An appropriate combination of multiple biometric sensors increases the reliability of verification through biometrics. In this paper we propose an effective method of fusion of biometrics based on a dynamic selection of threshold point of fingerprint and iris biometrics towards identifier of an optimal set of rules for fusion. The effectiveness of the method has been established using several b...

The purpose of this paper is to present a linear quadratic simultaneous multi-model stabilizing approach based on simulated annealing algorithm. This method is developed to design robust control laws for AC drives. A specific application to a synchronous self-controlled motor is detailed. The main objective here is design a robust controller with predetermined structure. Experimental results ar...

this paper presents a multi-objective resource-constrained project scheduling problem with positive and negative cash flows. the net present value (npv) maximization and making span minimization are this study objectives. and since this problem is considered as complex optimization in np-hard context, we present a mathematical model for the given problem and solve three evolutionary algorithms;...

In this paper we present the results of an investigation of the possibilities offered by three wellknown metaheuristic algorithms to solve the timetable problem, a multi-constrained, NP-hard, combinatorial optimization problem with real-world applications. First, we present our model of the problem, including the definition of a hierarchical structure for the objective function, and of the neig...

This paper presents a new method that integrates tabu search, simulated annealing, genetic algorithms and backpropagation in both a pruning and constructive manner. The approach obtained promising results in the simultaneous optimization of an artificial neural network architecture and weights. With the proposed method, we investigate four cost functions for global optimization methods: the ave...

In single-objective optimization it is possible to find a global optimum, while in the multi-objective case no optimal solution is clearly defined, but several that simultaneously optimize all the objectives. However, the majority of this kind of problems cannot be solved exactly as they have very large and highly complex search spaces. Recently, meta-heuristic approaches have become important ...

This paper has been worked on a RAP with multi-state components and the performance rate of each component working state may increase by spending technical and organizational activities costs. Whereas RAP belongs to Np-Hard problems, we used Genetic algorithm (GA) and simulated annealing (SA) and for solving the presented problem and calculating system reliability universal generating function ...

Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of solutions, called the Pareto-optimal front. Thus, the goal of multi-objective strategies is to generate a set of non-dominated solutions as an a...

Multi Objective Job Shop Scheduling Problem (MOJSP) is a problem for finding optimal operation sequences of some jobs according to more than one goal to achieve. The problem gets harder as its complexity increases. The development of optimization method has led many new methods to solve this problem. This paper offers Cross Entropy-Genetic Algorithm (CEGA) method to solve job shop scheduling pr...