Research on Feasible Direction Variation of Multi-Objective Genetic Algorithms and Simulation

2014 ◽  
Vol 644-650 ◽  
pp. 1965-1968
Author(s):  
Yue Li Li ◽  
Chao Wang
Keyword(s):  
Fitness Function ◽  
Ideal Point ◽  
Optimal Solution ◽  
Feasible Direction ◽  
Current Group ◽  
Multi Objective ◽  
Point Search ◽  
Solution Region ◽  
The Individual ◽  
The Ideal

The method provided in this paper can be according to the current population to readjust the weight, thus obtain toward the positive ideal point search pressure, finally converge to the optimal solution. This paper combines the feasible direction into genetic algorithm. This method can lead the individual to optimal solution region along feasible direction which approach the optimal solution sets. Through evaluating the degree of distance between chromosome and constrain, we introduce membership function into traditional GA and embed the information of infeasible solutions into fitness function. Propose a self-adapting evaluation function. This method can readjust the weights according to current group and then get the stress of searching to the ideal positive point. To a kind of fuzzy multi-objective optimization problem, propose a method of best satisfaction to transform the fuzzy models to clear ones and solve the models using GA based on interactive method. Then testify its validity though examples.

A fuzzy multi-objective programming optimization model for emergency resource dispatching under equitable distribution principle

2021 ◽  
pp. 1-10
Author(s):  
Zhaoping Tang ◽  
Wenda Li ◽  
Shijun Yu ◽  
Jianping Sun
Keyword(s):  
Optimization Model ◽  
Programming Model ◽  
Ideal Point ◽  
Optimal Solution ◽  
Multi Objective ◽  
Emergency Rescue ◽  
Satisfaction Degree ◽  
Multi Objective Programming ◽  
Parameter Interval ◽  
Objective Programming

In the initial stage of emergency rescue for major railway emergencies, there may be insufficient emergency resources. In order to ensure that all the emergency demand points can be effectively and fairly rescued, considering the fuzzy properties of the parameters, such as the resource demand quantity, the dispatching time and the satisfaction degree, the railway emergency resources dispatching optimization model is studied, with multi- demand point, multi-depot, and multi-resource. Based on railway rescue features, it was proposed that the couple number of relief point - emergency point is the key to affect railway rescue cost and efficiency. Under the premise of the maximum satisfaction degree of quantity demanded at all emergency points, a multi-objective programming model is established by maximizing the satisfaction degree of dispatching time and the satisfaction degree of the couple number of relief point - emergency point. Combined with the ideal point method, a restrictive parameter interval method for optimal solution was designed, which can realize the quick seek of Pareto optimal solution. Furthermore, an example is given to verify the feasibility and effectiveness of the method.

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

scholarly journals Ideal point error for model assessment in data-driven river flow forecasting

2012 ◽  
Vol 16 (8) ◽  
pp. 3049-3060 ◽  
Author(s):  
C. W. Dawson ◽  
N. J. Mount ◽  
R. J. Abrahart ◽  
A. Y. Shamseldin
Keyword(s):  
River Flow ◽  
Ideal Point ◽  
Model Performance ◽  
Model Assessment ◽  
River Flow Forecasting ◽  
The Individual ◽  
Measure Of Performance ◽  
Individual Metrics ◽  
Naive Model ◽  
The Ideal

Abstract. When analysing the performance of hydrological models in river forecasting, researchers use a number of diverse statistics. Although some statistics appear to be used more regularly in such analyses than others, there is a distinct lack of consistency in evaluation, making studies undertaken by different authors or performed at different locations difficult to compare in a meaningful manner. Moreover, even within individual reported case studies, substantial contradictions are found to occur between one measure of performance and another. In this paper we examine the ideal point error (IPE) metric – a recently introduced measure of model performance that integrates a number of recognised metrics in a logical way. Having a single, integrated measure of performance is appealing as it should permit more straightforward model inter-comparisons. However, this is reliant on a transferrable standardisation of the individual metrics that are combined to form the IPE. This paper examines one potential option for standardisation: the use of naive model benchmarking.

