Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teac...

In this paper, we study various supervised learning methods for training feed-forward neural networks. In general, such learning can be considered as a nonlinear global optimization problem in which the goal is to minimize a nonlinear error function that spans the space of weights using heuristic strategies that look for global optima (in contrast to local optima). We survey various global opti...

We consider multidimensional optimization problems, which are formulated and solved in terms of tropical mathematics. The problems are to minimize (maximize) a linear or nonlinear function defined on vectors of a finite-dimensional semimodule over an idempotent semifield, and may have constraints in the form of linear equations and inequalities. The aim of the paper is twofold: first to give a ...

The identification of nonlinear systems operating in a stochastic environment is an important problem in various discipline science and engineering. Fuzzy modeling and especially the T-S fuzzy model draw the attention of several researchers in recent decades this is due to their potential to approximate highly nonlinear behavior. An algorithm allowing the identification of the premise and conse...

This paper presents a leader glowworm swarm optimization algorithm (LGSO) for solving nonlinear equations systems. Since glowworm swarm optimization algorithm has bad optimized ability at high dimension, proposing glowworm swarm optimization algorithm with leader mechanism to strengthen the global optimization ability. Through various types nonlinear equations testing, experiment results show t...

A new nonmonotone algorithm is proposed and analyzed for unconstrained nonlinear optimization. The nonmonotone techniques applied in this algorithm are based on the estimate sequence proposed by Nesterov (Introductory Lectures on Convex Optimization: A Basic Course, 2004) for convex optimization. Under proper assumptions, global convergence of this algorithm is established for minimizing genera...

In this paper, we have presented an optimization approach to document summarization. The potential of optimization based document summarization models has not been well explored to date. This is partially the difficulty to formulate the criteria used for objective assessment. We modeled document summarization as the linear and nonlinear optimization problems. These models generally attempt simu...

In this paper, we provide a general classification of mathematical optimization problems, followed by a matrix of applications that shows the areas in which these problems have been typically applied in process systems engineering. We then provide a review of solution methods of the major types of optimization problems for continuous and discrete variable optimization, particularly nonlinear an...

Optimization as an enabling technology has been one of the big success stories in process systems engineering. In this paper we present first a general review of optimization and its applications to a variety of problems in process systems engineering. Next, we provide an overview of two key areas: nonlinear programming and logic-based discrete/continuous optimization. In particular, recent adv...