scholarly journals Interior Point Algorithm for Multi-UAVs Formation Autonomous Reconfiguration

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Wang Jian-hong ◽  
Rana Javed Masood
Keyword(s):  
Multiobjective Optimization ◽  
Interior Point ◽  
Optimization Problem ◽  
Inequality Constraints ◽  
Interior Point Algorithm ◽  
Weighted Sum ◽  
Multiobjective Optimization Problem ◽  
Weighted Sum Method ◽  
Nonlinear Dynamic Equations ◽  
Single Objective

Here the problem of designing multi-UAVs formation autonomous reconfiguration is considered. Combined with three kinds of cost functions, nonlinear dynamic equations, and four inequality constraints, one nonlinear multiobjective optimization problem is constructed. After applying weighted sum method and separating all equality or inequality constraints, the former nonlinear multiobjective optimization problem can be converted into a standard nonlinear single objective optimization problem. Then the interior point algorithm is applied to solve it. Further some improvements are proposed to avoid rank deficiency of some matrices. The equivalence property between multiobjective optimization and single objective optimization through weighted sum method is proved. Finally the efficiency of the proposed strategy can be confirmed by the simulation example results.

scholarly journals Exact Penalization and Necessary Optimality Conditions for Multiobjective Optimization Problems with Equilibrium Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Shengkun Zhu ◽  
Shengjie Li
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Necessary Optimality Conditions ◽  
Inequality Constraints ◽  
Multiobjective Optimization Problem ◽  
Equilibrium Constraints ◽  
Exact Penalization ◽  
Multiobjective Optimization Problems ◽  
Weak Vector

A calmness condition for a general multiobjective optimization problem with equilibrium constraints is proposed. Some exact penalization properties for two classes of multiobjective penalty problems are established and shown to be equivalent to the calmness condition. Subsequently, a Mordukhovich stationary necessary optimality condition based on the exact penalization results is obtained. Moreover, some applications to a multiobjective optimization problem with complementarity constraints and a multiobjective optimization problem with weak vector variational inequality constraints are given.

scholarly journals Combining the cross-entropy algorithm and ∈-constraint method for multiobjective optimization

2021 ◽  
Vol 7 (2) ◽  
pp. 299-311
Author(s):  
Abdelmajid Ezzine ◽  
Abdellah Alla ◽  
Nadia Raissi
Keyword(s):  
Multiobjective Optimization ◽  
Local Search ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Cross Entropy ◽  
Multiobjective Optimization Problem ◽  
Cross Entropy Method ◽  
The Cross ◽  
Constraint Method ◽  
Single Objective

Abstract This paper aims to propose a new hybrid approach for solving multiobjective optimization problems. This approach is based on a combination of global and local search procedures. The cross-entropy method is used as a stochastic model-based method to solve the multiobjective optimization problem and reach a first elite set of global solutions. In the local search step, an ∈-constraint method converts the multiobjective optimization problem to a series of parameterized single-objective optimization problems. Then, sequential quadratic programming (SQP) is used to solve the derived single-objective optimization problems allowing to reinforce and improve the global results. Numerical examples are used to demonstrate the efficiency and effectiveness of the proposed approach.

scholarly journals Multiobjective optimal design of reinforced concrete frames using two metaheuristic algorithms

2021 ◽  
Vol 9 (4B) ◽  
Author(s):  
Mehdi Babaei ◽  
◽  
Masoud Mollayi ◽  
Keyword(s):  
Reinforced Concrete ◽  
Optimal Design ◽  
Optimization Problem ◽  
Numerical Models ◽  
Steel Structures ◽  
Metaheuristic Algorithms ◽  
Rc Structures ◽  
Weighted Sum Method ◽  
Rc Frames ◽  
Single Objective

Genetic algorithm (GA) and differential evolution (DE) are metaheuristic algorithms that have shown a favorable performance in the optimization of complex problems. In recent years, only GA has been widely used for single-objective optimal design of reinforced concrete (RC) structures; however, it has been applied for multiobjective optimization of steel structures. In this article, the total structural cost and the roof displacement are considered as objective functions for the optimal design of the RC frames. Using the weighted sum method (WSM) approach, the two-objective optimization problem is converted to a single-objective optimization problem. The size of the beams and columns are considered as design variables, and the design requirements of the ACI-318 are employed as constraints. Five numerical models are studied to test the efficiency of the GA and DE algorithms. Pareto front curves are obtained for the building models using both algorithms. The detailed results show the accuracy and convergence speed of the algorithms.

scholarly journals Heat transfer from convecting-radiating fin through optimized Chebyshev polynomials with interior point algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 102-110
Author(s):  
Elyas Shivanian ◽  
Mahdi Keshtkar ◽  
Hamidreza Navidi
Keyword(s):  
Heat Transfer ◽  
Chebyshev Polynomials ◽  
Interior Point ◽  
Interior Point Method ◽  
Optimization Problem ◽  
Mathematical Formulation ◽  
Interior Point Algorithm ◽  
Equivalent Problem ◽  
One Dimensional ◽  
Fin Efficiency

AbstractIn this paper, the problem of determining heat transfer from convecting-radiating fin of triangular and concave parabolic shapes is investigated.We consider one-dimensional, steady conduction in the fin and neglect radiative exchange between adjacent fins and between the fin and its primary surface. A novel intelligent computational approach is developed for searching the solution. In order to achieve this aim, the governing equation is transformed into an equivalent problem whose boundary conditions are such that they are convenient to apply reformed version of Chebyshev polynomials of the first kind. These Chebyshev polynomials based functions construct approximate series solution with unknown weights. The mathematical formulation of optimization problem consists of an unsupervised error which is minimized by tuning weights via interior point method. The trial approximate solution is validated by imposing tolerance constrained into optimization problem. Additionally, heat transfer rate and the fin efficiency are reported.

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