Multiemotional product color design using gray theory and nondominated sorting genetic algorithm‐III

2019 ◽  
Vol 45 (1) ◽  
pp. 142-155
Author(s):  
Man Ding ◽  
Wei Dong
Keyword(s):  
Genetic Algorithm ◽  
Color Design ◽  
Gray Theory ◽  
Nondominated Sorting

scholarly journals An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jing Xiao ◽  
Jing-Jing Li ◽  
Xi-Xi Hong ◽  
Min-Mei Huang ◽  
Xiao-Min Hu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Experimental Comparison ◽  
Software Project ◽  
Project Portfolio ◽  
Multiobjective Evolutionary Algorithms ◽  
Reference Distance ◽  
Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II)

2022 ◽  
Vol 204 ◽  
pp. 111999
Author(s):  
Hanting Wu ◽  
Yangrui Huang ◽  
Lei Chen ◽  
Yingjie Zhu ◽  
Huaizheng Li
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Nsga Ii ◽  
Sewer Pipes ◽  
Nondominated Sorting

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

scholarly journals CONSTRAINT ROBUST PORTFOLIO SELECTION BY MULTIOBJECTIVE EVOLUTIONARY GENETIC ALGORITHM

2012 ◽  
pp. 57-62
Author(s):  
S.K. MISHRA ◽  
G. PANDA ◽  
S. MEHER ◽  
R. MAJHI
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Selection ◽  
Critical Issue ◽  
Total Risk ◽  
Cardinality Constraints ◽  
Real World Data ◽  
Robust Portfolio ◽  
Total Profit ◽  
Non Gaussian ◽  
Nondominated Sorting

The problem of portfolio selection is a very challenging problem in computational finance and has received a lot of attention in last few decades. Selecting an asset and optimal weighting of it from a set of available assets is a critical issue for which the decision maker takes several aspects into consideration. Different constraints like cardinality constraints, minimum buy in thresholds and maximum limit constraint are associated with assets selection. Financial returns associated are often strongly non-Gaussian in character, and exhibit multivariate outliers. Taking these constraints into consideration and with the presence of these outliers we consider a multi-objective problem where the percentage of each available asset is so selected that the total profit of the portfolio is maximized while total risk is minimized. Nondominated Sorting Genetic Algorithm-II is used for solving this multiobjective portfolio selection problem. Performance of the proposed algorithm is carried out by performing different numerical experiments using real-world data.

Multi Objective Optimisation of an Aero Engine Rotor System Using Nondominated Sorting Genetic Algorithm (NSGA)

2017 ◽  
Author(s):  
Joseph Shibu Kalloor ◽  
Ch. Kanna Babu ◽  
Girish K. Degaonkar ◽  
K. Shankar
Keyword(s):  
Genetic Algorithm ◽  
Critical Speed ◽  
Rotor System ◽  
Pareto Optimal ◽  
Multi Objective ◽  
Rayleigh Beam ◽  
Unbalance Response ◽  
Aero Engine ◽  
Nondominated Sorting ◽  
Engine Rotor

A comprehensive multi-objective optimisation methodology is presented and applied to a practical aero engine rotor system. A variant of Nondominated Sorting Genetic Algorithm (NSGA) is employed to simultaneously minimise the weight and unbalance response of the rotor system with restriction imposed on critical speed. Rayleigh beam is used in Finite Element Method (FEM) implemented in-house developed MATLAB code for analysis. The results of practical interest are achieved through bearing-pedestal model and eigenvalue based Rayleigh damping model. Pareto optimal solutions generated and best solution selected with the help of response surface approximation of the Pareto optimal front. The outcome of the paper is a minimum weight and minimum unbalance response rotor system which satisfied the critical speed constraints.

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