scholarly journals An Approach to Ship Deck Arrangement Optimization Problem Using an Improved Multiobjective Hybrid Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Hao Wang ◽  
Shunhuai Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Early Stage ◽  
Hybrid Genetic Algorithm ◽  
Performance Tests ◽  
Problem Statement ◽  
Nsga Ii ◽  
Adaptive Crossover ◽  
Validation Tests ◽  
The Mathematical Model

Ship deck arrangement design is about determining the positions and dimensions of arranged objects. This paper presents the mathematical model for the ship deck arrangement optimization problem statement and how the individual’s objective and constraint functions are computed. Moreover, an improved multiobjective hybrid genetic algorithm is redesigned to solve this complex nondeterministic problem and generate a set of diverse and rational deck arrangements in the early stage of ship design. An adaptive crossover operator and a novel topological replace operator invoked in this algorithm are described. Finally, the proposed algorithm is tested on a main deck arrangement optimization of an underwater detection ship. In the validation tests, the proposed algorithm is compared to the standard NSGA-II to determine its ability to produce a set of diverse and rational deck arrangements. Subsequently, the performance tests are used to determine the ability of the algorithm to work with the highly constrained arrangement problems and the efficiency of the adaptive crossover and topological replace operators.

Research on Multi-Objective Transmission Planning in Electricity Markets

2012 ◽  
Vol 516-517 ◽  
pp. 1429-1432
Author(s):  
Yang Liu ◽  
Xu Liu ◽  
Feng Xian Cui ◽  
Liang Gao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Electricity Markets ◽  
Optimization Problem ◽  
Nsga Ii ◽  
Transmission Planning ◽  
Multi Objective ◽  
Complex Optimization ◽  
The Mathematical Model

Abstract. Transmission planning is a complex optimization problem with multiple deciding variables and restrictions. The mathematical model is non-linear, discrete, multi-objective and dynamic. It becomes complicated as the system grows. So the algorithm adopted affects the results of planning directly. In this paper, a fast non-dominated sorting genetic algorithm (NSGA-II) is employed. The results indicate that NSGA-II has some advantages compared to the traditional genetic algorithms. In transmission planning, NSGA-II is feasible, flexible and effective.

A New Transmission Network Planning Method with Consideration of Transmission Cost

2013 ◽  
Vol 316-317 ◽  
pp. 132-135
Author(s):  
Xu Liu ◽  
Cui Lian Tang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Problem ◽  
Transmission Network ◽  
Nsga Ii ◽  
Transmission Planning ◽  
Transmission Cost ◽  
Transmission Network Planning ◽  
Complex Optimization ◽  
The Mathematical Model

Transmission planning is a complex optimization problem with multiple deciding variables and restrictions. The mathematical model is non-linear, discrete, multi-objective and dynamic. It becomes complicated as the system grows. So the algorithm adopted affects the results of planning directly. In this paper, a fast non-dominated sorting genetic algorithm (NSGA-II) is employed. The results indicate that NSGA-II has some advantages compared to the traditional genetic algorithms. In transmission planning, NSGA-II is feasible, flexible and effective.

Solving a Two-stage Supply Chain Network Design Problem with Fixed Costs by a Hybrid Genetic Algorithm

2021 ◽  
Author(s):  
Ovidiu Cosma ◽  
Petrică C Pop ◽  
Cosmin Sabo
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Network Design ◽  
Design Problem ◽  
Optimization Problem ◽  
Hybrid Genetic Algorithm ◽  
Network Design Problem ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Fixed Costs

Abstract In this paper we investigate a particular two-stage supply chain network design problem with fixed costs. In order to solve this complex optimization problem, we propose an efficient hybrid algorithm, which was obtained by incorporating a linear programming optimization problem within the framework of a genetic algorithm. In addition, we integrated within our proposed algorithm a powerful local search procedure able to perform a fine tuning of the global search. We evaluate our proposed solution approach on a set of large size instances. The achieved computational results prove the efficiency of our hybrid genetic algorithm in providing high-quality solutions within reasonable running-times and its superiority against other existing methods from the literature.

scholarly journals Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

2014 ◽  
Vol 63 (3) ◽  
pp. 367-384 ◽  
Author(s):  
K. Pandiarajan ◽  
C.K. Babulal
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimization Problem ◽  
Pareto Optimal ◽  
Contingency Analysis ◽  
Nsga Ii ◽  
Generation Cost ◽  
Fuzzy Ranking ◽  
Network Overload ◽  
Line Overload

