scholarly journals A Hybrid Intelligent Algorithm for Optimal Birandom Portfolio Selection Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Qi Li ◽  
Guo-Hua Cao ◽  
Dan Shan
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Portfolio Selection ◽  
Numerical Experiments ◽  
Intelligent Algorithm ◽  
Hybrid Intelligent Algorithm ◽  
Selection Problems

Birandom portfolio selection problems have been well developed and widely applied in recent years. To solve these problems better, this paper designs a new hybrid intelligent algorithm which combines the improved LGMS-FOA algorithm with birandom simulation. Since all the existing algorithms solving these problems are based on genetic algorithm and birandom simulation, some comparisons between the new hybrid intelligent algorithm and the existing algorithms are given in terms of numerical experiments, which demonstrate that the new hybrid intelligent algorithm is more effective and precise when the numbers of the objective function computations are the same.

scholarly journals A genetic algorithm for the routing and carrier selection problem

2012 ◽  
Vol 9 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Jozef Kratica ◽  
Tijana Kostic ◽  
Dusan Tosic ◽  
Djordje Dugosija ◽  
Vladimir Filipovic
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Numerical Experiments ◽  
Selection Problem ◽  
Evolutionary Approach ◽  
Data Sets ◽  
Genetic Operators ◽  
Encoding Scheme ◽  
Standard Data ◽  
Carrier Selection

In this paper we present new evolutionary approach for solving the Routing and Carrier Selection Problem (RCSP). New encoding scheme is implemented with appropriate objective function. This approach in most cases keeps the feasibility of individuals by using specific representation and modified genetic operators. The numerical experiments were carried out on the standard data sets known from the literature and results were successful comparing to two other recent heuristic for solving RCSP.

scholarly journals Modeling of Duck Density and Complex Stocking Time in Rice-Duck Agroecosystems in Terms of Economic and Ecological Benefits

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dahong Xiong ◽  
Kui Fang ◽  
Ying Luo ◽  
Xiaopeng Dai
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Quantitative Assessment ◽  
Intelligent Algorithm ◽  
Ecological Benefits ◽  
Hybrid Intelligent Algorithm ◽  
Numerical Examples ◽  
The Neural Networks ◽  
Fitting Ability ◽  
Selection Of

Rice-duck integrated farming is an effective step under today’s sustainable development background. To make better economic and ecological benefits, a rice-duck agroecosystem is established and kept, in which the paddy field, rice, and the duck mutually promote one another. But the duck density and complex stocking time must be rationally selected. Aiming to attain quantitative assessment and optimal selection of the duck density and complex stocking time in this kind of systems, a methodology based on proposed mathematical models in terms of comparative economic and ecological benefits is addressed. Then the models are solved by a hybrid intelligent algorithmNN-GAthat integrates the Neural Networks (NN) and Genetic Algorithm (GA), making use of the fitting ability in nonlinear fitness context of Neural Networks and the optimization ability of the Genetic Algorithm. Besides, numerical examples are demonstrated in order to test the proposed models. Results reveal that the methodology is reasonable and feasible.

scholarly journals A Computational Method for Optimizing Experimental Environments forPhellinus igniariusvia Genetic Algorithm and BP Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-6
Author(s):  
Zhongwei Li ◽  
Beibei Sun ◽  
Yuezhen Xin ◽  
Xun Wang ◽  
Hu Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Large Scale ◽  
Culture Conditions ◽  
Computational Method ◽  
Learning Ability ◽  
Intelligent Algorithm ◽  
Hybrid Intelligent Algorithm ◽  
Medicinal Value

Flavones, the secondary metabolites ofPhellinus igniariusfungus, have the properties of antioxidation and anticancer. Because of the great medicinal value, there are large demands on flavones for medical use and research. Flavones abstracted from naturalPhellinuscan not meet the medical and research need, sincePhellinusin the natural environment is very rare and is hard to be cultivated artificially. The production of flavones is mainly related to the fermentation culture ofPhellinus, which made the optimization of culture conditions an important problem. Some researches were made to optimize the fermentation culture conditions, such as the method of response surface methodology, which claimed the optimal flavones production was 1532.83 μg/mL. In order to further optimize the fermentation culture conditions for flavones, in this work a hybrid intelligent algorithm with genetic algorithm and BP neural network is proposed. Our method has the intelligent learning ability and can overcome the limitation of large-scale biotic experiments. Through simulations, the optimal culture conditions are obtained and the flavones production is increased to 2200 μg/mL.

