Portfolio selection with fuzzy synthetic evaluation and genetic algorithm

2017 ◽  
Vol 34 (7) ◽  
pp. 2422-2434 ◽  
Author(s):  
Hamid Nayebpur ◽  
Mohsen Nazem Bokaei
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Performance Evaluation ◽  
Portfolio Selection ◽  
Expert Knowledge ◽  
Fuzzy Synthetic Evaluation ◽  
Content Type ◽  
Multi Objective ◽  
Overall Performance ◽  
Synthetic Evaluation

Purpose The purpose of this paper is to present a new technique to portfolio selection using a genetic algorithm (GA) and fuzzy synthetic evaluation (FSE). Portfolio selection is a multi-objective/criteria decision-making problem in financial management. Design/methodology/approach The proposed approach solves the problem in two stages. In the first stage, by using a GA and FSE, the weight of criteria will be calculated. Euclidean distance between the computed overall performance evaluation and the surveyed overall performance evaluation is used to determine the weight of criteria. In the second stage, by using a GA and FSE, portfolios will be prioritized. A multi-objective GA is used to determine return and risk in the efficient frontier. A decision making approach is based on FSE to select the best portfolio from among the solutions obtained by a multi objective GA. Findings The main advantage of the proposed approach is to help an investor to find a portfolio which has best performance, and portfolio selection does not rely on expert knowledge. Originality/value The value of the paper is in it using a new approach to determine the weight of criteria and portfolio selection. It surveys firms’ performance in the stock market, based on which the weight of criteria will be determined and portfolios will be prioritized.

Optimum portfolio selection using a hybrid genetic algorithm and analytic hierarchy process

2015 ◽  
Vol 32 (3) ◽  
pp. 379-394 ◽  
Author(s):  
Maghsoud Solimanpur ◽  
Gholamreza Mansourfar ◽  
Farzad Ghayour
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Portfolio Selection ◽  
Efficient Frontier ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Multi Objective ◽  
Fitness Functions ◽  
Hierarchy Process

Purpose – The purpose of this paper is to present a multi-objective model to the optimum portfolio selection using genetic algorithm and analytic hierarchy process (AHP). Portfolio selection is a multi-objective decision-making problem in financial management. Design/methodology/approach – The proposed approach solves the problem in two stages. In the first stage, the portfolio selection problem is formulated as a zero-one mathematical programming model to optimize two objectives, namely, return and risk. A genetic algorithm (GA) with multiple fitness functions called as Multiple Fitness Functions Genetic Algorithm is applied to solve the formulated model. The proposed GA results in several non-dominated portfolios being in the Pareto (efficient) frontier. A decision-making approach based on AHP is then used in the second stage to select the portfolio from among the solutions obtained by GA which satisfies a decision-maker’s interests at most. Findings – The proposed decision-making system enables an investor to find a portfolio which suits for his/her expectations at most. The main advantage of the proposed method is to provide prima-facie information about the optimal portfolios lying on the efficient frontier and thus helps investors to decide the appropriate investment alternatives. Originality/value – The value of the paper is due to its comprehensiveness in which seven criteria are taken into account in the selection of a portfolio including return, risk, beta ratio, liquidity ratio, reward to variability ratio, Treynor’s ratio and Jensen’s alpha.

Customers satisfaction by fuzzy synthetic evaluation and genetic algorithm (case study)

2019 ◽  
Vol 14 (1) ◽  
pp. 31-46 ◽  
Author(s):  
Hamid Nayebpour ◽  
Mohsen Nazem Bokaei
Keyword(s):  
Genetic Algorithm ◽  
Expert Knowledge ◽  
Fuzzy Synthetic Evaluation ◽  
Content Type ◽  
Research Findings ◽  
A New Technique ◽  
Satisfaction Evaluation ◽  
Synthetic Evaluation

