scholarly journals Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

scholarly journals Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

2018 ◽  
Author(s):  
Morteza Madhkhan ◽  
Mohammad Reza Baradaran
Keyword(s):  
Genetic Algorithm ◽  
Rate Of Convergence ◽  
Optimal Solution ◽  
Search Space ◽  
Initial Population ◽  
Generation Process ◽  
Improved Genetic Algorithm ◽  
Cross Sectional ◽  
Design Constraints ◽  
Matlab Programming

Genetic Algorithm (GA) is one of the most widely used optimization algorithms. This algorithm consists of five stages, namely population generation, crossover, mutation, evaluation, and selection. This study presents a modified version of GA called Improved Genetic Algorithm (IGA) for the optimization of steel frame designs. In the IGA, the rate of convergence to the optimal solution is increased by splitting the population generation process to two stages. In the first stage, the initial population is generated by random selection of members from among AISC W-shapes. The generated population is then evaluated in another stage, where the member that does not satisfy the design constraints are replaced with stronger members with larger cross sectional area. This process continues until all design constraints are satisfied. Through this process, the initial population will be improved intelligently so that the design constraints fall within the allowed range. For performance evaluation and comparison, the method was used to design and optimize 10-story and 24-story frames based on the LRFD method as per AISC regulations with the finite element method used for frame analysis. Structural analysis, design, and optimization were performed using a program written with MATLAB programming language. The results show that using the proposed method (IGA) for frame optimization reduces the volume of computations and increases the rate of convergence, thus allowing access to frame designs with near-optimal weights in only a few iterations. Using the IGA also limits the search space to the area of acceptable solutions.

An efficient intelligent decision method for bionic motion unmanned system

2021 ◽  
pp. 095965182110655
Author(s):  
Haipeng Chen ◽  
Wenxing Fu ◽  
Yuze Feng ◽  
Jia Long ◽  
Kang Chen
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Path Planning ◽  
Motion Control ◽  
Control Method ◽  
Improved Genetic Algorithm ◽  
Decision Method ◽  
Multiple Traveling Salesman Problem ◽  
Intelligent Decision ◽  
Unmanned System

In this article, we propose an efficient intelligent decision method for a bionic motion unmanned system to simulate the formation change during the hunting process of the wolves. Path planning is a burning research focus for the unmanned system to realize the formation change, and some traditional techniques are designed to solve it. The intelligent decision based on evolutionary algorithms is one of the famous path planning approaches. However, time consumption remains to be a problem in the intelligent decisions of the unmanned system. To solve the time-consuming problem, we simplify the multi-objective optimization as the single-objective optimization, which was regarded as a multiple traveling salesman problem in the traditional methods. Besides, we present the improved genetic algorithm instead of evolutionary algorithms to solve the intelligent decision problem. As the unmanned system’s intelligent decision is solved, the bionic motion control, especially collision avoidance when the system moves, should be guaranteed. Accordingly, we project a novel unmanned system bionic motion control of complex nonlinear dynamics. The control method can effectively avoid collision in the process of system motion. Simulation results show that the proposed simplification, improved genetic algorithm, and bionic motion control method are stable and effective.

scholarly journals A GPU-Enabled Compact Genetic Algorithm for Very Large-Scale Optimization Problems

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 758
Author(s):  
Andrea Ferigo ◽  
Giovanni Iacca
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Space ◽  
Large Scale Problems ◽  
Processing Power ◽  
Compact Genetic Algorithm ◽  
Estimation Of Distribution ◽  
Feasible Solutions ◽  
Compact Optimization

The ever-increasing complexity of industrial and engineering problems poses nowadays a number of optimization problems characterized by thousands, if not millions, of variables. For instance, very large-scale problems can be found in chemical and material engineering, networked systems, logistics and scheduling. Recently, Deb and Myburgh proposed an evolutionary algorithm capable of handling a scheduling optimization problem with a staggering number of variables: one billion. However, one important limitation of this algorithm is its memory consumption, which is in the order of 120 GB. Here, we follow up on this research by applying to the same problem a GPU-enabled “compact” Genetic Algorithm, i.e., an Estimation of Distribution Algorithm that instead of using an actual population of candidate solutions only requires and adapts a probabilistic model of their distribution in the search space. We also introduce a smart initialization technique and custom operators to guide the search towards feasible solutions. Leveraging the compact optimization concept, we show how such an algorithm can optimize efficiently very large-scale problems with millions of variables, with limited memory and processing power. To complete our analysis, we report the results of the algorithm on very large-scale instances of the OneMax problem.

Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem

2013 ◽  
Vol 365-366 ◽  
pp. 194-198 ◽  
Author(s):  
Mei Ni Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Simulation Software ◽  
Initial Population ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Search Speed ◽  
Vehicle Path

mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

Research on Vehicle Routing Problem Models and Algorithms of Batteries Logistics Distribution for Electric Vehicle

2013 ◽  
Vol 791-793 ◽  
pp. 1409-1414 ◽  
Author(s):  
Meng Wang ◽  
Kai Liu ◽  
Zhu Long Jiang
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problem ◽  
Distribution Model ◽  
Initial Population ◽  
Mutation Operator ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Short Period

The battery quick exchange mode is an effective solution to resolve the battery charging problem of electric vehicle. For the electric vehicle battery distribution network with the battery quick exchange mode, the distribution model and algorithm are researched; the general mathematical model to take delivery of the vehicle routing problem with time window (VRP-SDPTW) is established. By analyzing the relationship between the main variables, structure priority function of the initial population, a new front crossover operator, swap mutation operator and reverse mutation operator are designed, and an improved genetic algorithm solving VRP-SDPTW is constructed. The algorithm could overcome the traditional genetic algorithm premature convergence defects. The example shows that the improved genetic algorithm can be effective in the short period of time to obtain the satisfactory solution of the VRP-SDPTW.

Based on Improved Genetic Algorithm for Task Scheduling of Heterogeneous Multi-Core Processor

2014 ◽  
Vol 1030-1032 ◽  
pp. 1671-1675
Author(s):  
Yue Qiu ◽  
Jing Feng Zang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Experimental Results ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
High Quality ◽  
Multi Core Processor ◽  
Better Than

This paper puts forward an improved genetic scheduling algorithm in order to improve the execution efficiency of task scheduling of the heterogeneous multi-core processor system and give full play to its performance. The attribute values and the high value of tasks were introduced to structure the initial population, randomly selected a method with the 50% probability to sort for task of individuals of the population, thus to get high quality initial population and ensured the diversity of the population. The experimental results have shown that the performance of the improved algorithm was better than that of the traditional genetic algorithm and the HEFT algorithm. The execution time of tasks was reduced.

The Study of Optimal Fuzzy PID-Smith Control Based on Genetic Algorithm

2013 ◽  
Vol 313-314 ◽  
pp. 448-452
Author(s):  
Dian Ting Liu ◽  
Hai Xia Li
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Control Method ◽  
Order System ◽  
Membership Functions ◽  
Fuzzy Pid ◽  
Improved Genetic Algorithm ◽  
Simulation Results ◽  
The Stability ◽  
Better Than

In this paper, the improved genetic algorithm is applied to optimize the quantization factors and the scaling factors of fuzzy control, and the optimized rule table and membership functions is obtained according to certain performances. Then a kind of optimal fuzzy PID-Smith control method based on genetic algorithm is proposed and its simulation model is built in this paper, a second-order system is simulated and analyzed. The results show that requirements of deterministic performances of the new control method are better than the conventional methods through the simulation results in the stability, rapidity and robustness.

Application of an Improved Genetic Algorithm with the Search Space Compression in TSP

2013 ◽  
Vol 765-767 ◽  
pp. 687-689
Author(s):  
Yi Song ◽  
Ni Ni Wei
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Random Search ◽  
Search Space ◽  
Traveling Salesman ◽  
Improved Genetic Algorithm ◽  
Space Compression ◽  
Accelerate Convergence ◽  
The Traveling Salesman Problem ◽  
Exponential Relation

The Traveling Salesman Problem is a combinatorial optimization problem, the problem has been shown to belong to the NPC problem, the possible solution of Traveling Salesman Problem and the scale of the cities have the exponential relation, so the more bigger of the scale. In this paper, improve the search process of the genetic algorithm by introducing the idea is to compress the search space. The simulation results show that for solving the TSP, the algorithm can quickly obtain multiple high-quality solutions. It can reduce the blindness of random search and accelerate convergence of the algorithm.

scholarly journals An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yong Deng ◽  
Yang Liu ◽  
Deyun Zhou
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Traveling Salesman ◽  
Average Error ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Population Strategy ◽  
Series Of Experiments

A new initial population strategy has been developed to improve the genetic algorithm for solving the well-known combinatorial optimization problem, traveling salesman problem. Based on thek-means algorithm, we propose a strategy to restructure the traveling route by reconnecting each cluster. The clusters, which randomly disconnect a link to connect its neighbors, have been ranked in advance according to the distance among cluster centers, so that the initial population can be composed of the random traveling routes. This process isk-means initial population strategy. To test the performance of our strategy, a series of experiments on 14 different TSP examples selected from TSPLIB have been carried out. The results show that KIP can decrease best error value of random initial population strategy and greedy initial population strategy with the ratio of approximately between 29.15% and 37.87%, average error value between 25.16% and 34.39% in the same running time.

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