A Novel Strategy to Control Population Diversity and Convergence for Genetic Algorithm

2017 ◽  
pp. 362-369
Author(s):  
Dongyang Li ◽  
Weian Guo ◽  
Yanfen Mao ◽  
Lei Wang ◽  
Qidi Wu
Keyword(s):  
Genetic Algorithm ◽  
Population Diversity ◽  
Control Population ◽  
Novel Strategy

scholarly journals An Enhanced Genetic Algorithm for Parameter Estimation of Sinusoidal Signals

2020 ◽  
Vol 10 (15) ◽  
pp. 5110
Author(s):  
Chao Jiang ◽  
Pruthvi Serrao ◽  
Mingjie Liu ◽  
Chongdu Cho
Keyword(s):  
Genetic Algorithm ◽  
Fundamental Problem ◽  
Cloud Model ◽  
Real Life ◽  
Cuckoo Search ◽  
Population Diversity ◽  
Adaptive Mutation ◽  
Parameter Estimations ◽  
Mutation Strategy ◽  
Sinusoidal Signals

Estimating the parameters of sinusoidal signals is a fundamental problem in signal processing and in time-series analysis. Although various genetic algorithms and their hybrids have been introduced to the field, the problems pertaining to complex implementation, premature convergence, and accuracy are still unsolved. To overcome these drawbacks, an enhanced genetic algorithm (EGA) based on biological evolutionary and mathematical ecological theory is originally proposed in this study; wherein a prejudice-free selection mechanism, a two-step crossover (TSC), and an adaptive mutation strategy are designed to preserve population diversity and to maintain a synergy between convergence and search ability. In order to validate the performance, benchmark function-based studies are conducted, and the results are compared with that of the standard genetic algorithm (SGA), the particle swarm optimization (PSO), the cuckoo search (CS), and the cloud model-based genetic algorithm (CMGA). The results reveal that the proposed method outperforms the others in terms of accuracy, convergence speed, and robustness against noise. Finally, parameter estimations of real-life sinusoidal signals are performed, validating the superiority and effectiveness of the proposed method.

Multi-Operator Genetic Algorithm for Dynamic Optimization Problems

2017 ◽  
Vol 6 (3) ◽  
pp. 139
Author(s):  
Al-khafaji Amen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Population Diversity ◽  
Dynamic Optimization Problems ◽  
Uncertain Environments ◽  
Convergence Problem ◽  
Royal Road

<span lang="EN-US">Maintaining population diversity is the most notable challenge in solving dynamic optimization problems (DOPs). Therefore, the objective of an efficient dynamic optimization algorithm is to track the optimum in these uncertain environments, and to locate the best solution. In this work, we propose a framework that is based on multi operators embedded in genetic algorithms (GA) and these operators are heuristic and arithmetic crossovers operators. The rationale behind this is to address the convergence problem and to maintain the diversity. The performance of the proposed framework is tested on the well-known dynamic optimization functions i.e., OneMax, Plateau, Royal Road and Deceptive. Empirical results show the superiority of the proposed algorithm when compared to state-of-the-art algorithms from the literature.</span>

scholarly journals Genetic Algorithm for Optimizing Routing Design and Fleet Allocation of Freeway Service Overlapping Patrol

2018 ◽  
Vol 10 (11) ◽  
pp. 4120 ◽  
Author(s):  
Xiuqiao Sun ◽  
Jian Wang ◽  
Weitiao Wu ◽  
Wenjia Liu
Keyword(s):  
Genetic Algorithm ◽  
Population Diversity ◽  
Decision Makers ◽  
Incident Response ◽  
Matrix Encoding ◽  
Routing Design ◽  
Speed Up ◽  
Fleet Allocation ◽  
Patrol Routing ◽  
Elitist Strategy

The freeway service patrol problem involves patrol routing design and fleet allocation on freeways that would help transportation agency decision-makers when developing a freeway service patrols program and/or altering existing route coverage and fleet allocation. Based on the actual patrol process, our model presents an overlapping patrol model and addresses patrol routing design and fleet allocation in a single integrated model. The objective is to minimize the overall average incident response time. Two strategies—overlapping patrol and non-overlapping patrol—are compared in our paper. Matrix encoding is applied in the genetic algorithm (GA), and to maintain population diversity and avoid premature convergence, a niche strategy is incorporated into the traditional genetic algorithm. Meanwhile, an elitist strategy is employed to speed up the convergence. Using numerical experiments conducted based on data from the Sioux Falls network, we clearly show that: overlapping patrol strategy is superior to non-overlapping patrol strategy; the GA outperforms the simulated annealing (SA) algorithm; and the computational efficiency can be improved when LINGO software is used to solve the problem of fleet allocation.

