Paknejad et al.’s model is considered in this paper. Itemwise multiobjective models for both exponential and uniform lead-time demand are taken and the results are compared numerically both in fuzzy optimization and intuitionistic fuzzy optimization techniques. Objective of this paper is to establish that intuitionistic fuzzy optimizaion method is better than usual fuzzy optimization technique ...

This study adapted an improved algorithm based on Artifical Bee Colony Optimization. It is not possible to justify that all the rules generated by fuzzy based apriori algorithm produce optimum result. Thus optimization of the result generated was carried out by Fuzzy Apriori algorithm using Fuzzy Artifical Bee Colony Optimization (FABCO), it's worth noting that a significant findings were revea...

Fact Gathering means generating rule base from available numerical data or data base. The intelligence of a fuzzy system lies in its rule base. Generating rule base is one of the most important and difficult tasks when designing fuzzy systems. Various rule base generation methods are used such as Neural networks, genetic algorithms, biogeography based optimization approach, ant colony optimizat...

A fuzzy directed graph is used to represent the dynamic relationships among the sub-objectives of a fuzzy multiobjective optimization problem. Using the comprehensive coordination function with exponential weights, we transform a fuzzy multi-objective optimization problem into a single-objective optimization problem. The optimal solution of the single-objective optimization problem can then be ...

this study proposes a combination of a fuzzy sliding mode controller (fsmc) with integral-proportion-derivative switching surface based superconducting magnetic energy storage (smes) and pid tuned by a multi-objective optimization algorithm to solve the load frequency control in power systems. the goal of design is to improve the dynamic response of power systems after load demand changes. in t...

So far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has been done. Moreover to overcome the problem of i...

This paper presents a novel fuzzy identification method for dynamic modelling of quadrotor UAVs. The method is based on a special parameterization of the antecedent part of fuzzy systems that results in fuzzy-partitions for antecedents. This antecedent parameter representation method of fuzzy rules ensures upholding of predefined linguistic value ordering and ensures that fuzzy-partitions remai...

Combustion optimization adjustment can effectively suppress NOx emissions from power plant boilers. Current combustion optimization adjustment methods involve nonlinear optimization based on the boiler combustion model, such as optimization by a genetic algorithm or particle swarm algorithm. The computational complexity of these methods results in poor real-time performance, which limits their ...

This study explores the interconnections between interval analysis, fuzzy interval analysis, and interval, fuzzy, and possibility optimization. The key ideas considered are: (1) Fuzzy and possibility optimization are important methods in applied optimization problems, (2) Fuzzy sets and possibility distributions are distinct which in the context of optimization lead to ‡exible optimization on o...

Based on credibilistic value-at-risk (CVaR) of regularfuzzy variable, we introduce a new CVaR reduction method fortype-2 fuzzy variables. The reduced fuzzy variables arecharacterized by parametric possibility distributions. We establishsome useful analytical expressions for mean values and secondorder moments of common reduced fuzzy variables. The convex properties of second order moments with ...