Satisficing solutions of multi-objective fuzzy optimization problems using genetic algorithm

Thapar, Antika; Pandey, Dhaneshwar; Gaur, S. K.

doi:10.1016/j.asoc.2012.03.002

Cited by 27 publications

(13 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To represent the nature of the fuzzy goal of each objective function the membership functions was first introduced by Zimmermann (1978). It was later followed by Narasimhan (1980), Hannan (1981), Lee and Li (1993), Rao et al (1993), Huang (1997), Mohan and Nguyen (1998), Deep et al (2011), Thapar et al (2012, Garg and Sharma (2013), etc.…”

Section: Normalization Of Values Of Linguistic Variables Involved In mentioning

confidence: 99%

“…Thapar et al (2012) presented genetic algorithm for solving multi-objective optimization problems with maxproduct fuzzy relation equations as constraints. Deep et al (2011) proposed an interactive approach based method for solving multi-objective optimization problems by treating objectives as fuzzy goals and the satisfaction of constraints is considered at different α-level sets of the fuzzy parameter used.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective optimization problem under fuzzy rule constraints using particle swarm optimization

Chakraborty

Guha

2015

Soft Comput

View full text Add to dashboard Cite

In this paper, a fuzzy multi-objective programming problem is considered where functional relationships between decision variables and objective functions are not completely known to us. Due to uncertainty in real decision situations sometimes it is difficult to find the exact functional relationship between objectives and decision variables. It is assumed that information source from where some knowledge may be obtained about the objective functions consists of a block of fuzzy if-then rules. In such situations, the decision making is difficult and the presence of multiple objectives gives rise to multi-objective optimization problem under fuzzy rule constraints. In order to tackle the problem, appropriate fuzzy reasoning schemes are used to determine crisp functional relationship between the objective functions and the decision variables. Thus a multi-objective optimization problem is formulated from the original fuzzy rule-based multi-objective optimization model. In order to solve the resultant problem, a deterministic single-objective non-linear optimization problem is reformulated with the help of fuzzy optimization technique. Finally, PSO (Particle Swarm Optimization) algorithm is employed to solve the resultant singleobjective non-linear optimization model and the computation procedure is illustrated by means of numerical examples. Communicated by V. Loia.

show abstract

Section: Normalization Of Values Of Linguistic Variables Involved In mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization problem under fuzzy rule constraints using particle swarm optimization

Chakraborty

Guha

2015

Soft Comput

View full text Add to dashboard Cite

show abstract

“…Further, Deep et al [16] proposed an interactive approach based method for solving multiobjective optimization problems. A genetic algorithm for multiobjective optimization problems with max-product fuzzy relation equations as constraints was presented by Thapar et al [17]. Green train scheduling model was proposed by Li et al [18].…”

Section: Introductionmentioning

confidence: 98%

Multi-objective optimization based on fuzzy if-then rules

Chakraborty

Guha

2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

In this paper, a fuzzy multi-objective mathematical programming problem is considered where functional relationship between the decision variables and the objective functions is not completely known to us. It is assumed that the information source from where some knowledge may be obtained about objective functions consists of a block of fuzzy if-then rules. The focus in this work is to solve the multi-objective optimization problem for the above situations. A numerical example is also presented to illustrate the method.

show abstract

“…(23), the value of the crowbar resistance must be large enough to limit the DFIG rotor overcurrent and to make the time constant attenuate quickly. According to Equation (22), if ' max rcb T is defined as the maximum damping time constant, the value of the crowbar resistance can be described as:…”

Section: B Problem Constraintsmentioning

confidence: 99%

Multi-objective Fuzzy-optimization of Crowbar Resistances for the Low-Voltage Ride-through of Doubly Fed Induction Wind Turbine Generation Systems

Zhang¹,

Zhang²,

Yang³

2015

Journal of Power Electronics

View full text Add to dashboard Cite

This study investigates the multi-objective fuzzy optimization of crowbar resistance for the doubly fed induction generator (DFIG) low-voltage ride-through (LVRT). By integrating the crowbar resistance of the crowbar circuit as a decision variable, a multi-objective model for crowbar resistance value optimization has been established to minimize rotor overcurrent and to simultaneously reduce the DFIG reactive power absorbed from the grid during the process of LVRT. A multi-objective genetic algorithm (MOGA) is applied to solve this optimization problem. In the proposed GA, the value of the crowbar resistance is represented by floating-point numbers in the GA population. The MOGA emphasizes the non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy-set-theory-based is employed to obtain the best solution. The proposed approach has been evaluated on a 3 MW DFIG LVRT. Simulation results show the effectiveness of the proposed approach for solving the crowbar resistance multi-objective optimization problem in the DFIG LVRT.

show abstract

Satisficing solutions of multi-objective fuzzy optimization problems using genetic algorithm

Cited by 27 publications

References 19 publications

Multi-objective optimization problem under fuzzy rule constraints using particle swarm optimization

Multi-objective optimization problem under fuzzy rule constraints using particle swarm optimization

Multi-objective optimization based on fuzzy if-then rules

Multi-objective Fuzzy-optimization of Crowbar Resistances for the Low-Voltage Ride-through of Doubly Fed Induction Wind Turbine Generation Systems

Contact Info

Product

Resources

About