Application of Dual-Adaptive Niched Genetic Algorithm in Optimal Design of Nuclear Power Components

2014 ◽  
Author(s):  
Cheng Wang ◽  
Chang-qi Yan ◽  
Jian-jun Wang ◽  
Lei Chen ◽  
Gui-jing Li
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Nuclear Power ◽  
Optimization Design ◽  
Population Diversity ◽  
Geometric Constraints ◽  
Local Optimum ◽  
Penalty Function Method ◽  
Benchmark Function ◽  
Nonlinear Constraint

Genetic algorithm (GA) has been widely applied in optimal design of nuclear power components. Simple genetic algorithm (SGA) has the defects of poor convergence accuracy and easily falling into the local optimum when dealing with nonlinear constraint optimization problem. To overcome these defects, an improved genetic algorithm named dual-adaptive niched genetic algorithm (DANGA) is designed in this work. The new algorithm adopts niche technique to enhance global search ability, which utilizes a sharing function to maintain population diversity. Dual-adaptation technique is developed to improve the global and local search capability at the same time. Furthermore, a new reconstitution operator is applied to the DANGA to handle the constraint conditions, which can avoid the difficulty of selecting punishment parameter when using the penalty function method. The performance of new algorithm is evaluated by optimizing the benchmark function. The volume optimization of the Qinshan I steam generator and the weight optimization of Qinshan I condenser, taking thermal-hydraulic and geometric constraints into consideration, is carried out by adopting the DANGA. The result of benchmark function test shows that the new algorithm is more effective than some traditional genetic algorithms. The optimization design shows obvious validity and can provide guidance for real engineering design.

User’s Preference Aggregation Based on Parallel Interactive Genetic Algorithms

2010 ◽  
Vol 34-35 ◽  
pp. 1159-1164 ◽  
Author(s):  
Yi Nan Guo ◽  
Yong Lin ◽  
Mei Yang ◽  
Shu Guo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Solution ◽  
Small Population ◽  
Population Diversity ◽  
Design System ◽  
Preference Aggregation ◽  
Ip Multicast ◽  
Interactive Genetic Algorithm ◽  
Interactive Genetic Algorithms

In traditional interactive genetic algorithms, high-quality optimal solution is hard to be obtained due to small population size and limited evolutional generations. Aming at above problems, a parallel interactive genetic algorithm based on knowledge migration is proposed. During the evolution, the number of the populations is more than one. Evolution information can be exchanged between every two populations so as to guide themselves evolution. In order to realize the freedom communication, IP multicast is adopted as the transfer protocol to find out the similar users instead of traditional TCP/IP communication mode. Taken the fashion evolutionary design system as test platform, the results indicate that the IP multicast-based parallel interactive genetic algorithm has better population diversity. It also can alleviate user fatigue and speed up the convergence.

Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm

2016 ◽  
pp. 349-377 ◽  
Author(s):  
Sandip Dey ◽  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik
Keyword(s):  
Genetic Algorithm ◽  
Chaotic Map ◽  
Population Diversity ◽  
Gray Level ◽  
The Novel ◽  
Image Thresholding ◽  
Gray Level Image ◽  
Quantum Operators ◽  
Pass Through ◽  
Gray Level Images

In this article, a genetic algorithm inspired by quantum computing is presented. The novel algorithm referred to as quantum inspired genetic algorithm (QIGA) is applied to determine optimal threshold of two gray level images. Different random chaotic map models exhibit the inherent interference operation in collaboration with qubit and superposition of states. The random interference is followed by three different quantum operators viz., quantum crossover, quantum mutation and quantum shifting produce population diversity. Finally, the intermediate states pass through the quantum measurement for optimization of image thresholding. In the proposed algorithm three evaluation metrics such as Brinks's, Kapur's and Pun's algorithms have been applied to two gray level images viz., Lena and Barbara. These algorithms have been applied in conventional GA and Han et al.'s QEA. A comparative study has been made between the proposed QIGA, Han et al.'s algorithm and conventional GA that indicates encouraging avenues of the proposed QIGA.

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