This manuscript introduces ANTIGONE, Algorithms for coNTinuous / Integer Global Optimization of Nonlinear Equations, a general mixed-integer nonlinear global optimization framework. ANTIGONE is the evolution of the Global Mixed-Integer Quadratic Optimizer, GloMIQO, to general nonconvex terms. The purpose of this paper is to show how the extensible structure of ANTIGONE realizes our previously-p...

For some kinds of linearly constrained optimization problems with unique optimal solution, such as linear and convex problems, the single local optimum is also global. However, there are a broad variety of problems in which the property of unique solution cannot be simply postulated or verified. The paper presents an effective approach for the global linearly constrained optimization problem wi...

optimal reactive power dispatch (orpd) is a multi-variable problem with nonlinear constraints and continuous/discrete decision variables. due to the stochastic behavior of loads, the orpd requires a probabilistic mathematical model. in this paper, monte carlo simulation (mcs) is used for modeling of load uncertainties in the orpd problem. the problem is formulated as a nonlinear constrained mul...

This paper presents a particle swarm optimizer to solve constrained optimization problems. The proposed approach adopts a simple method to handle constraints of any type (linear, nonlinear, equality and inequality), and it also presents a novel mechanism to update the velocity and position of each particle. The approach is validated using standard test functions reported in the specialized lite...

This paper presents a modified barebones particle swarm optimization OBPSO to solve constrained nonlinear optimization problems. The proposed approach OBPSO combines barebones particle swarm optimization BPSO and opposition-based learning OBL to improve the quality of solutions. A novel boundary search strategy is used to approach the boundary between the feasible and infeasible search region. ...

Uncertainties may have a large impact on equipment decisions, plant operability, and economic analysis. Thus the consideration of uncertainties in optimization approaches is necessary for robust process design and operation. As a part of it, efficient chance constrained programming has become an important field of research in process systems engineering. In this work, a new approach is proposed...

In this work, and based on numerical optimization techniques, constitutive parameters for viscoelastic materials are determined using a inverse problem formulation. The optimization methodology is based on experimental results obtained in the frequency domain, for a CFRP-Carbon Fibre Reinforced Polymer, through DMA-Dynamic Mechanical Analysis. The relaxation modulus of viscoelastic materials is...

trajectories generally used to describe the space and time required to perform a desired motion task for a mobile robot or manipulator system. in this paper, we considered a cubic polynomial trajectory for the problem of moving a mobile robot from its initial position to a goal position in over a continuous set of time. along the path, the robot requires to observe a certain acceleration profil...

Critical environmental and human health concerns are associated with the rapidly growing fields of nanotechnology and manufactured nanomaterials (MNMs). The main risk arises from occupational exposure via chronic inhalation of nanoparticles. This research presents a chance-constrained nonlinear programming (CCNLP) optimization approach, which is developed to maximize the nanaomaterial productio...

In this paper, we present constrained simulated annealing (CSA), a global minimization algorithm that converges to constrained global minima with probability one, for solving nonlinear discrete non-convex constrained minimization problems. The algorithm is based on the necessary and suucient condition for constrained local minima in the theory of discrete Lagrange multipliers we developed earli...