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Sequential quadratic programming methods for large-scale problems

COMPUTATIONAL optimization AND APPLICATIONS 1997年第1期7卷 127-142页

作者： Murray, W Systems Optimization Laboratory Department of Operations Research Stanford University USA

Sequential quadratic (SQP) programming methods are the method of choice when solving small or medium-sized problems. Since they are complex methods they are difficult (but not impossible) to adapt to solve large-scale problems. We start by discussing the difficulties that need to be addressed and then describe some general ideas that may be used to resolve these difficulties. A number of SQP codes have been written to solve specific applications and there is a general purposed SQP code called SNOPT, which is intended for general applications of a particular type. These are described briefly together with the ideas on which they are based. Finally we discuss new work on developing SQP methods using explicit second derivatives.

关键词： nonlinearly constrained minimization quadratic programming large-scale optimization

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A Massively Parallel BWP Algorithm for Solving Large-Scale Systems of nonlinear Equations

A Massively Parallel BWP Algorithm for Solving Large-Scale S...

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IEEE high performance Extreme Computing Virtual conference (HPEC)

作者： Silva, Bruno Lopes, Luiz Guerreiro Univ Madeira PhD Program Comp Eng SRE Reg Govt Madeira Funchal Madeira Is Portugal Univ Madeira Fac Exact Sci & Engn Funchal Madeira Is Portugal

ISBN: (纸本)9798350308600

This paper presents a GPU-based massively parallel implementation of the Best-Worst-Play (BWP) metaphor-less optimization algorithm, which results from the combination of two other simple and quite efficient population-based algorithms, Jaya and Rao-1, that have been used to solve a variety of problems. The proposed parallel GPU version of the algorithm is here used for solving large nonlinear equation systems, which have enormous importance in different areas of science, engineering, and economics and are usually considered the most difficult class of problems to solve by traditional numerical methods. The proposed parallelization of the BWP algorithm was implemented using the Julia programming language on a GeForce RTX 3090 GPU with 10 496 CUDA cores and 24 GB of VRAM and tested on a set of challenging scalable systems of nonlinear equations with dimensions between 500 and 2000. Depending on the tested problem and dimension, the GPU-based implementation of BWP exhibited a speedup up to 283.17x, with an average of 161.21x, which shows the efficiency of the proposed GPU-based parallel version of the BWP algorithm.

关键词： metaheuristic optimization metaphor-less algorithms GPU acceleration best-worst-play algorithm nonlinear equation systems

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SLOTSA: A Multi-Strategy Improved tunicate swarm algorithm for engineering constrained optimization problems

SLOTSA: A Multi-Strategy Improved tunicate swarm algorithm f...

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1st IEEE International conference on software Services Engineering (IEEE SSE)

作者： Wang, Wentao Fan, Chengshuai Pan, Zhongjie Tian, Jun Nankai Univ Coll Software Tianjin Peoples R China

ISBN: (纸本)9798350340754

With the increasing demands on industrial products, the design and manufacturing problems of industrial products are becoming more and more complex, Many industrial design problems belong to nonlinear optimization problems and NP-hard problems, such as shop scheduling, path planning, and industrial part design problems, In the last decade, more and more researchers have developed heuristic-based algorithms to deal with industrial design problems, Tnnicate swarm algorithm (fSA) is a newly proposed high performance heuristic optimization algorithm, The TSA algorithm has the problems of low solution accuracy, slow convergence and easy attraction by local extrema, In order to solve some drawbacks of TSA, this paper improves TSA and proposes the Sine cosine -Levy-Opposition-based Tnnicate swarm algorithm (SLOTSA) by combining opposition-based learning, Levy flight and positive cosine operator, Comparative experiments on SLOTSA in 10 benchmark functions fully demonstrate the rationality of the three improvement strategies, In addition, this paper applies SLOTSA to two practical industrial design problems, and the optimization results demonstrate the wide applicability of SLOTSA to industrial design problems,

关键词： Tunicate swarm algorithm Industrial design problems Heuristic algorithms Opposition-based learning strategy Engineering Problem Design

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COMPUTERIZED DESIGN AND optimization OF SELF-CONTAINED AIRBORNE high POWER ELECTRICAL SYSTEMS.

IEEE Proceedings of the National Aerospace and Electronics C...

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IEEE Proceedings of the National Aerospace and Electronics conference 1980年 768-773页

作者： Brockhurst, Frederick C.

A philosophy is presented for computerized design and optimization of high power electrical systems. A brief overview of the types of systems to be designed is presented, including reasons for considering a variety of systems to meet a specific performance requirement. Implementation of the computerized design process requires development of three separate software packages: system feasibility, component design, and system analysis. The three packages are intertied by an executive program that has automatic and iteractive capabilities. System requirements are input to the executive program in an interactive mode. The requirements data are automatically transmitted to the system feasibility package where feasibility is determined from algorithms that map the results of many detailed component designs.

