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Bio-inspired computational heuristics to study Lane-Emden systems arising in astrophysics model

SPRINGERPLUS 2016年第1期5卷 1页

作者： Ahmad, Iftikhar Raja, Muhammad Asif Zahoor Bilal, Muhammad Ashraf, Farooq Univ Gujrat Dept Math Gujrat 50700 Pakistan COMSATS Inst Informat Technol Dept Elect Engn Attock 43600 Pakistan Univ Malaysia Pahang Fac Sci & Technol Pekan Pahang Malaysia

This study reports novel hybrid computational methods for the solutions of nonlinear singular Lane-Emden type differential equation arising in astrophysics models by exploiting the strength of unsupervised neural network models and stochastic optimization techniques. In the scheme the neural network, sub-part of large field called soft computing, is exploited for modelling of the equation in an unsupervised manner. The proposed approximated solutions of higher order ordinary differential equation are calculated with the weights of neural networks trained with genetic algorithm, and pattern search hybrid with sequential quadratic programming for rapid local convergence. The results of proposed solvers for solving the nonlinear singular systems are in good agreements with the standard solutions. Accuracy and convergence the design schemes are demonstrated by the results of statistical performance measures based on the sufficient large number of independent runs.

关键词： Singular systems Computational intelligence Genetic algorithm Artificial neural networks Pattern search methods sequential quadratic programming

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An Integrated Biomimetic Control Strategy with Multi-agent Optimization for Nonlinear Chemical Processes⁎

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IFAC-PapersOnLine 2018年第18期51卷 55-60页

作者： Mirlekar, Gaurav Gebreslassie, Berhane H. Li, Shuyun Diwekar, Urmila M. Lima, Fernando V. Department of Chemical and Biomedical Engineering West Virginia University MorgantownWV26506 United States Vishwamitra Research Institute Clarendon Hills IL60514 United States

In this paper, a framework is proposed for integrating a Biologically-Inspired Optimal Control Strategy (BIO-CS) with Multi-Agent Optimization (MAO) algorithms for process systems engineering applications. In this framework, the BIO-CS employs gradient-based optimal control solvers in an intelligent manner to simultaneously control multiple outputs of the process at their desired setpoints. Also, the MAO uses the capabilities of nonlinear heuristic-based optimization techniques such as Efficient Ant Colony Optimization (EACO), Efficient Genetic Algorithm (EGA) and Efficient Simulated Annealing (ESA) by sharing process information to obtain as an upper layer optimal operating setpoints for the controller that satisfy the overall process objective. The resulting approach is a unique combination of control and optimization methods that provide optimal solutions for dynamic systems. The applicability of the proposed framework is demonstrated using a nonlinear, multivariable fermentation process. In particular, a multivariable control structure associated with the first-principles-based model derived from mass and energy balances of the fermentation process is addressed. The performance of the proposed approach for each step is compared to sequential quadratic programming (SQP) and a classical Proportional-Integral (PI) controller in terms of optimization and control, respectively. The proposed approach improves the overall performance of the process in terms of cumulative production rate by approximately 10-15%, resulting in economic benefits. The obtained results illustrate the capabilities of this novel integrated framework to achieve desired nonlinear system performance considering scenarios associated with setpoint tracking and plant-model mismatch. © 2018

关键词： Multi agent systems Agents Ant colony optimization Biomimetic processes Controllers Fermentation Genetic algorithms Intelligent control Optimal control systems Optimization Process control quadratic programming Simulated annealing Two term control systems Control and optimization Efficient genetic algorithms Non linear control Optimal controls Optimization and control Process systems engineering Proportional integral controllers sequential quadratic programming

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A feasible SQP-GS algorithm for nonconvex, nonsmooth constrained optimization

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NUMERICAL ALGORITHMS 2014年第1期65卷 1-22页

作者： Tang, Chun-ming Liu, Shuai Jian, Jin-bao Li, Jian-ling Guangxi Univ Coll Math & Informat Sci Nanning 530004 Guangxi Peoples R China Yulin Normal Univ Coll Math & Informat Sci Yulin 537000 Peoples R China

