a set of practical of machining constraints, the first objective function is cutting cost and the second one is the used tool life time. The proposed model deals multi-pass turning processes where the cutting operations are divided into multi-pass rough machining and finish machining. Results obtained from Genetic Algorithms method are presented in Pareto frontier graphic;this technique helps us in decision making process. An example is presented to illustrate the procedure of this technique. 展开更多
关键词 genetic algorithms Mutli-objective Optimization TURNING Process MACHINING
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A Hybrid Parallel Multi-Objective Genetic Algorithm for 0/1 Knapsack Problem 被引量:3
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作者 Sudhir B. Jagtap Subhendu Kumar Pani Ganeshchandra Shinde 《Journal of Software Engineering and Applications》 2011年第5期316-319,共4页
In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to ... In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non- Parallel MOGAs) may fail to solve such intractable problem in a reasonable amount of time. The proposed hybrid model will combine the best attribute of island and Jakobovic master slave models. We conduct an extensive experimental study in a multi-core system by varying the different size of processors and the result is compared with basic parallel model i.e., master-slave model which is used to parallelize NSGA-II. The experimental results confirm that the hybrid model is showing a clear edge over master-slave model in terms of processing time and approximation to the true Pareto front. 展开更多
关键词 multi-objective genetic algorithm PARALLEL Processing Techniques NSGA-II 0/1 KNAPSACK Problem TRIGGER MODEL CONE Separation MODEL Island MODEL
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MULTI OBJECTIVE OPTIMIZATION USING GENETIC ALGORITHM WITH LOCAL SEARCH
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作者 戴晓晖 李敏强 寇纪淞 《Transactions of Tianjin University》 EI CAS 1998年第2期31-35,共5页
In this paper,we propose a hybrid algorithm for finding a set of non dominated solutions of a multi objective optimization problem.In the proposed algorithm,a local search procedure is applied to each solution gener... In this paper,we propose a hybrid algorithm for finding a set of non dominated solutions of a multi objective optimization problem.In the proposed algorithm,a local search procedure is applied to each solution generated by genetic operations.The aim of the proposed algorithm is not to determine a single final solution but to try to find all the non dominated solutions of a multi objective optimization problem.The choice of the final solution is left to the decision makers preference.High search ability of the proposed algorithm is demonstrated by computer simulation. 展开更多
关键词 multi objective genetic algorithm Pareto set local search
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The Use of Multi-Objective Genetic Algorithm Based Approach to Create Ensemble of ANN for Intrusion Detection
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作者 Gulshan Kumar Krishan Kumar 《International Journal of Intelligence Science》 2012年第4期115-127,共13页
Due to our increased dependence on Internet and growing number of intrusion incidents, building effective intrusion detection systems are essential for protecting Internet resources and yet it is a great challenge. In... Due to our increased dependence on Internet and growing number of intrusion incidents, building effective intrusion detection systems are essential for protecting Internet resources and yet it is a great challenge. In literature, many researchers utilized Artificial Neural Networks (ANN) in supervised learning based intrusion detection successfully. Here, ANN maps the network traffic into predefined classes i.e. normal or specific attack type based upon training from label dataset. However, for ANN-based IDS, detection rate (DR) and false positive rate (FPR) are still needed to be improved. In this study, we propose an ensemble approach, called MANNE, for ANN-based IDS that evolves ANNs by Multi Objective Genetic algorithm to solve the problem. It helps IDS to achieve high DR, less FPR and in turn high intrusion detection capability. The procedure of MANNE is as follows: firstly, a Pareto front consisting of a set of non-dominated ANN solutions is created using MOGA, which formulates the base classifiers. Subsequently, based upon this pool of non-dominated ANN solutions as base classifiers, another Pareto front consisting of a set of non-dominated ensembles is created which exhibits classification tradeoffs. Finally, prediction aggregation is done to get final ensemble prediction from predictions of base classifiers. Experimental results on the KDD CUP 1999 dataset show that our proposed ensemble approach, MANNE, outperforms ANN trained by Back Propagation and its ensembles using bagging & boosting methods in terms of defined performance metrics. We also compared our approach with other well-known methods such as decision tree and its ensembles using bagging & boosting methods. 展开更多
关键词 ENSEMBLE CLASSIFIERS INTRUSION DETECTION System INTRUSION DETECTION multi-objective genetic algorithm
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Two Multi-Objective Genetic Algorithms for Finding Optimum Design of an I-beam
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作者 Ali Khazaee Hossein Miar Naimi 《Engineering(科研)》 2011年第10期1054-1061,共8页