Abstract This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem

Optimization Design of Structure and Parameters for RBF Neural Network Using Hybrid Hierarchy Genetic Algorithm

2014 ◽  
Vol 951 ◽  
pp. 274-277 ◽  
Author(s):  
Xu Sheng Gan ◽  
Can Yang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Design ◽  
Rbf Neural Network ◽  
Hybrid Genetic Algorithm ◽  
Proposed Model ◽  
Crossover Probability ◽  
Ga Algorithm ◽  
The Hierarchical Structure ◽  
Adaptive Crossover

To improve the optimization design of Radial Basis Function (RBF) neural network, a RBF neural network based on a hybrid Genetic Algorithm (GA) is proposed. First the hierarchical structure and adaptive crossover probability is introduced into the traditional GA algorithm for the improvement, and then the hybrid GA algorithm is used to optimize the structure and parameters of the network. The simulation indicates that the proposed model has a good modeling performance.

Dynamic Optimization of a Three Translational Degrees of Freedom Parallel Robot Based on a Multi-Objective Genetic Algorithm

2015 ◽  
Vol 789-790 ◽  
pp. 723-734
Author(s):  
Xing Guo Lu ◽  
Ming Liu ◽  
Min Xiu Kong
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Optimization ◽  
Degrees Of Freedom ◽  
Optimization Problem ◽  
Parallel Robot ◽  
Nsga Ii ◽  
Multi Objective ◽  
Global Dynamic ◽  
Design Variables ◽  
Dynamic Fluctuation

This work tends to deal with the multi-objective dynamic optimization problem of a three translational degrees of freedom parallel robot. Two global dynamic indices are proposed as the objective functions for the dynamic optimization: the index of dynamic dexterity, the index describing the dynamic fluctuation effects. The length of the linkages and the circumradius of the platforms were chosen as the design variables. A multi-objective optimal design problem, including constrains on the actuating and passive joint angle limits and geometrical interference is then formulated to find the Pareto solutions for the robot in a desired workspace. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to solve the constrained nonlinear multi-objective optimization problem. The simulation results obtained shows that the robot can achieve better dynamic dexterity and less dynamic fluctuation simultaneously after the optimization.

Multi-Objective Optimization Design of Screw Conveyor Using Genetic Algorithm

2013 ◽  
Vol 732-733 ◽  
pp. 402-406
Author(s):  
Duan Yi Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Design ◽  
Optimization Problem ◽  
Machine Design ◽  
Screw Conveyor ◽  
Optimized Design ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Mathematical Model

The weight minimum and drive efficiency maxima1 of screw conveyor were considered as double optimizing objects in this paper. The mathematical model of the screw conveyor has been established based on the theory of the machine design, and the genetic algorithm was adopted to solving the multi-objective optimization problem. The results show that the mass of spiral shaft reduces 13.6 percent, and the drive efficiency increases 6.4 percent because of the optimal design based on genetic algorithm. The genetic algorithm application on the screw conveyor optimized design can provided the basis for designing the screw conveyor.

scholarly journals Multi-objective optimization of retaining wall using genetic algorithm

2021 ◽  
Vol 8 (1-2) ◽  
pp. 58-65
Author(s):  
Filip Dodigović ◽  
Krešo Ivandić ◽  
Jasmin Jug ◽  
Krešimir Agnezović
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Retaining Wall ◽  
Silty Sand ◽  
Embedment Depth ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Definition Of ◽  
The Cost

The paper investigates the possibility of applying the genetic algorithm NSGA-II to optimize a reinforced concrete retaining wall embedded in saturated silty sand. Multi-objective constrained optimization was performed to minimize the cost, while maximizing the overdesign factors (ODF) against sliding, overturning, and soil bearing resistance. For a given change in ground elevation of 5.0 m, the width of the foundation and the embedment depth were optimized. Comparing the algorithm's performance in the cases of two-objective and three objective optimizations showed that the number of objectives significantly affects its convergence rate. It was also found that the verification of the wall against the sliding yields a lower ODF value than verifications against overturning and soil bearing capacity. Because of that, it is possible to exclude them from the definition of optimization problem. The application of the NSGA-II algorithm has been demonstrated to be an effective tool for determining the set of optimal retaining wall designs.

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