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.

The Optimal Design on Connection Order of Network in Hydraulic Manifold Block

2011 ◽  
Vol 201-203 ◽  
pp. 2190-2194
Author(s):  
Jun Jun Zhang ◽  
Ji Sheng Wang ◽  
Jiang Yong Wang ◽  
Gang Liu ◽  
Jie Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Genetic Manipulation ◽  
Optimal Algorithm ◽  
Single Point ◽  
Adaptive Mutation ◽  
Gene Encoding ◽  
Mutation Strategy ◽  
The Common

As one of the important questions in the design of hydraulic manifold block — connection order of network, give a solution based on genetic algorithm. Genetic algorithm is the common effective intelligent optimal algorithm and suitable for solving a large combinatorial optimal problems. Gene encoding of ordinal representation, single-point crossover strategy and adaptive mutation strategy are used in the design of genetic manipulation.

A Dynamic Genetic Algorithm for Clustering Problems

2013 ◽  
Vol 411-414 ◽  
pp. 1884-1893
Author(s):  
Yong Chun Cao ◽  
Ya Bin Shao ◽  
Shuang Liang Tian ◽  
Zheng Qi Cai
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Search Space ◽  
Adaptive Mutation ◽  
Data Sets ◽  
Data Set ◽  
Local Optima ◽  
Clustering Problems

Due to many of the clustering algorithms based on GAs suffer from degeneracy and are easy to fall in local optima, a novel dynamic genetic algorithm for clustering problems (DGA) is proposed. The algorithm adopted the variable length coding to represent individuals and processed the parallel crossover operation in the subpopulation with individuals of the same length, which allows the DGA algorithm clustering to explore the search space more effectively and can automatically obtain the proper number of clusters and the proper partition from a given data set; the algorithm used the dynamic crossover probability and adaptive mutation probability, which prevented the dynamic clustering algorithm from getting stuck at a local optimal solution. The clustering results in the experiments on three artificial data sets and two real-life data sets show that the DGA algorithm derives better performance and higher accuracy on clustering problems.

scholarly journals Multipopulation Genetic Algorithm Based on GPU for Solving TSP Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Boqun Wang ◽  
Hailong Zhang ◽  
Jun Nie ◽  
Jie Wang ◽  
Xinchen Ye ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Coarse Grained ◽  
Mutation Rates ◽  
Adaptive Mutation ◽  
Original Population ◽  
Elite Strategy ◽  
Mutation Strategy ◽  
Local Optimal Solution ◽  
Crossover Method

A GPU-based Multigroup Genetic Algorithm was proposed, which parallelized the traditional genetic algorithm with a coarse-grained architecture island model. The original population is divided into several subpopulations to simulate different living environments, thus increasing species richness. For each subpopulation, different mutation rates were adopted, and the crossover results were optimized by combining the crossover method based on distance. The adaptive mutation strategy based on the number of generations was adopted to prevent the algorithm from falling into the local optimal solution. An elite strategy was adopted for outstanding individuals to retain their superior genes. The algorithm was implemented with CUDA/C, combined with the powerful parallel computing capabilities of GPUs, which greatly improved the computing efficiency. It provided a new solution to the TSP problem.

Reactive Power Optimization Based on CAGA Algorithm

2012 ◽  
Vol 616-618 ◽  
pp. 2210-2213
Author(s):  
Li Jun Chen ◽  
Ran Ran Hai ◽  
Ya Hong Zhang ◽  
Gang Gang Xu
Keyword(s):  
Genetic Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Cloud Model ◽  
Optimal Solution ◽  
Population Diversity ◽  
Adaptive Genetic Algorithm ◽  
Reactive Power Optimization ◽  
Global Optimal ◽  
Crossover And Mutation

