Multi-objective genetic algorithms for solving portfolio optimization problems in the electricity market

2014 ◽  
Vol 58 ◽  
pp. 150-159 ◽  
Author(s):  
Karoon Suksonghong ◽  
Kittipong Boonlong ◽  
Kim-Leng Goh
Keyword(s):  
Genetic Algorithms ◽  
Portfolio Optimization ◽  
Electricity Market ◽  
Optimization Problems ◽  
Multi Objective

Pareto Genetic Algorithms for Multi-Objective Design of Water Distribution Systems

2012 ◽  
Vol 212-213 ◽  
pp. 664-670
Author(s):  
Matteo Nicolini
Keyword(s):  
Genetic Algorithms ◽  
Distribution Systems ◽  
Water Distribution ◽  
Optimization Problems ◽  
Multi Objective ◽  
Supply And Distribution ◽  
The Family ◽  
Hydraulic Network ◽  
Optimal Configurations ◽  
Different Levels

The paper describes the development of a technique for the optimal design of water supply and distribution systems, based on a coupling between evolutionary algorithms and a pressurized hydraulic network solver. The purpose is to show the capabilities of Pareto genetic algorithms (PGAs) in solving multi-objective, constrained optimization problems: in such cases, the optimum is represented not only by one solution, as in single-objective optimization, but by a set of optimal configurations (the Pareto front or frontier), satisfying different levels of compromise among the competing objectives. A Pareto GA should determine the family of such non-dominated solutions, each of which is optimal in the sense that no improvement can be achieved in one criterion without the degradation in at least one of the remaining criteria. This might be of great help to the decision maker in selecting the best trade-off configuration, which will eventually depend on the actual context. An application to a real case is also presented.

Assessment of Multi-Objective Genetic Algorithms With Different Niching Strategies and Regression Methods for Engine Optimization and Design

2009 ◽  
Author(s):  
Yu Shi ◽  
Rolf D. Reitz
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Prediction Accuracy ◽  
Optimization Problems ◽  
Design Parameters ◽  
Hybrid Mode ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Methods ◽  
Objective Space

In previous study [1] the Non-dominated Sorting Genetic Algorithm II (NSGA II) [2] performed better than other popular Multi-Objective Genetic Algorithms (MOGA) in engine optimization that sought optimal combinations of the piston bowl geometry, spray targeting, and swirl ratio. NSGA II is further studied in this paper using different niching strategies that are applied to the objective-space and design-space, which diversify the optimal objectives and design parameters accordingly. Convergence and diversity metrics are defined to assess the performance of NSGA II using different niching strategies. It was found that use of the design niching achieved more diversified results with respect to design parameters, as expected. Regression was then conducted on the design datasets that were obtained from the optimizations with two niching strategies. Four regression methods, including K-nearest neighbors (KN), Kriging (KR), Neural Networks (NN), and Radial Basis Functions (RBF), were compared. The results showed that the dataset obtained from optimization with objective niching provided a more fitted learning space for the regression methods. The KN, KR, outperformed the other two methods with respect to the prediction accuracy. Furthermore, a log transformation to the objective-space improved the prediction accuracy for the KN, KR, and NN methods but not the RBF method. The results indicate that it is appropriate to use a regression tool to partly replace the actual CFD evaluation tool in engine optimization designs using the genetic algorithm. This hybrid mode saves computational resources (processors) without losing optimal accuracy. A Design of Experiment (DoE) method (the Optimal Latin Hypercube method) was also used to generate a dataset for the regression processes. However, the predicted results were much less reliable than results that were learned using the dynamically increasing datasets from the NSGA II generations. Applying the dynamical learning strategy during the optimization processes allows computationally expensive CFD evaluations to be partly replaced by evaluations using the regression techniques. The present study demonstrates the feasibility of applying the hybrid mode to engine optimization problems, and the conclusions can also extend to other optimization studies (numerical or experimental) that feature time-consuming evaluations and have highly non-linear objective-spaces.

scholarly journals Redundant binary codes in genetic algorithms: multi-objective design optimization of water distribution networks

2020 ◽  
Author(s):  
Tiku T. Tanyimboh
Keyword(s):  
Genetic Algorithms ◽  
Design Optimization ◽  
Water Distribution ◽  
Optimization Problems ◽  
Search Space ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Binary Codes ◽  
Multi Objective ◽  
Binary Coding