The gradient sampling (GS) algorithm for minimizing a nonconvex, nonsmooth function was proposed by Burke et al. (SIAM J Optim 15:751-779, 2005), whose most interesting feature is the use of randomly sampled gradients instead of subgradients. In this paper, combining the GS technique with the sequential quadratic programming (SQP) method, we present a feasible SQP-GS algorithm that extends the GS algorithm to nonconvex, nonsmooth constrained optimization. The proposed algorithm generates a sequence of feasible iterates, and guarantees that the objective function is monotonically decreasing. Global convergence is proved in the sense that, with probability one, every cluster point of the iterative sequence is stationary for the improvement function. Finally, some preliminary numerical results show that the proposed algorithm is effective.

关键词： Nonsmooth optimization sequential quadratic programming Gradient sampling Feasible algorithm

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Some results on the design of brushless DC wheel motor using SQP and GA

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2007年第3-4期26卷 233-241页

作者： Moussouni, F. Brisset, S. Brochet, P. Ecole Cent Lille L2EP F-59651 Villeneuve Dascq France

The sequential quadratic programming and genetic algorithms are finely tuned on the optimal design of a brushless DC wheel motor. Combining both methods, a hybrid approach is tested. Multiobjective optimizations are performed using Nondominated Sorting Genetic Algorithm, Strength Pareto Evolutionary Algorithm, and a variable weighted sum of objectives with the sequential quadratic programming. The Pareto fronts are compared in term of accuracy, uniformity, and coverage criteria using some quality indicators.

关键词： sequential quadratic programming genetic algorithms mixed-integer optimization multiobjective optimization

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Modifying SQP for degenerate problems

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SIAM JOURNAL ON OPTIMIZATION 2002年第2期13卷 470-497页

作者： Wright, SJ Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

Most local convergence analyses of the sequential quadratic programming (SQP) algorithm for nonlinear programming make strong assumptions about the solution, namely, that the active constraint gradients are linearly independent and that there are no weakly active constraints. In this paper, we establish a framework for variants of SQP that retain the characteristic superlinear convergence rate even when these assumptions are relaxed, proving general convergence results and placing some recently proposed SQP variants in this framework. We discuss the reasons for which implementations of SQP often continue to exhibit good local convergence behavior even when the assumptions commonly made in the analysis are violated. Finally, we describe a new algorithm that formalizes and extends standard SQP implementation techniques, and we prove convergence results for this method also.

关键词： nonlinear programming sequential quadratic programming degeneracy

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Analytical Gradient-Guided Nonlinear programming Approach for Multitarget Rendezvous Mission

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第3期46卷 581-589页

作者： Huang, An-Yi Luo, Ya-Zhong Li, Heng-Nian Xian Satellite Control Ctr State Key Lab Astronaut Dynam Xian 710043 Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn Hunan Key Lab Intelligent Planning & Simulat Aeros Changsha 410073 Peoples R China Xian Satellite Control Ctr Xian 710043 Peoples R China

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Constrained Dynamic Differential Evolution Using a Novel Hybrid Constraint Handling Technique

Constrained Dynamic Differential Evolution Using a Novel Hyb...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Eita, Mohammad A. Shoukry, Amin A. E JUST Dept Comp Sci & Engn Alex Egypt Univ Alexandria Comp & Syst Engn Dept Alex Egypt

ISBN: (纸本)9781479938407

In this paper, a Constrained Dynamic Differential Evolution (CDDE) algorithm is proposed to solve constrained optimization problems. In CDDE, the crossover rate CR and scale factor F are dynamically changed and selected randomly from the range [0.5,1]. This way, CDDE has degrees of exploration abilities for the landscape of the constrained optimization problems and can be able to discover the search space and reach the feasible regions. Also, a novel hybrid simple constraint handling technique is suggested, which combines two well-known techniques: feasible rules and adaptive penalty function. Near convergence, CDDE uses the sequential quadratic programming (SQP) method to enhance its local search ability. CDDE performance has been tested on the constrained benchmark functions of the CEC 2010 competition. The results demonstrate that CDDE outperforms other state-of-the-art algorithms and consistently reaches feasible solutions.