Many engineering design problems are characterized by presence of several conflicting objectives. This requires efficient search of the feasible design region for optimal solutions which simultaneously satisfy multipl... Many engineering design problems are characterized by presence of several conflicting objectives. This requires efficient search of the feasible design region for optimal solutions which simultaneously satisfy multiple design objectives. Genetic algorithm optimization (GAO) is a powerful search technique with faster convergence rates than traditional evolutionary algorithms. This paper applies two GAO-based approaches to multi-objective engineering design and finds design variables through the feasible space. To demonstrate the utility of the proposed methods, the multi-objective design of an I-beam will be presented. 展开更多
关键词 genetic algorithm multi-objective I-BEAM OPTIMIZATION
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A New Genetic Algorithm Applied to Multi-Objectives Optimal of Upgrading Infrastructure in NGWN
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作者 Dac-Nhuong Le Nhu Gia Nguyen +1 位作者 Dac Binh Ha Vinh Trong Le 《Communications and Network》 2013年第3期223-231,共9页
A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and... A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, we propose a new genetic algorithm has double population to solve Multi-Objectives Optimal of Upgrading Infrastructure (MOOUI) problem in NGWN. We modeling network topology for MOOUI problem has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. Our objective function is the sources to concentrators connectivity cost as well as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. We generate two populations satisfy constraints and combine them to build solutions and evaluate the performance of my algorithm with data randomly generated. Numerical results show that our algorithm is a promising approach to solve this problem. 展开更多
关键词 multi-objectives Optimal NEXT Generation Wireless NETWORK NETWORK Design Capacity Planning genetic algorithm Two-populations
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Optimal Test Points Selection Based on Multi-Objective Genetic Algorithm
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作者 Yong Zhang Xi-Xiang Chen Guan-Jun Liu Jing Qiu Shu-Ming Yang 《Journal of Electronic Science and Technology of China》 2009年第4期317-321,共5页
A new approach to select anoptimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table i... A new approach to select anoptimal set of test points is proposed. The described method uses fault-wise table and multi-objective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The selection of optimal test points is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method can efficiently and accurately find the optimal set of test points and is practical for large scale systems. 展开更多
关键词 Design for testability multi-objective genetic algorithm system testing test points selection.
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SELECTION OF OBJECTIVE FUNCTIONS AND APPLICATION OF GENETIC ALGORITHMS IN DAMPING DESIGN OF PIPE SYSTEM 被引量:1
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作者 ChenYanqiu FanQinsban ZhuZigen 《Acta Mechanica Solida Sinica》 SCIE EI 2003年第2期171-178,共8页
The vibration failure of pipe system of aeroengine seriously influences the safety of aircraft.Its damping design is determined by the selection of the design target,method and their feasibility.Five objective functio... The vibration failure of pipe system of aeroengine seriously influences the safety of aircraft.Its damping design is determined by the selection of the design target,method and their feasibility.Five objective functions for the vibration design of a pipeline or pipe system are introduced,namely,the frequency,amplitude,transfer ratio,curvature and deformation energy as options for the optimization process.The genetic algorithms(GA)are adopted as the opti- mization method,in which the selection of the adaptive genetic operators and the method of implementation of the GA process are crucial.The optimization procedure for all the above ob- jective functions is carried out using GA on the basis of finite element software-MSC/NASTRAN. The optimal solutions of these functions and the stress distribution on the structure are calculated and compared through an example,and their characteristics are analyzed.Finally we put forward two new objective functions,curvature and deformation energy for pipe system optimization.The calculations show that using the curvature as the objective function can reflect the case of minimal stress,and the optimization results using the deformation energy represent lesser and more uni- form stress distribution.The calculation results and process showed that the genetic algorithms can effectively implement damping design of engine pipelines and satisfy the efficient engineering design requirement. 展开更多
关键词 objective function genetic algorithms OPTIMIZATION pipe system
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A Multi-Objective Hybrid Genetic Based Optimization for External Beam Radiation 被引量:3
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作者 李国丽 宋钢 +2 位作者 吴宜灿 张建 王群京 《Plasma Science and Technology》 SCIE EI CAS CSCD 2006年第2期234-236,共3页
A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective property of inverse planning. It is based on hybrid adaptive genetic algorithm which combines the simulated an... A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective property of inverse planning. It is based on hybrid adaptive genetic algorithm which combines the simulated annealing, uses adaptive crossover and mutation, and adopts niched tournament selection. The result of the test calculation demonstrates that an excellent converging speed can be achieved using this approach. 展开更多
关键词 inverse planning multi-objective optimization genetic algorithm HYBRID
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A Genetic Algorithm for Single Machine Scheduling with Fuzzy Processing Time and Multiple Objectives
