Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms ...

The identification of an optimized model is one of the key issues in the field of fuzzy system modeling. This has gained significant importance since; most of the real life systems are highly complex and nonlinear. Fuzzy model identification involves two stages i.e. identification of input and output membership functions as well as generation of rule base for the system being modeled. The fuzzy...

This paper discusses flexible approaches for fuzzy optimization problems. Specifically, it presents a new solving method for fuzzy optimization problems (FOP) when the variables coefficients and/or the constraints limits parameters are imprecise. The new method uses the simulated annealing algorithm as a resolution procedure because it is a well-suited algorithm for solving all forms of fuzzine...

This paper compares various optimization methods for fuzzy inference system optimization. The optimization methods compared are genetic algorithm, particle swarm optimization and simulated annealing. When these techniques were implemented it was observed that the performance of each technique within the fuzzy inference system classification was context dependent.

This chapter discusses several issues related to the design of linguistic models with high interpretability using fuzzy genetics-based machine learning (GBML) algorithms. We assume that a set of linguistic terms has been given for each variable. Thus our modelling task is to find a small number of fuzzy rules from possible combinations of the given linguistic terms. First we formulate a threeob...

The Karush–Kuhn–Tucker (KKT) optimality conditions and saddle point optimality conditions in fuzzy programming problems have been studied in literature by various authors under different conditions. In this paper, by considering a partial order relation on the set of fuzzy numbers, and convexity with differentiability of fuzzy mappings, we have obtained the Fritz John (FJ) constraint qualificat...

Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Arti...

A novel robust adaptive fuzzy control algorithm is presented for autonomous surface vehicle (ASV) autopilot. This paper studies an approach for fuzzy rule base optimization. The optimization solution especially solves the non-compatible problem in the generation process of fuzzy control rules. For the design study, the optimization model has been carried out in the simulink environment. It is s...

1 Technical University of Cluj-Napoca Abstract -The paper proposes a new multiobjective optimization method, based on fuzzy techniques. The method performs a real multiobjective optimization, every parameter modification taking into account the unfulfillment degrees of all the requirements. It uses fuzzy sets to define fuzzy objectives and fuzzy systems to compute new parameter values. The stra...

Abstract— This paper develops a solution procedure of multi-objective intuitionistic fuzzy optimization to solve a non-linear model with inexact co-efficient and resources. Interval approximation method is used here to convert the imprecise co-efficient which is a triangular fuzzy number to an interval number. We transform this interval number to a parametric interval valued functional form and...