Research on multi-objective optimization of switched flux motor based on improved NSGA-II algorithm

2019 ◽  
Vol 233 (6) ◽  
pp. 1268-1279 ◽  
Author(s):  
Liying Jin ◽  
Shengdun Zhao ◽  
Wei Du ◽  
Xuesong Yang ◽  
Wensheng Wang ◽  
...  
Keyword(s):  
Permanent Magnets ◽  
Optimal Solution ◽  
Torque Ripple ◽  
Maximum Output ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Tooth Width ◽  
The Individual ◽  
Rotor Tooth

In order to optimize the local search efficiency of multi-objective parameters of flux switching permanent motor based on traditional NSGA-II algorithm, an improved NSGA-II (iNSGA-II) algorithm is proposed, with an anti-redundant mutation operator and forward comparison operation designed for quick identification of non-dominated individuals. In the initial stage of the iNSGA-II algorithm, half of the individual populations were randomly generated, while the other half was generated according to feature distribution information. Taking the flux switching permanent motor stator/rotor gap, permanent magnets width, stator tooth width, rotor tooth width and other parameters as optimization variables, the flux switching permanent motor maximum output shaft torque and minimum torque ripple are taken as optimization objectives, thus a multi-objective optimization model is established. Real number coding was adopted for obtaining the Pareto optimal solution of flux switching permanent motor structure parameters. The results showed that the iNSGA-II algorithm is better than the traditional NSGA-II on convergence. A 1.8L TOYOTA PRIUS model was selected as the prototype vehicle. By using the optimized parameters, a joint optimization simulation model was established by calling ADVISOR’s back-office function. The simulation results showed that the entire vehicle’s 100-km acceleration time is under 8 s and the battery’s SOC value maintains at 0.5–0.7 in the entire cycle, implying that the iNSGA-II algorithm optimizes the flux switching permanent motor design and is suitable for the initial design and optimizing calculation of the flux switching permanent motor.

A Social Choice Analysis of Online Hotel Rating

2020 ◽  
pp. 2050039
Author(s):  
Yang Xu ◽  
Yelin Fu ◽  
Kin Keung Lai
Keyword(s):  
Social Choice ◽  
Ideal Point ◽  
Individual Preference ◽  
Real Data ◽  
Subjective Judgement ◽  
Choice Analysis ◽  
The Social ◽  
Online Ratings ◽  
The Individual ◽  
The Ideal

The primary purpose of this paper is to aggregate the overall rating based on guests’ online ratings by performing a social choice analysis of online hotel rating. Specifically, we first define the individual preference as the subjective judgement on the important order of the dimensions of online hotel rating, then quantify the individual preference through an analytical approach, the pessimistic and optimistic results of which are balanced by the Hurwicz criterion approach, lastly formulate the social choice result by means of the ideal-point concept. An empirical study using the real data collected from Trip.com is conducted to show the applicability and superiority of our methodology.

scholarly journals Stability of multi-objective bi-level linear programming problems under fuzziness

2013 ◽  
Vol 23 (3) ◽  
pp. 343-354
Author(s):  
Mahmoud Abo-Sinna
Keyword(s):  
Linear Programming ◽  
Electronic Commerce ◽  
Partial Information ◽  
Information Service ◽  
Optimal Solution ◽  
Programming Technique ◽  
Multi Objective ◽  
Linear Programming Problems ◽  
Primary Focus ◽  
The Individual

This paper deals with multi-objective bi-level linear programming problems under fuzzy environment. In the proposed method, tentative solutions are obtained and evaluated by using the partial information on preference of the decision-makers at each level. The existing results concerning the qualitative analysis of some basic notions in parametric linear programming problems are reformulated to study the stability of multi-objective bi-level linear programming problems. An algorithm for obtaining any subset of the parametric space, which has the same corresponding Pareto optimal solution, is presented. Also, this paper established the model for the supply-demand interaction in the age of electronic commerce (EC). First of all, the study uses the individual objectives of both parties as the foundation of the supply-demand interaction. Subsequently, it divides the interaction, in the age of electronic commerce, into the following two classifications: (i) Market transactions, with the primary focus on the supply demand relationship in the marketplace; and (ii) Information service, with the primary focus on the provider and the user of information service. By applying the bi-level programming technique of interaction process, the study will develop an analytical process to explain how supply-demand interaction achieves a compromise or why the process fails. Finally, a numerical example of information service is provided for the sake of illustration.