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作者 吴超超 顾幸生 《Journal of Donghua University(English Edition)》 EI CAS 2004年第3期185-189,共5页
In this paper, by considering the fuzzy nature of the data in real-life problems, single machine scheduling problems with fuzzy processing time and multiple objectives are formulated and an efficient genetic algorithm... In this paper, by considering the fuzzy nature of the data in real-life problems, single machine scheduling problems with fuzzy processing time and multiple objectives are formulated and an efficient genetic algorithm which is suitable for solving these problems is proposed. As illustrative numerical examples, twenty jobs processing on a machine is considered. The feasibility and effectiveness of the proposed method have been demonstrated in the simulation. 展开更多
关键词 SCHEDULING single machine genetic algorithms fuzzy processing time multiple objectives
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PI Multi-Objective Genetic for LFC Based Different Wind Penetration
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作者 Gaber El-Saady El-Nobi A. Ibrahim A. A. Donkol 《Journal of Power and Energy Engineering》 2018年第7期76-91,共16页
Future energy descent systems will be expected to be controlled by the using of renewable power sources of which wind energy is one of the favorable sources. This paper treats with the implantation of genetic algorith... Future energy descent systems will be expected to be controlled by the using of renewable power sources of which wind energy is one of the favorable sources. This paper treats with the implantation of genetic algorithms for making the parameters needed for PID applied to interconnected thermal and hydraulic power systems at best use and most effective. Two-areas of hydraulic and thermal power systems with wind connected parallel to each one are considered to exemplify the effective parameter investigation. First hydraulic and thermal are connected with tie line with the wind connected parallel to hydraulic or thermal, and then disturbance was made at thermal power plant, then to hydraulic power plant. Simulations are performed aided by the integrated Simulink/Matlab environment taking into consideration the genetic optimization process. Multiple integral representations variables with different cost functions were considered in the search for the effective AGC parameters. The outcomes established by this paper shows the impact of the genetic algorithms for LFC about multiple areas connected power systems based on different wind power using in the tuning of such a process. 展开更多
关键词 PID WIND genetic multi-objective genetic algorithm LFC
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Multiple-Objective Optimization and Design of Series-Parallel Systems Using Novel Hybrid Genetic Algorithm Meta-Heuristic Approach
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作者 Essa Abrahim Abdulgader Saleem Thien-My Dao Zhaoheng Liu 《World Journal of Engineering and Technology》 2018年第3期532-555,共24页
In this study, we develop a new meta-heuristic-based approach to solve a multi-objective optimization problem, namely the reliability-redundancy allocation problem (RRAP). Further, we develop a new simulation process ... In this study, we develop a new meta-heuristic-based approach to solve a multi-objective optimization problem, namely the reliability-redundancy allocation problem (RRAP). Further, we develop a new simulation process to generate practical tools for designing reliable series-parallel systems. Because the?RRAP is an NP-hard problem, conventional techniques or heuristics cannot be used to find the optimal solution. We propose a genetic algorithm (GA)-based hybrid meta-heuristic algorithm, namely the hybrid genetic algorithm (HGA), to find the optimal solution. A simulation process based on the HGA is developed to obtain different alternative solutions that are required to generate application tools for optimal design of reliable series-parallel systems. Finally, a practical case study regarding security control of a gas turbine in the overspeed state is presented to validate the proposed algorithm. 展开更多
关键词 multi-objective Optimization Reliability-Redundancy ALLOCATION OVERSPEED Gas TURBINE Hybrid genetic algorithm
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Three-Objective Programming with Continuous Variable Genetic Algorithm
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作者 Adugna Fita 《Applied Mathematics》 2014年第21期3297-3310,共14页
The subject area of multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually, there does not exist a single solution that optimizes all f... The subject area of multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually, there does not exist a single solution that optimizes all functions simultaneously;quite the contrary, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision made by taking elements of nondominated set as alternatives, which is given by analysts. Since it is important for the decision maker to obtain as much information as possible about this set, our research objective is to determine a well-defined and meaningful approximation of the solution set for linear and nonlinear three objective optimization problems. In this paper a continuous variable genetic algorithm is used to find approximate near optimal solution set. Objective functions are considered as fitness function without modification. Initial solution was generated within box constraint and solutions will be kept in feasible region during mutation and recombination. 展开更多
关键词 CHROMOSOME CROSSOVER HEURISTICS Mutation Optimization Population Ranking genetic algorithms multi-objective PARETO Optimal Solutions PARENT Selection
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