Adaptive Decomposition-Based Evolutionary Algorithm for Many-Objective Optimization with Two-Stage Dual-Density Judgment

2022 ◽  
Author(s):  
Yongjun Sun ◽  
jiaqi liu ◽  
zujun liu
Keyword(s):  
Evolutionary Algorithm ◽  
Two Stage ◽  
Adaptive Decomposition

Two-stage evolutionary algorithm for dynamic multicast routing in mesh network

2008 ◽  
Vol 9 (6) ◽  
pp. 791-798
Author(s):  
Li Zhu ◽  
Zhi-shu Li ◽  
Liang-yin Chen ◽  
Yan-hong Cheng
Keyword(s):  
Evolutionary Algorithm ◽  
Multicast Routing ◽  
Mesh Network ◽  
Two Stage ◽  
Dynamic Multicast

scholarly journals A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization

2019 ◽  
Vol 23 (5) ◽  
pp. 748-761 ◽  
Author(s):  
Yanan Sun ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
Gary G. Yen
Keyword(s):  
Evolutionary Algorithm ◽  
Two Stage

scholarly journals A two-stage R2 indicator based evolutionary algorithm for many-objective optimization

2018 ◽  
Vol 67 ◽  
pp. 245-260 ◽  
Author(s):  
Fei Li ◽  
Ran Cheng ◽  
Jianchang Liu ◽  
Yaochu Jin
Keyword(s):  
Evolutionary Algorithm ◽  
Two Stage ◽  
R2 Indicator

Hybrid Evolutionary Optimization of Two-Stage Stochastic Integer Programming Problems: An Empirical Investigation

2009 ◽  
Vol 17 (4) ◽  
pp. 511-526 ◽  
Author(s):  
Thomas Tometzki ◽  
Sebastian Engell
Keyword(s):  
Evolutionary Algorithm ◽  
Empirical Investigation ◽  
Scale Up ◽  
Exact Algorithm ◽  
Integer Program ◽  
Mixed Integer ◽  
Two Stage ◽  
Integer Programming Problems ◽  
Problem Instances ◽  
Feasible Solutions

In this contribution, we consider decision problems on a moving horizon with significant uncertainties in parameters. The information and decision structure on moving horizons enables recourse actions which correct the here-and-now decisions whenever the horizon is moved a step forward. This situation is reflected by a mixed-integer recourse model with a finite number of uncertainty scenarios in the form of a two-stage stochastic integer program. A stage decomposition-based hybrid evolutionary algorithm for two-stage stochastic integer programs is proposed that employs an evolutionary algorithm to determine the here-and-now decisions and a standard mathematical programming method to optimize the recourse decisions. An empirical investigation of the scale-up behavior of the algorithms with respect to the number of scenarios exhibits that the new hybrid algorithm generates good feasible solutions more quickly than a state of the art exact algorithm for problem instances with a high number of scenarios.

scholarly journals Solving the problem of optimal design for a two-stage reducer by using a modified evolutionary algorithm

2020 ◽  
Vol 317 ◽  
pp. 01004
Author(s):  
Oleksandr Ustynenko ◽  
Oleksiy Bondarenko ◽  
Volodymyr Serykov
Keyword(s):  
Optimal Design ◽  
Evolutionary Algorithm ◽  
Specific Problem ◽  
Functional Limitations ◽  
Evolutionary Process ◽  
Design Parameters ◽  
Initial Population ◽  
Objective Functions ◽  
Two Stage ◽  
Test Points

The work is devoted to solving the problem of selecting optimal geometric parameters of gears of a two-stage cylindrical reducer using a modified evolutionary algorithm (EA). The statement of the problem is considered, design parameters, objective functions, limitations on design parameters are determined. This allowed us to propose a modification of EA. To generate the initial test points, it was proposed to use the LP-τ sequence, this allowed us to reduce the initial population of test points and bring EA closer to a truly “random” process. The scheme of the proposed algorithm is considered, which gives an idea of the sequence of operations that are carried out with populations of test points at each stage of the evolutionary process. The solution of the specific problem of selecting optimal parameters for a serial reducer is given. The input data, numerical and functional limitations are determined, the objective functions are formed. The results of the solution are shown in several presentation formats: tabular and graphical, which allows to qualitatively interpret and analyze the results. Conclusions are made about testing the proposed algorithm for solving a specific problem of optimal design. Further ways of improving this methodology are proposed.

An adaptive decomposition-based evolutionary algorithm for many-objective optimization

2019 ◽  
Vol 491 ◽  
pp. 204-222 ◽  
Author(s):  
Dong Han ◽  
Wenli Du ◽  
Wei Du ◽  
Yaochu Jin ◽  
Chunping Wu
Keyword(s):  
Evolutionary Algorithm ◽  
Adaptive Decomposition
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