Multi-choice programming solves some optimization problems where multiple information exists for a parameter. The aim of this paper is to select an appropriate parameter from a set of multiple choices, which optimizes the objective function. We consider a linear programming problem where the right hand side parameters are multi-choice in nature. In this paper, the multiple choices of a paramete...

Inertia weight is a most important parameter of particle swarm optimization (PSO), which can keep a right balance between the global search and local search. In this paper, a novel PSO with non-linear inertia weight based on the tangent function is provided. The paper also presents the method of determining a control parameter in our proposed method, saving the user from a tedious trial and err...

Coordinate descent (CD) algorithms have become the method of choice for solving a number of optimization problems in machine learning. They are particularly popular for training linear models, including linear support vector machine classification, LASSO regression, and logistic regression. We consider general CD with non-uniform selection of coordinates. Instead of fixing selection frequencies...

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...

A H∞ control for linear parameter-varying (LPV) systems with a parameter-varying state delay is described. A new parameter-dependent H∞ performance criterion is first established by the introduction of a slack variable, which exhibits a kind of decoupling between Lyapunov functions and system matrices. This kind of decoupling enables us to obtain a more easily tractable condition for analysis a...

An optimization design method was proposed for linear phased array ultrasonic imaging testing. In the method, main lobe width and the amplitude ratio of side lobe to main lobe were served as optimization objectives to judge the testing precision of phased array beam directivity. The combination of element of width and inter-element space was served as optimize parameter for the optimization obj...

To increase their competitiveness, many industrial companies monitor their production process, collecting large amount of measurements. This paper describes a technique using this data to improve the performance of a monitored process. In particular we wish to find a set of rules, i.e. intervals on a reduced number of parameters, for which an output value is maximized. The model-free optimizati...

This work presents joint iterative power allocation and interference suppression algorithms for spread spectrum networks which employ multiple hops and the amplify-and-forward cooperation strategy for both the uplink and the downlink. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to individual and global power const...

1. The fitting or parameter estimation of complex ecological models is a challenging optimization task, with a notable lack of tools for fitting complex stochastic models. 2. calibrar is an R package that has been developed for fitting complex ecological models to data, including complex Individual Based Models. It is a generic tool that can be used for any type of model, especially those with ...