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Optimal design of looped water supply network by GA-NLP hybrid approach

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Optimal design of looped water supply network by GA-NLP hybr...

International Conference on Recent Inventions and Innovations in Mathematical Sciences, ICRIIMS 2019

作者： Prasad, Srinivasa A. Krishna, Leela K. Dept. of Civil Engineering R.V.R. and J.C.College of Engineering Chowdavaram Andhra Pradesh Guntur India

Any Municipal distribution system consists of pipe network, valves and pressure generating facilities etc. Around 70% of total cost of any water distribution system is towards cost of pipe network only. Therefore, the cost of pipe network is to be reduced by selecting the appropriate lengths of candidate diameter pipes between the nodes in a pipe network. In the present study, a municipal looped water distribution network is designed for least cost by Genetic Algorithm - Non Linear programming (GA-NLP) hybrid approach. In the present study, optimal lengths of sub pipes of various discrete diameters (sizes) available in the market making the link between the nodes are obtained by minimising the total cost of pipe network. In order to obtain the optimal solution of a looped network, non - linear hydraulic analysis and optimization of network is to be performed. In this study, non - linear hydraulic analysis is made a part of optimization by adding loop head loss and node continuity equations as constraints to the objective function. The model developed is solved by GA-NLP hybrid approach in MATLAB. Near optimal solution obtained by GA is taken as initial solution of NLP for obtaining optimal solution. By adopting GA-NLP hybrid approach, convergence is faster when compared to GA avoiding the complexity in selection of suitable trial values of GA parameters. Many researchers applied different techniques like linear programming, integer linear programming, linear programming gradient method and branch & bound algorithm etc. and optimized the looped pipe network by minimizing the cost. The results obtained by proposed model are compared with that of other investigators and found to be superior. It is also concluded that the proposed model may be applied to any larger municipal looped pipe network through demonstration by applying proposed model to a case study of Rajiv Gandhi Nagar of Guntur Municipal Corporation. © Published under licence by IOP Publishing Ltd.

关键词： nonlinear programming

Semi Eh-b-preinvexity and its applications to optimization problems 2

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Semi Eh-b-preinvexity and its applications to optimization p...

2nd International Scientific Conference on Pure Sciences ... Brilliant Creativity and Renewed Building

作者： Majeed, S.N. Enad, A.A. University of Baghdad Baghdad Iraq

In this paper, the class of semi E h-b-preinvex and pseudo E h-b-preinvex functions are defined as an extension of E-B-preinvex and h-preinvex functions. In this extension the functions E:Rn → Rn, h: [0,1] → , and b:n × n × [0,1] → + are taken into consideration. Various properties for the new functions as well as some properties and characterizations of quasi semi E-preinvex functions are discussed. Some optimality properties for semi E h-b-preinvex nonlinear optimization problems are proved. © 2019 Published under licence by IOP Publishing Ltd.

关键词： nonlinear programming

Risk Aware SUAS Path Planning in an Unstructured Wildfire Environment

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Risk Aware SUAS Path Planning in an Unstructured Wildfire En...

American Control Conference

作者： Rachit Aggarwal Alexander Soderlund Mrinal Kumar David Grymin Department of Mechanical and Aerospace Engineering The Ohio State University Columbus OH USA Department of Mechanical and Aerospace University The Ohio State University Columbus OH USA Control Science Center of Excellence Air Force Research Laboratory Wright-Patterson Air Force Base OH 45433

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

This paper addresses path planning for small unmanned aerial vehicles (SUAS) in an unstructured environment with poorly understood and time varying obstacles. These environmental characteristics manifest in an evolving wildfire, which is used as the test-case for this work. An evidential framework is employed to estimate the current state of wildfire and the resulting heat aura at flight level. This approach accounts for ignorance in estimation that can result from frequent conflict among sensors operating in a harsh environment combined with a computational forecasting agent that must operate with poor models of fire evolution. The objective of SUAS mission design is to visit such points of high conflict in order to provide additional situational awareness. A novel unsupervised classification algorithm based on statistical formalism is developed to identify distinct keep-out zones within the estimated heat aura. Boundaries of identified obstacles are modeled as probabilistic barriers. The planning problem is posed as a chance-constrained optimal control problem, which is transcribed to a nonlinear program via pseudospectral discretization. It is shown that by actively varying the probability of violating obstacle boundaries, a family of solutions can be generated that elicit the risk associated with each mission design.

关键词： Path planning Wildfire flight altitude nonlinear programming Small unmanned aerial vehicles Harsh environments Awareness Sorting algorithm Aura current status Environments CASING INSPECTION

A factor-graph approach for optimization problems with dynamics constraints

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arXiv 2020年

作者： Xie, Mandy Escontrela, Alejandro Dellaert, Frank Georgia Institute of Technology AtlantaGA30332 United States

In this paper, we introduce dynamics factor graphs as a graphical framework to solve dynamics problems and kinodynamic motion planning problems with full consideration of whole-body dynamics and contacts. A factor graph representation of dynamics problems provides an insightful visualization of their mathematical structure and can be used in conjunction with sparse nonlinear optimizers to solve challenging, high-dimensional optimization problems in robotics. We can easily formulate kinodynamic motion planning as a trajectory optimization problem with factor graphs. We demonstrate the flexibility and descriptive power of dynamics factor graphs by applying them to control various dynamical systems, ranging from a simple cart pole to a 12-DoF quadrupedal robot. © 2020, CC-BY-NC-SA.

