scholarly journals Multi-weapon multi-target assignment based on hybrid genetic algorithm in uncertain environment

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090592
Author(s):  
Yang Zhao ◽  
Yifei Chen ◽  
Ziyang Zhen ◽  
Ju Jiang
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Hybrid Genetic Algorithm ◽  
Threat Assessment ◽  
Local Optimum ◽  
Grey Theory ◽  
Interval Numbers ◽  
Uncertain Environment ◽  
Mutation Operators ◽  
Target Assignment

The multi-weapon multi-target assignment is always an unavoidable problem in military field. It does make sense to find a proper assignment of weapons to targets which may help maximize the attack effect. In this article, as the information achieved from the battlefield is becoming more and more uncertain, a novel threat assessment method and target assignment algorithm are proposed against the background of unmanned aerial vehicles intelligent air combat. Specifically, with regard to the threat assessment issue, a possibility degree function based on grey theory is structured to further improve the grey analytic hierarchy process. It can transform the interval weight of threat factors into scalar-valued weight, with which the accuracy of threat assessment can be improved. Regarding the target assignment problem, combining with interval grey number, an improved hybrid genetic algorithm is developed. The improvements are mainly consisting of adaptive crossover and mutation operators which can help to find an approximate solution within certain time constraints. Meanwhile, the simulated annealing operation is incorporated to avoid local optimum and premature phenomenon. In addition, the selection operation and fitness function are also redesigned to handle the interval numbers. Simulation results demonstrate the effectiveness of our algorithm in completing the multi-objective weapon-target assignment under uncertain environment.

The Optimization Design of the Cross-Sectional Layout of an Umbilical Based on the Hybrid Genetic Algorithm

2021 ◽  
Author(s):  
Xu Yin ◽  
Zhixun Yang ◽  
Dongyan Shi ◽  
Jun Yan ◽  
Lifu Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cross Sections ◽  
Optimization Design ◽  
Mechanical Performance ◽  
Fitness Function ◽  
Hybrid Genetic Algorithm ◽  
Optimization Strategy ◽  
Cross Sectional ◽  
Great Probability ◽  
The Cross

Abstract The umbilical which consists of hydraulic tubes, electrical cables and optical cables is a key equipment in the subsea production system. Each components perform different physical properties, so different cross-sections will present different geometrical characteristic, carrying capacities, the cost and the ease of manufacture. Therefore, the cross-sectional layout design of the umbilical is a typical multi-objective optimization problem. A mathematical model of the cross-sectional layout considering geometric and mechanical properties is proposed, and the genetic algorithm is introduced to copy with the optimization model in this paper. A steepest descent operator is embedded into the basic genetic algorithm, while the appropriate fitness function and the selection operator are advanced. The optimization strategy of the cross-sectional layout based on the hybrid genetic algorithm is proposed with the fast convergence and the great probability for global optimization. Finally, the cross-section of an umbilical case is performed to obtain the optimal the cross-sectional layout. The geometric and mechanical performance of results are compared with the initial design, which verify the feasibility of the proposed algorithm.

Weighted K-Means Clustering Analysis Based on Improved Genetic Algorithm

2014 ◽  
Vol 511-512 ◽  
pp. 904-908 ◽  
Author(s):  
Tong Jie Zhang ◽  
Yan Cao ◽  
Xiang Wei Mu
Keyword(s):  
Genetic Algorithm ◽  
Clustering Analysis ◽  
Local Optimum ◽  
Speed Of Convergence ◽  
Improved Genetic Algorithm ◽  
Low Speed ◽  
Dynamic Mutation ◽  
Mutation Operators

An algorithm of weighted k-means clustering is improved in this paper, which is based on improved genetic algorithm. The importance of different contributors in the process of manufacture is not the same when clustering, so the weight values of the parameters are considered. Retaining the best individuals and roulette are combined to decide which individuals are chose to crossover or mutation. Dynamic mutation operators are used here to decrease the speed of convergence. Two groups of data are used to make comparisons among the three algorithms, which suggest that the algorithm has overcome the problems of local optimum and low speed of convergence. The results show that it has a better clustering.

Improved Method of Hybrid Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4014-4017
Author(s):  
Lei Ding ◽  
Yong Jun Luo ◽  
Yang Yang Wang ◽  
Zheng Li ◽  
Bing Yin Yao
Keyword(s):  
Genetic Algorithm ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Local Optimum ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Better Than

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.

scholarly journals Research on Damage Identification of Grid Structure Based on Genetic Algorithm

2019 ◽  
Vol 136 ◽  
pp. 03014
Author(s):  
Yu Wang ◽  
Menghong Wang ◽  
Huan Lu
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Damage Identification ◽  
Simulation Analysis ◽  
Fitness Function ◽  
Reduction Factor ◽  
Grid Structure ◽  
Identification Method ◽  
Stiffness Reduction ◽  
Mutation Operators

This paper proposes a genetic algorithm based damage identification method for grid structures. The genetic algorithm is used to process the modal information of the structure, and the damage identification of the truss structure is carried out. The stiffness reduction factor of the structural member is used as the optimization variable. The objective function is constructed according to the frequency and vibration mode, and the fitness function is established. The binary coding method is used to improve the crossover and mutation operators. In this paper, a grid structure model is used for numerical simulation analysis and verified by experiments. In the experimental stage, the grid structure is excited by hammering method, and the response data of each node and the modal information of the structure are obtained. Numerical simulation and experimental analysis show that the damage identification method based on genetic algorithm can effectively judge the location and extent of damage.