关键词： AIRCRAFT

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An improved whale optimization algorithm for locating critical slip surface of slopes

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ADVANCES IN ENGINEERING software 2021年 157卷 103009-103009页

作者： Li, S. H. Luo, X. H. Wu, L. Z. Southwest Petr Univ Coll Civil Engn & Geomat Chengdu 610500 Sichuan Peoples R China Southwest Jiaotong Univ Dept Geol Engn Chengdu 610031 Sichuan Peoples R China Chengdu Univ Technol Coll Environm & Civil Engn Chengdu 610059 Sichuan Peoples R China

Locating a critical slip surface or calculating the minimum safety factor of a slope is important in geotechnical engineering, and also involves a complex optimization problem. A novel mathematical model considering linear and nonlinear failure criteria is developed to locate the critical slip surface. And an improved whale optimization algorithm (IWOA), which employs a nonlinear adjustment parameter and Gaussian perturbation operator in whale optimization algorithm (WOA), is proposed to examine this model. The parameter of IWOA is determined by numerical experiments. The performance of IWOA, WOA, salp swarm algorithm (SSA), and cuckoo search algorithm (CS) is investigated. Statistical analyses of twelve benchmark functions show that the four algorithms with high to low performances are IWOA, WOA, SSA and CS. Two multimodal functions for analyzing slope stability show that IWOA outperforms ant colony optimization algorithm (ACO), and imperialistic competitive algorithm (ICA). The time computational complexity of IWOA is same as WOA. Case studies of homogeneous and multilayer soil slopes indicate that the failure criterion has a significant effect on the minimum safety factor and the critical slip surface, and that IWOA can show better performance than WOA, SSA and CS. This study develops an efficient method to locate critical slip surfaces of soil slopes for engineers.

关键词： improved whale optimization algorithm critical slip surface minimum safety factor failure criterion

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Parallel Modified Artificial Bee Colony Algorithm for Solving Conditional nonlinear Optimal Perturbation

Parallel Modified Artificial Bee Colony Algorithm for Solvin...

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18th IEEE International conference on high performance Computing and Communications (HPCC) / 14th IEEE International conference on Smart City (Smart City) / 2nd IEEE International conference on Data Science and Systems (DSS)

作者： Ren, Juhui Yuan, Shijin Mu, Bin Tongji Univ Sch Software Engn Shanghai Peoples R China

ISBN: (纸本)9781509042975

Intelligent algorithms have been applied to solving conditional nonlinear optimal perturbation (CNOP), which plays an important role in the study of weather and climate predictability. Single particle intelligent optimization algorithms can get similar CNOP to adjoint method, and show higher time efficiency in solving CNOP. However, swarm intelligent optimization algorithms can only get similar CNOP, and still show lower time efficiency than adjoint method. In this paper, we proposed a modified artificial bee colony algorithm (MABC) to solve CNOP, and to accelerate the computation speed, we parallelize the MABC algorithm with MPI technology. In order to demonstrate its validity and efficiency, we apply MABC algorithm to solving CNOP in Zebiak`-Cane model, which is a medium-complexity numerical model. The results obtained are compared with those from standard bee colony algorithm (ABC) algorithm, generic algorithm (GA) and adjoint method which is the benchmark. The MABC algorithm can get better results than the standard ABC and GA algorithm in solving CNOP, and can obtain similar results with adjoint method in CNOP magnitude and pattern aspects. The parallel MABC with MPI also shows a higher efficiency than adjoint method. All the experimental results show that it is feasible and efficient to solve CNOP with the proposed parallel modified artificial bee colony algorithm.

关键词： artificial bee colony algorithm CNOP MPI Zebiak-Cane model

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Reliability-Based Design optimization of Structures Combining Genetic algorithms and Finite Element Reliability Analysis 10th

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10th International conference on Soft Computing Models in Industrial and Environmental Applications (SOCO)

作者： Celorrio, Luis Univ La Rioja Dept Mech Engn Logrono La Rioja Spain

ISBN: (纸本)9783319197197;9783319197180

Structural optimization has undergone a substantial progress. Most of these research efforts deal with deterministic parameters. However, in a realistic structural design, it is necessary to consider inherent uncertainties in geometric variables and material properties to ensure safety and quality. Then, design constraints are formulated in terms such as probability of failure or reliability index. The process of design optimization enhanced by the addition of reliability constraints is referred as Reliability-Based Design optimization (RBDO). Most of RBDO methods use classical mathematical optimization algorithms and require computing gradients of objective function and constraints. This task sometimes can be cumbersome and very hard because reliability constraints are implicit functions of design variables. However, the increased power of computers has made possible to apply heuristic methods, especially Genetic algorithms in RBDO problems. In this paper Genetic algorithms have been combined with nonlinear Finite Element Reliability Analysis software, named OpenSees, to solve RBDO problems. A numerical example shows the performance of the implementation.