Reactive power optimization is a typical high-dimensional, nonlinear, discontinuous problem. Traditional Genetic algorithm(GA) exists precocious phenomenon and is easy to be trapped in local minima. To overcome this shortcoming, this article will introduce cloud model into Adaptive Genetic Algorithm (AGA), adaptively adjust crossover and mutation probability according to the X-condition cloud generator to use the randomness and stable tendency of droplets in cloud model. The article proposes the cloud adaptive genetic algorithm(CAGA) ,according to the theory, which probability values have both stability and randomness, so, the algorithm have both rapidity and population diversity. Considering minimum network loss as the objective function, make the simulation in standard IEEE 14 node system. The results show that the improved CAGA can achieve a better global optimal solution compared with GA and AGA.

Chaos Glowworm Swarm Optimization Algorithm Based on Cloud Model for Face Recognition

2020 ◽  
Vol 34 (12) ◽  
pp. 2056009
Author(s):  
Guangyu Zhou ◽  
Aijia Ouyang ◽  
Yuming Xu
Keyword(s):  
Face Recognition ◽  
Cloud Model ◽  
Cuckoo Search ◽  
Global Optimum ◽  
Convergence Speed ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Better Than

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures.

scholarly journals An improved Genetic Algorithm For Fuzzy Production PlanningProblems with Application

2015 ◽  
Vol 1 (3) ◽  
pp. 390
Author(s):  
Jalal Abdulkareem Sultan ◽  
Omar Ramzi Jasim ◽  
Sarmad Abdulkhaleq Salih
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Fundamental Problem ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Main Idea ◽  
Production Schedule ◽  
Improved Genetic Algorithm ◽  
Customer Service Level ◽  
Master Production Schedule

Production Planning or Master Production Schedule (MPS) is a key interface between marketing and manufacturing, since it links customer service directly to efficient use of production resources. Mismanagement of the MPS is considered as one of fundamental problem in operation and it can potentially lead to poor customer satisfaction.  In this paper, an improved Genetic Algorithm (IGA) is used to solving fuzzy multi-objective master production schedule (FMOMPS). The main idea is to integrate GA with local search operator. The FMOMPS was applied in the Cotton and medical gauzes plant in Mosul city. The application involves determine the gross requirements by demand forecasting using artificial neural networks. The IGA proved its efficiency in solving MPS problems compared with the genetic algorithm for fuzzy and non-fuzzy model, as the results clearly showed the ability of IGA to determine intelligently how much, when, and where the additional capacities (overtimes) are required such that the inventory can be reduced without affecting customer service level.

A solution method for treatment scheduling in rehabilitation hospitals with real-life requirements

2018 ◽  
Vol 30 (4) ◽  
pp. 367-386 ◽  
Author(s):  
Liyang Xiao ◽  
Mahjoub Dridi ◽  
Amir Hajjam El Hassani ◽  
Wanlong Lin ◽  
Hongying Fei
Keyword(s):  
Waiting Time ◽  
Programming Model ◽  
Search Algorithm ◽  
Real Life ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Mixed Integer ◽  
Solution Quality ◽  
Improved Cuckoo Search Algorithm ◽  
Rehabilitation Hospitals

Abstract In this study, we aim to minimize the total waiting time between successive treatments for inpatients in rehabilitation hospitals (departments) during a working day. Firstly, the daily treatment scheduling problem is formulated as a mixed-integer linear programming model, taking into consideration real-life requirements, and is solved by Gurobi, a commercial solver. Then, an improved cuckoo search algorithm is developed to obtain good quality solutions quickly for large-sized problems. Our methods are demonstrated with data collected from a medium-sized rehabilitation hospital in China. The numerical results indicate that the improved cuckoo search algorithm outperforms the real schedules applied in the targeted hospital with regard to the total waiting time of inpatients. Gurobi can construct schedules without waits for all the tested dataset though its efficiency is quite low. Three sets of numerical experiments are executed to compare the improved cuckoo search algorithm with Gurobi in terms of solution quality, effectiveness and capability to solve large instances.

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