A Closeness Degree Method for Multi-Objective Optimization of Belt Transmission

1992 ◽  
Author(s):  
Lin Qun ◽  
Wu Meijuan
Keyword(s):  
Mathematical Model ◽  
Objective Function ◽  
Optimization Design ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Objective Function Values ◽  
Belt Transmission ◽  
Fuzzy Subsets ◽  
The Ideal

Abstract A mathematical model for multi objective optimization design of belt transmission is proposed in this paper. The normal fuzzy distribution is used to convert the ideal and non-inferior solutions into fuzzy subsets over the space of objective function values. The optimal solution which is closest to the ideal one could then be found on the basis of closeness degree method.

Fuzzy Linear Multi-Objective Stochastic Programming Models

2019 ◽  
pp. 78-127
Keyword(s):  
Deterministic Model ◽  
Programming Model ◽  
Probability Distributions ◽  
Ideal Point ◽  
Solution Process ◽  
Multi Objective ◽  
Fuzzy Goals ◽  
Objective Chance ◽  
Constrained Programming ◽  
The Individual

In this chapter, fuzzy goal programming (FGP) technique is presented to solve fuzzy multi-objective chance constrained programming (CCP) problems having parameters associated with the system constrains following different continuous probability distributions. Also, the parameters of the models are presented in the form of crisp numbers or fuzzy numbers (FNs) or fuzzy random variables (FRVs). In model formulation process, the imprecise probabilistic problem is converted into an equivalent fuzzy programming model by applying CCP methodology and the concept of cuts of FNs, successively. If the parameters of the objectives are in the form of FRVs then expectation model of the objectives are employed to remove the probabilistic nature from multiple objectives. Afterwards, considering the fuzzy nature of the parameters involved with the problem, the model is converted into an equivalent crisp model using two different approaches. The problem can either be decomposed on the basis of tolerance values of the parameters; alternatively, an equivalent deterministic model can be obtained by applying different defuzzification techniques of FNs. In the solution process, the individual optimal value of each objective is found in isolation to construct the fuzzy goals of the objectives. Then the fuzzy goals are transformed into membership goals on the basis of optimum values of each objective. Then priority-based FGP under different priority structures or weighted FGP is used for achievement of the highest membership degree to the extent possible to achieve the ideal point dependent solution in the decision-making context. Finally, several numerical examples considering different types of probability distributions and different forms of FNs are considered to illustrate the developed methodologies elaborately.

Research on Nondestructive Image Compression Technology Based on Genetic Algorithm

2012 ◽  
Vol 155-156 ◽  
pp. 789-794
Author(s):  
Jie He ◽  
Hui Guo
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Adaptive Genetic Algorithm ◽  
Coding Method ◽  
Iteration Function ◽  
Block Iteration ◽  
Definition Of ◽  
Adaptive Crossover ◽  
The Individual

Based on nondestructive and block iteration function the characteristics of the system, and put forward a kind of improved the global optimal solution from similar partition adaptive genetic algorithm is proposed. In the algorithm for the father the searching space of the individual pieces by gray coding method; Definition of father and son of minimum error for the match fitness function; Genetic algorithm is put forward the improvement of the linear adaptive crossover and mutation probability; Take excellent protection strategy choice. The experimental results show that this method in the similar image guarantee the quality and the compression ratio decompression also can obviously reduce compressed time, effectively improve the searching efficiency.

Multi-Objective Optimization Algorithm for Instrument Integrated Development

2010 ◽  
Vol 44-47 ◽  
pp. 3487-3491
Author(s):  
Guo Xin Wu ◽  
Xiao Li Xu
Keyword(s):  
Optimal Design ◽  
Optimization Algorithm ◽  
Collaborative Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Integrated Development ◽  
Multi Objective ◽  
Individual Fitness ◽  
The Individual

The integrated technology is the main way for the instrument development. The combination of networked collaborative design and multi-objective optimization method, considering the different product design and development of individual fitness degree, to provide the best integrated development for the product solution. The system of Flexible integrated knowledge management was built for networked collaborative design. The system architecture is flexible hub, to support the collaborative development of decision-making and optimal design of innovative integrated development. Innovative multi-objective optimization algorithm also was established based on networked collaborative design. It is realized to obtain fast convergence of the optimal solution set for Knowledge groups. The individual goals, to achieve the optimal design of integrated development, were achieved.

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