关键词： Reliability based design optimization nonlinear finite elements Opensees Genetic algorithms Discrete design variables

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A Parallel Multi-Core Team of Multiobjective Evolutionary algorithms to Discover DNA Motifs

A Parallel Multi-Core Team of Multiobjective Evolutionary Al...

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14th IEEE International conference on high performance Computing and Communications (HPCC) / IEEE 9th International conference on Embedded software and Systems (ICESS)

作者： Gonzalez-Alvarez, David L. Vega-Rodriguez, Miguel A. Gomez-Pulido, Juan A. Sanchez-Perez, Juan M. Univ Extremadura Dept Technol Comp & Commun Escuela Politecn Caceres 10003 Spain

ISBN: (纸本)9780769547497

This paper proposes the use of a parallel multicore team to exploit the characteristics of several algorithms, when solving one of the best known biological problems, the Motif Discovery Problem (MDP). Often, when we solve an optimization problem, we have to tackle very different scenarios. This complicates finding a single technique or algorithm that will be able to solve all of them adequately. One possible solution to this problem is to combine different algorithms in a team that allows us to solve all scenarios. A parallel methodology is designed in this work, which combines the operation of four multiobjective evolutionary algorithms by using OpenMP to solve different motif discovery scenarios. Our conclusions show that our parallel multi-core team is quite flexible and accurate for this application, and an important tool for discovering relevant DNA motifs.

关键词： Multi-core OpenMP evolutionary algorithms motif discovery multiobjective optimization

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Constrained-optimization Approach Delivers Superior Classical performance for Graph Partitioning via Quantum-ready Method

Constrained-optimization Approach Delivers Superior Classica...

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IEEE high performance Extreme Computing conference (HPEC)

作者： Chukwu, Uchenna Dridi, Raouf Berwald, Jesse Booth, Michael Dawson, John Le, DeYung Wainger, Mark Reinhardt, Steven P. Quantum Comp Inc Leesburg VA USA

ISBN: (纸本)9781728192192

Graph partitioning is one of an important set of well-known compute-intense (NP-hard) graph problems that devolve to discrete constrained optimization. We sampled solutions to the problem via two different quantum-ready methods to understand the strengths and weaknesses of each method. First we formulated and sampled the problem as a quadratic unconstrained binary optimization (QUBO) problem, via the best known QUBO formulation, using a best-in-class QUBO sampler running purely classically. Second, we formulated the problem at a higher level, as a set of constraints and an objective function, and sampled it with a recently developed constrained-optimization sampler (which generates QUBOs and also samples them classically). We find that both approaches often deliver better partitions than the purpose-built classical graph partitioners. Further, we find that the constrained-optimization approach is often able to deliver better partitions in less time than the bespoke-QUBO approach, without specific knowledge of the graph-partitioning problem. Stepping back from graph partitioning itself, one key question is whether bespoke algorithms for high-value problems or general tools for classes of problems are more likely to deliver the power of QCs to a broad market of real-world users. We believe this early evidence, though anecdotal, supports the proposition that general tools may contribute significant benefit to a range of problems, expanding the impact of QCs on industry and society. The fact that this benefit is independent of the low-level sampler employed, whether classical software or one of a variety of QC architectures, reinforces the need for further work on high-level optimization. The commercial availability in the cloud of such software today, delivering superior classical performance for some problems, enables quantum-forward organizations to migrate to quantum-ready methods now, accelerating progress toward quantum advantage and ensuring sampler software evol

关键词： quantum computing alloyed quantum-classical computing quadratic unconstrained binary optimization (QUBO) quantum algorithms quantum advantage QAOA

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The software tool for comparison and configuration of nonlinear optimization techniques in ORB-SLAM 12

The software tool for comparison and configuration of nonlin...

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12th International conference on the Developments in eSystems Engineering (DeSE)

作者： Makaev, Boris Afanasyev, Ilya Innopolis Univ Innopolis 420500 Russia

ISBN: (纸本)9781728130217

This paper presents the study on development of a software tool with a graphical user interface (GUI) to compare nonlinear optimization methods in ORB-SLAM library. It uses g2o graph optimization framework to solve five optimization problems while tracking SLAM: (a) Global Bundle Adjustment (GBA), (b) Local Bundle Adjustment (LBA), (c) Relative Simulation optimization (SO), (d) Pose Graph optimization over SO(3) Constraints, and (e) Essential Graph optimization. By default, ORB-SLAM solves these problems using Levenberg-Marquardt method, but our tool allows to select optimization methods between Levenberg-Marquardt, Powell's Dogleg and Gauss-Newton algorithms to result in the best configuration solution when working with a video dataset. The developed application allows a user to configure the optimization tasks in ORB-SLAM and run an experiment to observe the performance evaluation of the current configuration. In addition, the ORB-SLAM functionality has been extended using Ceres solver optimization framework. The tests of various datasets showed that the developed application is a convenient and effective tool for comparing different solutions and finding the fastest and most accurate ORB-SLAM configuration.

关键词： Computer aided software engineering

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