Abstract Genetic algorithms have been shown to be highly effective for optimization problems in various disciplines, and binary coding is generally adopted as it is straightforward to implement and lends itself to problems with discrete-valued decision variables. However, a difficulty associated with binary coding is the existence of redundant codes that do not correspond to any element in the finite discrete set that the encoded parameter belongs to. A common technique used to address redundant binary codes is to discard the chromosomes in which they occur. Effective alternatives to the outright removal of redundant codes are lacking in the literature. This article presents illustrative examples based on the problem of optimizing the design of water distribution networks. Two benchmark networks in the literature and two different multi-objective design optimization models were considered. Different fixed mapping schemes gave significantly different solutions in the search space. The main inference from the results is that mapping schemes that improved diversity in the population of solutions achieved better results, which may pave the way for the development of practical and effective mapping schemes.

scholarly journals Solving Portfolio Optimization Problems Using MOEA/D and Lévy Flight

2020 ◽  
Vol 12 (03n04) ◽  
pp. 2050005
Author(s):  
Yifan He ◽  
Claus Aranha
Keyword(s):  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problems ◽  
Statistical Test ◽  
Financial Assets ◽  
Lévy Flight ◽  
Nsga Ii ◽  
Levy Flight ◽  
Trade Off ◽  
Multi Objective

Portfolio optimization is a financial task which requires the allocation of capital on a set of financial assets to achieve a better trade-off between return and risk. To solve this problem, recent studies applied multi-objective evolutionary algorithms (MOEAs) for its natural bi-objective structure. This paper presents a method injecting a distribution-based mutation method named Lévy Flight into a decomposition based MOEA named MOEA/D. The proposed algorithm is compared with three MOEA/D-like algorithms, NSGA-II, and other distribution-based mutation methods on five portfolio optimization benchmarks sized from 31 to 225 in OR library without constraints, assessing with six metrics. Numerical results and statistical test indicate that this method can outperform comparison methods in most cases. We analyze how Lévy Flight contributes to this improvement by promoting global search early in the optimization. We explain this improvement by considering the interaction between mutation method and the property of the problem. We additionally show that our method perform well with a round-lot constraint on Nikkei.

scholarly journals Portfolio Optimization: Some Aspects of Modeling and Computing

2017 ◽  
Vol 33 (2) ◽  
Author(s):  
Thanh Nguyen
Keyword(s):  
Portfolio Optimization ◽  
Optimization Problems ◽  
Optimization Method ◽  
Expected Return ◽  
Multi Objective ◽  
Computational Optimization ◽  
Multi Objective Programming ◽  
Computational Aspects ◽  
Mathematical Programming Problems ◽  
Portfolio Return

The paper focuses on computational aspects of portfolio optimization (PO) problems. The objectives of such problems may include: expected return, standard deviation and variation coefficient of the portfolio return rate. PO problems can be formulated as mathematical programming problems in crisp, stochastic or fuzzy environments. To compute optimal solutions of such single- and multi-objective programming problems, the paper proposes the use of a computational optimization method such as RST2ANU method, which can be applied for non-convex programming problems. Especially, an updated version of the interactive fuzzy utility method, named UIFUM, is proposed to deal with portfolio multi-objective optimization problems.

scholarly journals A Firefly Algorithm for Portfolio Optimization

2019 ◽  
Vol 25 (3) ◽  
pp. 282-291
Author(s):  
Indana Lazulfa
Keyword(s):  
Financial Markets ◽  
Portfolio Optimization ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Risk Measures ◽  
Variance Model ◽  
Multi Objective ◽  
Risk Return ◽  
Portfolio Optimization Model ◽  
Mean Variance

Portfolio optimization is the process of allocating capital among a universe of assets to achieve better risk – return trade-off. Portfolio optimization is a solution for investors to get the return as large as possible and make the risk as small as possible. Due to the dynamic nature of financial markets, the portfolio needs to be rebalanced to retain the desired risk-return characteristics. This study proposed multi objective portfolio optimization model with risk, return as the objective function. For multi objective portfolio optimization problems will be used mean-variance model as risk measures. All these portfolio optimization problems will be solved by Firefly Algorithm (FA).

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