Purpose The purpose of this paper is to present a new technique for the determination of effective criteria weight on satisfaction using genetic algorithm and fuzzy synthetic evaluation. Design/methodology/approach The weight values express the relative importance of criteria. In most of research works, weight values depend heavily on expert knowledge, and customer’s perspective have not been considered. The proposed approach determines the criteria weight on satisfaction using genetic algorithm and fuzzy synthetic evaluation considering Euclidean distance between the computed overall satisfaction evaluation and the surveyed overall satisfaction evaluation. Findings The research findings show that different segments of customer have various needs and explain causes of various needs in customers using genetic algorithm and fuzzy synthetic evaluation. Originality/value The value of the paper is in it using a new approach in order to determine the weight of criteria. The main advantage of proposed approach is that it will help managers and researchers to determine the weight of criteria on satisfaction, and this process will no longer just rely on expert knowledge.

scholarly journals Selection methods and diversity preservation in many-objective evolutionary algorithms

2018 ◽  
Vol 52 (4) ◽  
pp. 502-519 ◽  
Author(s):  
Luis Martí ◽  
Eduardo Segredo ◽  
Nayat Sánchez-Pi ◽  
Emma Hart
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Euclidean Distance ◽  
Selection Methods ◽  
Selection Scheme ◽  
Content Type ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Diversity Preservation ◽  
Promising Line

Purpose One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. The paper aims to discuss these issues. Design/methodology/approach Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III and a modified version of the Non-dominated Sorting Genetic Algorithm II, where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of the knowledge, it has not been investigated yet.

Multi-objective integrated planning and scheduling model for operating rooms under uncertainty

2018 ◽  
Vol 232 (9) ◽  
pp. 930-948 ◽  
Author(s):  
Javad Ansarifar ◽  
Reza Tavakkoli-Moghaddam ◽  
Faezeh Akhavizadegan ◽  
Saman Hassanzadeh Amin
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Particle Swarm Optimization ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Operating Rooms ◽  
Programming Approach ◽  
Test Problems ◽  
Swarm Optimization ◽  
Multi Objective

This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.

Multi-objective optimization of hydro-viscous flexible drive for dynamic characteristics using genetic algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jianzhong Cui ◽  
Hu Li ◽  
Dong Zhang ◽  
Yawen Xu ◽  
Fangwei Xie
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Characteristics ◽  
Transmission Efficiency ◽  
Outer Radius ◽  
Transmission Model ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Dynamic Transmission Model

Purpose The purpose of this study is to investigate the flexible dynamic characteristics about hydro-viscous drive providing meaningful insights into the credible speed-regulating behavior during the soft-start. Design/methodology/approach A comprehensive dynamic transmission model is proposed to investigate the effects of key parameters on the dynamic characteristics. To achieve a trade-off between the transmission efficiency and time proportion of hydrodynamic and mixed lubrication, a multi-objective optimization of friction pair system by genetic algorithm is presented to obtain the optimal combination of design parameters. Findings Decreasing the engagement pressure or the ratio of inner and outer radius, increasing the lubricating oil viscosity or the outer radius will result in the increase of time proportion of hydrodynamic and mixed lubrication, as well as the transmission efficiency and its maximum value. After optimization, main dynamic parameters including the oil film thickness, angular velocity of the driven disk, viscous torque and total torque show remarkable flexible transmission characteristics. Originality/value Both the dynamic transmission model and multi-objective optimization model are established to analyze the effects of main design parameters on the dynamic characteristics of hydro-viscous flexible drive.