关键词： constraint handling evolutionary computation quadratic programming CDDE algorithm CEC 2010 competition SQP method adaptive penalty function constrained dynamic differential evolution constrained optimization problems crossover rate feasible rules hybrid simple constraint handling technique local search ability scale factor sequential quadratic programming Evolutionary computation Genetic algorithms Heuristic algorithms Linear programming Optimization Sociology Vectors

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Numerical Algorithms for Solving Boundary-Value Problems on Reduced Dimensional Manifolds

Numerical Algorithms for Solving Boundary-Value Problems on ...

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AIAA Aviation Forum and Exposition

作者： Sparapany, Michael J. Grant, Michael J. Purdue Univ Aeronaut & Astronaut Engn W Lafayette IN 47904 USA

ISBN: (数字)9781624105890

ISBN: (纸本)9781624105890

One key result from trajectory optimization through indirect methods is that the infinite-dimensional optimization problem is transcribed into a finite-dimensional boundary-value problem on a manifold. Various reduction techniques exist that may reduce this boundary-value problem to an equivalent, but lower dimensional problem. A general format for describing boundary-value problems on reduced dimensional manifolds is given. A collocation and shooting algorithm are then presented that are capable of operating on the reduced dimensional manifold while simultaneously satisfying the so-called full dimensional mission requirements. These algorithms are then applied to a few boundary-value problems as well as the classic Brachistochrone problem from optimal control theory.

关键词： Boundary Value Problems sequential quadratic programming Trajectory Optimization Diminishing Returns Pseudospectral Methods Newton's Method FORTRAN

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Optimal Control and Numerical Optimization for Missile Interception Guidance 13

Optimal Control and Numerical Optimization for Missile Inter...

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13th European Control Conference (ECC)

作者： Walter, Leif Schloeffel, Gunther Theodoulis, Spilios Wernert, Philippe Kostina, Ekaterina Holzapfel, Florian French German Res Inst St Louis Dept Guidance Nav & Control St Louis France Univ Marburg Fac Math & Comp Sci Marburg Germany Tech Univ Munich Inst Flight Syst Dynam Garching Germany

ISBN: (纸本)9783952426913

Conventional guidance algorithms for exo-atmospheric missile interception, such as Proportional Navigation, provide guidance laws that are derived from conceptual heuristics where certain simplifications have to be made. Typically, these guidance laws have the disadvantage of not being able to fully take into account system and/or vehicle limitations such as maximum thrust. In this paper, optimal control and numerical optimization methods are used to provide a guidance scheme that may incorporate these critical constraints, while still ensuring a successful hit-to-kill interception of the target. Therein, a direct optimal control approach is deployed, based on multiple shooting and a sequential quadratic programming algorithm for solving the resulting nonlinear optimization problem. Numerical results for three distinctive transcription methods for deriving the nonlinear program are presented that illustrate the overall efficiency of numerical optimization as a guidance scheme.

关键词： Acceleration Algorithm design and analysis Heuristic algorithms Navigation Optimal control Optimization Trajectory Optimal control Algorithm design and analysis Heuristic algorithms trajectory navigation Guidance acceleration interceptions missiles sequential quadratic programming maximum thrust multiple blasting

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Active-set Newton methods for mathematical programs with vanishing constraints

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2012年第2期53卷 425-452页

作者： Izmailov, A. F. Pogosyan, A. L. MSU OR Dept VMK Fac Moscow 119991 Russia

Mathematical programs with vanishing constraints constitute a new class of difficult optimization problems with important applications in optimal topology design of mechanical structures. Vanishing constraints usually violate standard constraint qualifications, which gives rise to serious difficulties in theoretical and numerical treatment of these problems. In this work, we suggest several globalization strategies for the active-set Newton-type methods developed earlier by the authors for this problem class, preserving superlinear convergence rate of these methods under weak assumptions. Preliminary numerical results demonstrate that our approach is rather promising and competitive with respect to the existing alternatives.

关键词： Mathematical program with vanishing constraints Constraint qualification Optimality condition Active-set method sequential quadratic programming Semismooth Newton method Global convergence

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