关键词： nonlinear programming

Optimality conditions and exact penalty for mathematical programs with switching constraints

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arXiv 2020年

作者： Liang, Yan-Chao Ye, Jane J. College of Mathematics and Information Science Henan Normal University Xinxiang453007 China Department of Mathematics and Statistics University of Victoria VictoriaBCV8W 2Y2 Canada

In this paper, we give an overview on optimality conditions and exact penalization for the mathematical program with switching constraints (MPSC). MPSC is a new class of optimization problems with important applications. It is well known that if MPSC is treated as a standard nonlinear program, some of the usual constraint qualifications may fail. To deal with this issue, one could reformulate it as a mathematical program with disjunctive constraints (MPDC). In this paper, we first survey recent results on constraint qualifications and optimality conditions for MPDC, then apply them to MPSC. Moreover, we provide two types of sufficient conditions for the local error bound and exact penalty results for MPSC. One comes from the directional quasi-normality for MPDC, and the other is obtained via the local decomposition approach. Copyright © 2020, The Authors. All rights reserved.

关键词： nonlinear programming

Reactive Power Optimization for Loss Reduction and Voltage Profile Improvement

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Reactive Power Optimization for Loss Reduction and Voltage P...

2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019

作者： Mazzini, Ana Paula Asada, Eduardo N. Lage, Guilherme G. Federal Institute of Education Science and Technology of São Paulo Campus Matão Rua Stéfano Dävassi 625 Nova Cidade Matão SP Brazil Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos SP Brazil Department of Electrical Engineering Center for Exact Sciences and Technology Federal University of São Carlos São Carlos SP13565-905 Brazil

ISBN: (纸本)9781538695678

This paper proposes the resolution of the optimal reactive dispatch (ORD) problem for the combined objective of minimizing active power losses and voltage profile improvement on load buses considering discrete variables and limited the number of control adjustments. The problem is modeled as a multiobjective ORD problem, a resolution strategy which models discrete control variables by a polynomial discretization function and considers limited number of control adjustments is proposed. The proposed solution method is based on transformation of the original mixed integer nonlinear programming (MINLP) problem with mixed continuous, discrete and binary control variables into a nonlinear programming (NLP) problem with only continuous variables. The method has been tested with IEEE test systems with 118 and 300 buses and the success of the proposed strategy has been observed with the resulting high quality solutions with high speed computation. © 2019 IEEE.

关键词： nonlinear programming

A sequential optimality condition for Mathematical Programs with Cardinality Constraints

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arXiv 2020年

作者： Krulikovski, Evelin H.M. Ribeiro, Ademir Alves Sachine, Mael Graduate Program in Mathematics Federal University of Paraná Brazil Department of Mathematics Federal University of Paraná Brazil

In this paper we propose an Approximate Weak stationarity (AW-stationarity) concept designed to deal with Mathematical Programs with Cardinality Constraints (MPCaC), and we proved that it is a legitimate optimality condition independently of any constraint qualification. Such a sequential optimality condition improves weaker stationarity conditions, presented in a previous work. Many research on sequential optimality conditions has been addressed for nonlinear constrained optimization in the last few years, some works in the context of MPCC and, as far as we know, no sequential optimality condition has been proposed for MPCaC problems. We also establish some relationships between our AW-stationarity and other usual sequential optimality conditions, such as AKKT, CAKKT and PAKKT. We point out that, despite the computational appeal of the sequential optimality conditions, in this work we are not concerned with algorithmic consequences. Our aim is purely to discuss theoretical aspects of such conditions for MPCaC *** Codes 90C30, 90C33, 90C46. Copyright © 2020, The Authors. All rights reserved.

关键词： nonlinear programming

A branch and bound based on NSGAII algorithm for multi-objective mixed integer non linear optimization problems

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arXiv 2020年

作者： Jaber, Ahmed Lafon, Pascal Younes, Rafic University of Technology of Troyes France Lebanese University Lebanon

Multi-Objective Mixed Integer Non-Linear programming problems (MO-MINLPs) appear in several real-world applications, especially in the mechanical engineering field. To determine a good approximated Pareto front for this type of problems, we propose a general hybrid approach based on a Multi Criteria Branch-and-Bound (MCBB) and Non-dominated Sorting Genetic Algorithm 2 (NSGAII). We present a computational experiment based on statistical assessment to compare the performance of the proposed algorithm (BnB-NSGAII) with NSGAII using well-known metrics from literature. We propose a new metric, Investment Ratio (IR), that relate the quality of solution to the consumed effort. We consider five real world mechanical engineering problems and two mathematical ones to be used as test problems in this experiment. Experimental results indicate that BnB-NSGAII could be a competitive alternative for solving MO-MINLPs. © 2020, CC BY.

关键词： nonlinear programming

A Novel Evolutionary Algorithm for nonlinear programming Problems

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A Novel Evolutionary Algorithm for Nonlinear Programming Pro...

IEEE 5th International Conference on Advanced Computational Intelligence (ICACI)

作者： Sun, Gaoji Lishui Univ Coll Sci Lishui 323000 Zhejiang Peoples R China

ISBN: (纸本)9781467317443

This paper proposes a novel evolutionary algorithm referred to as importance search algorithm (ISA) for constrained nonlinear programming problems, which is initialized with a population of random feasible solutions and searches for the optimal solution by updating generations. The ISA mainly consists of initialization process and iteration process, and the process of iteration is accomplished according to the move of the best particle in the colony. To show the effectiveness of the proposed ISA, we apply it to solve 8 different kinds of nonlinear programming problems, and compare the computational results with those obtained by using particle swarm optimization (PSO) and genetic algorithm (GA) in the literature. The comparison results show that the ISA is efficient to the problems in multiple-dimensional, nonlinear and complex programming problems. Furthermore, three test problems are selected to demonstrate the effectiveness of the ISA from the sensitivity perspective. The numerical experiments show that the ISA is robust to the parameters settings.

关键词： genetic algorithms iterative methods nonlinear programming particle swarm optimisation search problems