A hybrid intelligent algorithm for a fuzzy multi-objective job shop scheduling problem with reentrant workflows and parallel machines

2020 ◽  
Vol 39 (5) ◽  
pp. 7769-7785
Author(s):  
Mohammad-Ali Basiri ◽  
Esmaeil Alinezhad ◽  
Reza Tavakkoli-Moghaddam ◽  
Nasser Shahsavari-Poure
Keyword(s):  
Genetic Algorithm ◽  
Parallel Machines ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Intelligent Algorithm ◽  
Hybrid Intelligent Algorithm ◽  
Shop Scheduling ◽  
Multi Objective

This paper presents a multi-objective mathematical model for a flexible job shop scheduling problem (FJSSP) with fuzzy processing times, which is solved by a hybrid intelligent algorithm (HIA). This problem contains a combination of a classical job shop problem with parallel machines (JSPM) to provide flexibility in the production route. Despite the previous studies, the number of parallel machines is not pre-specified in this paper. This constraint with other ones (e.g., sequence-dependent setup times, reentrant workflows, and fuzzy variables) makes the given problem more complex. To solve such a multi-objective JSPM, Pareto-based optimization algorithms based on multi-objective meta-heuristics and multi-criteria decision making (MCDM) methods are utilized. Then, different comparison metrics (e.g., quality, mean ideal distance, and rate of achievement simultaneously) are used. Also, this paper includes two major phases to provide a new model of the FJSSP and introduce a new proposed HIA for solving the presented model, respectively. This algorithm is a hybrid genetic algorithm with the SAW/TOPSIS method, namely HGASAW/HGATOPSIS. The comparative results indicate that HGASAW and HGATOPSIS outperform the non-dominated sorting genetic algorithm (NSGA-II) to tackle the fuzzy multi-objective JSPM.

scholarly journals Portfolio selection based on uncertain fractional differential equation

2021 ◽  
Vol 7 (3) ◽  
pp. 4304-4314
Author(s):  
Ling Rao ◽  
Keyword(s):  
Differential Equation ◽  
Genetic Algorithm ◽  
Portfolio Selection ◽  
Fractional Differential Equations ◽  
Expected Value ◽  
The Other ◽  
Chance Constraint ◽  
Selection Problems ◽  
Fractional Differential ◽  
Memory Characteristics

<abstract><p>Portfolio selection problems are considered in the paper. The securities in the proposed problems are suggested to follow uncertain fractional differential equations which have memory characteristics. By introducing the left semi-deviation of the wealth, two problems are proposed. One is to maximize the expected value and minimize the left semi-variance of the wealth. The other is to maximize the expected value of the wealth with a chance constraint that the left semi-deviation of the wealth is not less than a given number at a confidence level. The problems are equivalent to determinant ones which will be solved by genetic algorithm. Examples are provided to show the effectiveness of the proposed methods.</p></abstract>

A Layer Hybrid Intelligent Algorithm for Solving Resources Scheduling Problem

2014 ◽  
Vol 644-650 ◽  
pp. 1506-1509
Author(s):  
Yun Jie Ma ◽  
Zi Hui Ren ◽  
Ping Zhu
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimal Solution ◽  
Ant Algorithm ◽  
Scheduling Problem ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Hybrid Intelligent Algorithm ◽  
Modified Genetic Algorithm

A New hybrid intelligent algorithm is used to solve the resources scheduling problem. This new algorithm contains Adaptive Particle Swarm Optimization (APSO) algorithm and Modified Genetic Algorithm (MGA) and Machine Learning (ML) algorithm, MGA is used to realize global searching, APSO is used to get the local searching. The choose processing depend on the definite of information in ant algorithm. Machine learning principle was proposed, after some iteration, the part of the optimal solution was deserved. Then we search the optimal solution in each layer. Simulational results based on the well-known benchmark suites in the literature showed that the algorithm had better optimization performance.

A Hierarchical Fuzzy Portfolio Selection Process Considering Transaction Costs with a Hybrid Intelligent Algorithm

2013 ◽  
Vol 4 (3) ◽  
pp. 90-108 ◽  
Author(s):  
Masoud Rabbani ◽  
Seyyed Mostafa Bahadornia
Keyword(s):  
Portfolio Selection ◽  
Selection Process ◽  
Least Square ◽  
Intelligent Algorithm ◽  
Hybrid Intelligent Algorithm ◽  
Fuzzy Portfolio Selection ◽  
De Algorithm ◽  
Fuzzy Environment ◽  
Multiobjective Mathematical Programming ◽  
Pareto Frontiers

This article presents a hierarchical process for multiobjective portfolio selection in fuzzy environment. Methodology proposed in this paper is consist of three main steps; (a) determining weight of each objective including return, risk and liquidity, by fuzzy logarithmic least square according to investors' preferences matrix by the means of DE algorithm, (b&c) assigning pareto frontiers of problem and computing portion of each security by multiobjective mathematical programming in accordance to obtained weights by the means of GA. Also transaction cost related to each security which are rarely considered in previous works are brought in the authors’ model.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

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