scholarly journals Penerapan Metode Hybrid Genetic Algorithm (GA) dan Pattern Search (PS) untuk Penjadwalan Mata Kuliah Universitas

2020 ◽  
Vol 9 (3) ◽  
pp. 201-212
Author(s):  
Fani Puspitasari ◽  
Parwadi Moengin
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Pattern Search ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Course Scheduling ◽  
Fitness Value ◽  
Optimum Solution ◽  
University Course

The problem of university course scheduling is a complicated job to do because of the many constraints that must be considered, such as the number of courses, the number of rooms available, the number of students, lecturer preferences, and time slots. The more courses that will be scheduled, the scheduling problem becomes more complex to solve. Therefore, it is necessary to set an automatic course schedule based on optimization method. The aim of this research is to gain an optimal solution in the form of schedule in order to decrease the number of clashed courses, optimize room utilization and consider the preferences of lecturer-course. In this research, a hybridization method of Genetic Algorithm (GA) and Pattern Search (PS) is investigated for solving university course scheduling problems. The main algorithm is GA to find the global optimum solution, while the PS algorithm is used to find the local optimum solution that is difficult to obtain by the GA method. The simulation results with 93 courses show that the Hybrid GA-PS method works better than does the GA method without hybrid, as evidenced by the better fitness value of the hybrid GA-PS method which is -3528.62 and 99.24% of the solutions achieved. While the GA method without hybrid is only able to reach a solution of around 65% and has an average fitness value of -3100.76.

scholarly journals Experimental Programming of Genetic Algorithms for the “Casse-Tête” Problem

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Search Space ◽  
Rank Test ◽  
Mutation Operators ◽  
Signed Rank ◽  
Significant Difference ◽  
Chromosome Representation ◽  
Wilcoxon Signed Rank Test ◽  
Signed Rank Test

The Casse-tête board puzzle consists of an n×n grid covered with n^2 tokens. m<n^2 tokens are deleted from the grid so that each row and column of the grid contains an even number of remaining tokens. The size of the search space is exponential. This study used a genetic algorithm (GA) to design and implement solutions for the board puzzle. The chromosome representation is a matrix of binary permutations. Variants for two crossover operators and two mutation operators were presented. The study experimented with and compared four possible operator combinations. Additionally, it compared GA and simulated annealing (SA)-based solutions, finding a 100% success rate (SR) for both. However, the GA-based model was more effective in solving larger instances of the puzzle than the SA-based model. The GA-based model was found to be considerably more efficient than the SA-based model when measured by the number of fitness function evaluations (FEs). The Wilcoxon signed-rank test confirms a significant difference among FEs in the two models (p=0.038).

scholarly journals An enhanced hybrid genetic algorithm for solving traveling salesman problem

2020 ◽  
Vol 18 (2) ◽  
pp. 1035
Author(s):  
Zeravan Arif Ali ◽  
Subhi Ahmed Rasheed ◽  
Nabeel No’man Ali
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Mutation Operators ◽  
Np Hard Problem ◽  
The Traveling Salesman Problem

<span>Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.</span>

Sequencing Mixed-Model Assembly Lines to Minimize the Variation of Parts Consumption by Hybrid Genetic Algorithms

2012 ◽  
Vol 566 ◽  
pp. 253-256
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Hybrid Genetic Algorithm ◽  
Initial Population ◽  
Assembly Lines ◽  
Mixed Model Assembly Lines ◽  
Mutation Operators ◽  
Sequencing Problems ◽  
Crossover And Mutation ◽  
Mixed Model Assembly

This paper is concerned about the sequencing problems in mixed-model assembly lines. The optimization objective is to minimizing the variation of parts consumption. The mathematical models are put forward. Since the problem is NP-hard, a hybrid genetic algorithm is newly-designed for solving the models. In this algorithm, the new method of forming the initial population is presented, the hybrid crossover and mutation operators are adopted, and moreover, the adaptive probability values for performing the crossover and mutation operations are used. The optimization performance is compared between the hybrid genetic algorithm and a genetic algorithm proposed in early published literature. The computational results show that satisfactory solutions can be obtained by the hybrid genetic algorithm and it performs better in terms of solution’s quality.

Intelligent Design of Internal Combustion Engine in Hybrid Genetic Algorithm

2010 ◽  
Vol 26-28 ◽  
pp. 186-189 ◽  
Author(s):  
Jiang Zhu ◽  
Ping Yuan Xi
Keyword(s):  
Genetic Algorithm ◽  
Internal Combustion Engine ◽  
Internal Combustion Engines ◽  
Combustion Engine ◽  
Hybrid Genetic Algorithm ◽  
Internal Combustion ◽  
Optimization Process ◽  
Operating Parameters ◽  
Local Optimum ◽  
Vehicle Engine

The basic parameters of internal combustion engines reflect the working performance and quality of the internal combustion engine. Therefore it is of great significance to design the operating parameters of vehicle engine by design optimization method. In this paper, hybrid genetic algorithm is adopted to optimize operating parameters of vehicle engine, so that optimization process was simplified and the global optimal solution is ensured reliably. Being satisfied with the heating loading, mechanical loading and the conditions of gas mixture of engine and boundary constraints, the optimization mathematical model is created which is to minimize the heating surface area of engine. Considering the problem of low efficiency and local optimum caused by traditional optimal methods, the hybrid Genetic Algorithm are adopted to solve the optimization model. So that the optimization process is simplified and global optimum is acquired reliably.

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