Method of Parallel System Performance Evaluation Based on Multi-Level Fuzzy Synthetic Evaluation Model

2011 ◽  
Vol 301-303 ◽  
pp. 1202-1207
Author(s):  
Xue Mei Hou ◽  
Lei Yu ◽  
Zhi Bo Li ◽  
Zhu Ping Du ◽  
Bai You Lian
Keyword(s):  
Performance Evaluation ◽  
System Performance ◽  
Hot Spot ◽  
Evaluation Model ◽  
Evaluation Process ◽  
Parallel System ◽  
Fuzzy Synthetic Evaluation ◽  
Multi Level ◽  
System Performance Evaluation ◽  
Synthetic Evaluation

Nowadays, the parallel system performance evaluation is the hot spot topic. It’s helpful to improve the parallel system performance to establish a scientific evaluation model. In this paper, using of analytic hierarchy process and the fuzzy mathematics theory, the multi-level fuzzy synthetic evaluation model is established, the performance index system is defined and the parallel system performance evaluation process is realized.

Computational design and development of novel Al-Mg-Sc-Cr alloy

2015 ◽  
Vol 11 (3) ◽  
pp. 401-412 ◽  
Author(s):  
Abhijit Patra ◽  
Subhas Ganguly ◽  
Partha Protim Chattopadhyay ◽  
Shubhabrata Datta
Keyword(s):  
Genetic Algorithm ◽  
Mg Alloy ◽  
Age Hardening ◽  
Pareto Solutions ◽  
Content Type ◽  
Multi Objective ◽  
Hardening Effect ◽  
Ann Models ◽  
Strength And Ductility ◽  
Alloy Systems

Purpose – The purpose of this paper is to design and develop precipitation hardened Al-Mg alloy imparting enhanced strength with acceptable ductility through minor addition of Sc and Cr by using multi-objective genetic algorithm-based searching. In earlier attempts of strengthening aluminum alloys, owing to the formation of Al3Sc and Al7Cr phase, addition of Sc and Cr have yielded attractive precipitation hardening, respectively. Both the Al-Sc and Al-Cr system are quench sensitive due to presence of a sloping solvus in their phase diagrams. It is also known that both the Al3Sc and Al7Cr phases nucleate directly from the supersaturated solid solution without formation of GP-zones or transient phases prior to the formation of the Al3Sc and Al7Cr. Sc also found to have beneficial effect on the corrosion property of such alloys. In view of the above, it is of interest to explore the possibility of enhancing the age hardening effect in Al-Mg alloy by addition of Sc and Cr. Design/methodology/approach – The paper uses an approach where experimental information of two different alloy systems (namely, Al-Mg-Sc and Al-Cr) has been combined to generate a single database involving the potential features of both the systems with the aim to formulate the suitable artificial neural network (ANN) models for strength and ductility. The models are used as the objective functions for the optimization process. The patterns of the optimized Pareto front are analyzed to recognize the optimal property of the alloy system. The hitherto unexplored Al-Mg-Sc-Cr alloy, designed from the Pareto solutions and suitably modified on the basis of prior knowledge of the system, is then synthesized and characterized. Findings – The paper has demonstrated the ANN- and genetic algorithm (GA)-based design of a hitherto unexplored alloy by utilizing the existing information concerning the component alloy systems. The paper also established that analyses of the Pareto solutions generated through multi-objective optimization using GA provide an insight of the variation of the parameters at different combination of strength and ductility. It also revealed that the Al-Mg-Sc-Cr alloy has exhibited a two-stage age hardening effect. The first and second stages are due to the precipitation of Al3Sc and Al7Cr phases, respectively. Research limitations/implications – In the present study the two alloy systems are used in tandem to develop models to describe the properties involving the distinct mechanistic features of phase evolution inherent in both the systems. Though the ANN models having the capability to capture huge non-linearity of a system have been employed to predict the convoluted effects of those characteristics when an alloy containing Mg, Sc and Cr are added simultaneously, but the ANN models predictions can be checked experimentally by the future researchers. Practical implications – The paper demonstrates the role of scandium and chromium addition on the ageing characteristics of the alloy by analyzing the age hardening behavior of the designed alloy in cast and cold rolled condition clearly. Originality/value – The approach stated in this paper is a novel one, in the sense that experimental data of two different alloy systems have been clubbed to generate a single database with the aim to formulate the suitable ANN models for strength and ductility.

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