scholarly journals The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Ismael Jannoud ◽  
Yousef Jaradat ◽  
Mohammad Z. Masoud ◽  
Ahmad Manasrah ◽  
Mohammad Alia
Keyword(s):  
Genetic Algorithm ◽  
Extension Problem ◽  
Genetic Operators ◽  
Network Stability ◽  
Stability Period ◽  
Truncation Selection ◽  
Roulette Wheel ◽  
Parent Selection ◽  
Selection Operators ◽  
Selection Operator

A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by 25.8% and data throughput by 26.86%. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.

Application of Genetic Algorithm on Optimal Pipeline Route Considering Complex Terrains and Obstacles

2018 ◽  
Author(s):  
Bingyuan Hong ◽  
Xiaoping Li ◽  
Yu Li ◽  
Jingjing Gao ◽  
Yanhong Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Complex Structure ◽  
Genetic Operators ◽  
Construction Costs ◽  
Gas Field ◽  
Proposed Model ◽  
Slope Factor ◽  
Total Investment ◽  
Selection Operators ◽  
Novel Model

Gathering network, which is usually characterized by various and complex structure, takes a large proportion of the overall construction cost of gas field. Optimization of pipeline routes is an effective way to reduce the investment. In this paper, a novel model for optimal route of pipeline considering complex terrains and obstacles is proposed and solved by Genetic Algorithm. Minimizing the total investment is the object of this model. Since the construction costs under different terrains are different, the distance factor Li, slope factor Di and elastic factor Si are introduced into the objective function to represent the length of the pipeline, the gradient of the pipeline, and the fluctuation of terrain. In addition, the performance of the model is verified by taking three typical situations of different terrains and obstacles as examples. The results illustrate that the proposed model can address the optimal design of pipeline routes in complex terrains. Moreover, the effects of different genetic operators on solutions are investigated, including three selection operators and two crossover operators. The study provides a guideline for designing pipeline routes in complex terrains and is also applicable to the analogous problem.

Research on the Genetic Algorithm Simulating Human Reproduction Mode and its Blending Application with Neural Network

2012 ◽  
Vol 532-533 ◽  
pp. 1785-1789
Author(s):  
Tai Shan Yan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Optimal Solution ◽  
Bp Algorithm ◽  
Human Reproduction ◽  
Genetic Operators ◽  
Reproduction Mode ◽  
Evolutionary Neural Network ◽  
Selection Operator

In this study, a genetic algorithm simulating human reproduction mode (HRGA) is proposed. The genetic operators of HRGA include selection operator, help operator, crossover operator and mutation operator. The sex feature, age feature and consanguinity feature of genetic individuals are considered. Two individuals with opposite sex can reproduce the next generation if they are distant consanguinity individuals and their age is allowable. Based on this genetic algorithm, an improved evolutionary neural network algorithm named HRGA-BP algorithm is formed. In HRGA-BP algorithm, HRGA is used firstly to evolve and design the structure, the initial weights and thresholds, the training ratio and momentum factor of neural network roundly. Then, training samples are used to search for the optimal solution by the evolutionary neural network. HRGA-BP algorithm is used in motor fault diagnosis. The illustrational results show that HRGA-BP algorithm is better than traditional neural network algorithms in both speed and precision of convergence, and its validity in fault diagnosis is proved.

scholarly journals Evolutionary Algorithm with Roulette-Tournament Selection for Solving Aquaculture Diet Formulation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Rosshairy Abd Rahman ◽  
Razamin Ramli ◽  
Zainoddin Jamari ◽  
Ku Ruhana Ku-Mahamud
Keyword(s):  
Evolutionary Algorithm ◽  
The Other ◽  
Roulette Wheel ◽  
Diet Formulation ◽  
Tournament Selection ◽  
Selection Operators ◽  
Selection For ◽  
Feasible Solutions ◽  
Selection Operator ◽  
Formulation Problem

The function of operators in an evolutionary algorithm (EA) is very crucial as the operators have a strong effect on the performance of the EA. In this paper, a new selection operator is introduced for a real valued encoding problem, which specifically exists in a shrimp diet formulation problem. This newly developed selection operator is a hybrid between two well-known established selection operators: roulette wheel and binary tournament selection. A comparison of the performance of the proposed operator and the other existing operator was made for evaluation purposes. The result shows that the proposed roulette-tournament selection is better in terms of its ability to provide many good feasible solutions when a population size of 30 is used. Thus, the proposed roulette-tournament is suitable and comparable to established selection for solving a real valued shrimp diet formulation problem. The selection operator can also be generalized to any problems related to EA.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

scholarly journals Research on Logistics Distribution Path Planning Based on Genetic Algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032013
Author(s):  
Shaokun Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulation Experiment ◽  
Time Window ◽  
Planning Model ◽  
Genetic Operators ◽  
Intelligent Algorithm ◽  
Total Cost ◽  
Logistics Distribution ◽  
Survey Results ◽  
Distribution Cost

Abstract In this paper, SF express company Jinzhou Guta District Pinganli business point as an example, to investigate its distribution, statistical analysis of the survey results, summed up the problems in logistics and distribution. Through the systematic study of the problem, a planning model with time window and with the objective of minimizing the total cost of distribution is established. At the same time, an intelligent algorithm for distribution path optimization - Genetic Algorithm (GA) is designed. Genetic algorithm is used to design chromosome coding methods and genetic operators for solving the planning model with the objective of minimizing the total cost of distribution. Finally, the simulation experiment is carried out. MATLAB software is used to solve the distribution route and the total driving distance of vehicles, and the distribution route with the goal of minimizing the total distribution cost is obtained.

scholarly journals Design of Water Distribution Networks using a Pseudo-Genetic Algorithm and Sensitivity of Genetic Operators

2013 ◽  
Vol 27 (12) ◽  
pp. 4149-4162 ◽  
Author(s):  
D. Mora-Melia ◽  
P. L. Iglesias-Rey ◽  
F. J. Martinez-Solano ◽  
V. S. Fuertes-Miquel
Keyword(s):  
Genetic Algorithm ◽  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Genetic Operators

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 1881-1886
Author(s):  
Cen Zeng ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
High Performance ◽  
Search Space ◽  
Area Coverage ◽  
Robotic Systems ◽  
Genetic Operators ◽  
A Algorithm ◽  
Coverage Path Planning ◽  
Search Spaces

Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made.This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms.The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.

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