We focus on fuzzy random multiobjective linear programming problems with variance covariance matrices, and propose a fuzzy decision making method to obtain a satisfactory solution. In the proposed method, it is assumed that the decision maker has fuzzy goals for both permissible levels and the corresponding objective functions for a probability maximization model or a fractile optimization mode...

It is with a great significance to discuss the selection model of regional strategic emerging industry. First, the paper uses the fuzzy optimization theory to select the central industry of area as the regional strategic emerging industry and try to optimize the weight’s calculation in the multistage fuzzy comprehensive evaluation to get more accurate results. Then it will do a strategic emergi...

The aim of his paper is to present some applications of fuzzy techniques in the optimization-based design of analog circuits. Our approach turns into profit the advantages offered by different fuzzy techniques. Fuzzy systems or fuzzy sets are involved in every algorithm phase. The optimization problem formulation is accomplished in a flexible manner using fuzzy sets to define fuzzy optimization...

This paper presents a new multiobjective evolutionary algorithm for Environmental/Economic power Dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem. A new Strength Pareto Evolutionary Algorithm (SPEA) based approach is proposed to handle the EED as a true multiobjective optimization problem with competing and noncommensurable obj...

K e y w o r d s F u z z y number, Possibility and necessity indices, a-optimal solution, Embedding theorem, Banach space. 1. I N T R O D U C T I O N The concept of fuzzy set was introduced by Zadeh [1]. Since then, many applications of fuzzy sets have been widely developed. One of them is the fuzzy optimization in operations research. The randomness occurring in the optimization problems is cat...

often in modeling the engineering optimization design problems, the value of objective function(s) is not clearly defined in terms of design variables. instead it is obtained by some numerical analysis such as fe structural analysis, fluid mechanic analysis, and thermodynamic analysis, etc. yet, the numerical analyses are considerably time consuming to obtain the final value of objective functi...

This paper presents a new approach to multiobjective optimization by evolutionary algorithm. The approach is based on fuzzification of Pareto dominance relation. Using fuzzy degrees of dominance, a set of vectors (multiple objectives) can be partially ranked. The FDD algorithm, a modification of standard genetic algorithm using this ranking scheme for the selection operations, is presented and ...

Optimal power flow problem plays a major role in the operation and planning of power systems. It assists in acquiring the optimized solution for the optimal power flow problem. It consists of several objective functions and constraints. This paper solves the multiobjective optimal power flow problem using a new hybrid technique by combining the particle swarm optimization and ant colony optimiz...

FACTS devices allocation and assessing its optimal capacity is one of most discussed subject in scheduling and utilization of the power system. According to value of the matter such as power loss reduction, enhance of the stability margin and also, less cost imposition need to create a partial balance between these several goals, FACTS allocation problem established as a multiobjective optimiza...

The paper introduces a novel problem based on causal modeling in marketing where knowledge discovery is able to provide useful results (as shown in a real-world application). The problem features (with uncertain data and available expert knowledge) and the proposed multiobjective optimization approach makes genetic fuzzy systems to be a good framework to deal with that.