scholarly journals Developing an Inspection Optimization Model Based on the Delay-Time Concept

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ehsan Nazemi ◽  
Kamran Shahanaghi
Keyword(s):  
Delay Time ◽  
Optimization Problems ◽  
Good Condition ◽  
Substantial Reduction ◽  
Structural System ◽  
Academic World ◽  
Decision Variables ◽  
Inspection Policy ◽  
Maintenance And Inspection ◽  
Inspection Schedule

Infrastructures are considered as important facilities required for every country and society to be able to work properly. Aging and deterioration of such structures during their lifetime are a major concern both for maintenance researchers in the academic world and for the practitioners. This concern is mainly because the deterioration increases the maintenance costs dramatically and lowers the reliability, availability, and safety of the structural system. Preventive maintenance and inspection activities are the most usual means for keeping the structure in a good condition. This paper utilizes the concept of delay-time for developing the optimal inspection policy for deteriorating structures. In the proposed stochastic model, discrete times of inspection activities are taken as the decision variables of an optimization problem, in a way that the obtained aperiodic (nonuniform) inspection schedule minimizes the total downtime ratio of the structure. To illustrate the model capabilities, various numerical examples are solved and results are compared with the traditional periodic (uniform) inspection policies. The results indicate the substantial reduction in system downtime due to the wisely planned inspection schedule and the appropriate utilization of delay-time concept, which is indeed a powerful framework for inspection optimization problems.

scholarly journals Two-stage multi-tasking transform framework for large-scale many-objective optimization problems

2021 ◽  
Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma
Keyword(s):  
Large Scale ◽  
Original Problem ◽  
Optimization Problems ◽  
Reference Points ◽  
Optimization Strategy ◽  
Two Stage ◽  
Decision Space ◽  
Second Stage ◽  
Decision Variables ◽  
Objective Space

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.

scholarly journals Comparative Study of Optimization Algorithms for The Optimal Reservoir Operation

2018 ◽  
Vol 246 ◽  
pp. 01003
Author(s):  
Xinyuan Liu ◽  
Yonghui Zhu ◽  
Lingyun Li ◽  
Lu Chen
Keyword(s):  
Reservoir Operation ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Complex Problem ◽  
Optimization Techniques ◽  
Decision Variables ◽  
Handling Methods ◽  
Optimality Algorithm ◽  
Parameter Values

Apart from traditional optimization techniques, e.g. progressive optimality algorithm (POA), modern intelligence algorithms, like genetic algorithms, differential evolution have been widely used to solve optimization problems. This paper deals with comparative analysis of POA, GA and DE and their applications in a reservoir operation problem. The results show that both GA and DES are feasible to reservoir operation optimization, but they display different features. GA and DE have many parameters and are difficult in determination of these parameter values. For simple problems with mall number of decision variables, GA and DE are better than POA when adopting appropriate parameter values and constraint handling methods. But for complex problem with large number of variables, POA combined with simplex method are much superior to GA and DE in time-assuming and quality of optimal solutions. This study helps to select proper optimization algorithms and parameter values in reservoir operation.

Numerical Optimization Methods for Ship Hydrodynamic Design

2009 ◽  
Author(s):  
Emilio F. Campana ◽  
Daniele Peri ◽  
Yusuke Tahara ◽  
Manivannan Kandasamy ◽  
Frederick Stern
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Substantial Reduction ◽  
Noise Robustness ◽  
Naval Research ◽  
Mathematical Framework ◽  
Original Design ◽  
Computational Expense ◽  
Ship Hydrodynamic ◽  
Variable Fidelity

The use of computational methods in design engineering is growing rapidly at all stages of the design process, with the final goal of a substantial reduction of the cost and time for the development of a design. Simulations and optimization algorithms can be combined together into what is known as Simulation-Based Design (SBD) techniques. Using these tools the designers may find the minimum of some user defined objective functions with constraints, under the general mathematical framework of a Non-Linear Programming problem. There are problems of course: computational complexity, noise, robustness and accuracy of the numerical simulations, flexibility in the use of these tools; all these issues will have to be solved before the SBD methodology can become more widespread. In the paper, some derivative-based algorithms and methods are initially described, including efficient ways to compute the gradient of the objective function. Derivative-free methods - such as genetic algorithms and swarm methods are then described and compared on both algebraic tests and on hydrodynamic design problems. Both local and global hydrodynamic ship design optimization problems are addressed, defined in either a single- or a multi-objective formulation framework. Methods for reducing the computational expense are presented. Metamodels (or surrogated models) are a rigorous framework for optimizing expensive computer simulations through the use of inexpensive approximations of expensive analysis codes. The Variable Fidelity idea tries instead to alleviate the computational expense of relying exclusively on high-fidelity models by taking advantage of well-established engineering approximation concepts. Examples of real ship hydrodynamic design optimization cases are given, reporting results mostly collected through a series of projects funded by the Office of Naval Research. Whenever possible, an experimental check of the success of the optimization process is always advisable. Several examples of this testing activity are reported in the paper one is illustrated by the two pictures at the top of this page, which show the wave pattern close to the sonar dome of an Italian Navy Anti-Submarine Warfare corvette: left, the original design; right, the optimized one.

Innovative Hybrid Genetic Algorithms and Line Search Method for Industrial Production Management

2010 ◽  
pp. 142-160 ◽  
Author(s):  
Pandian M. Vasant
Keyword(s):  
Production Management ◽  
Optimization Problems ◽  
Fuzzy Optimization ◽  
Computational Techniques ◽  
Optimization Approach ◽  
Non Linear Programming ◽  
Decision Variables ◽  
Line Search Method ◽  
Non Linear ◽  
Management Optimization

Many engineering, science, information technology and management optimization problems can be considered as non linear programming real world problems where the all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research chapter is to solve non linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers which was represented by logistic membership functions by using hybrid evolutionary optimization approach. To explore the applicability of the present study a numerical example is considered to determine the production planning for the decision variables and profit of the company.

scholarly journals Managing randomization in the multi-block alternating direction method of multipliers for quadratic optimization

2020 ◽  
Author(s):  
Krešimir Mihić ◽  
Mingxi Zhu ◽  
Yinyu Ye
Keyword(s):  
Quadratic Programming ◽  
Large Scale ◽  
Optimization Problems ◽  
Quadratic Optimization ◽  
Alternating Direction Method ◽  
Convex Quadratic Programming ◽  
Method Of Multipliers ◽  
Numerical Tests ◽  
Alternating Direction ◽  
Decision Variables

Abstract The Alternating Direction Method of Multipliers (ADMM) has gained a lot of attention for solving large-scale and objective-separable constrained optimization. However, the two-block variable structure of the ADMM still limits the practical computational efficiency of the method, because one big matrix factorization is needed at least once even for linear and convex quadratic programming. This drawback may be overcome by enforcing a multi-block structure of the decision variables in the original optimization problem. Unfortunately, the multi-block ADMM, with more than two blocks, is not guaranteed to be convergent. On the other hand, two positive developments have been made: first, if in each cyclic loop one randomly permutes the updating order of the multiple blocks, then the method converges in expectation for solving any system of linear equations with any number of blocks. Secondly, such a randomly permuted ADMM also works for equality-constrained convex quadratic programming even when the objective function is not separable. The goal of this paper is twofold. First, we add more randomness into the ADMM by developing a randomly assembled cyclic ADMM (RAC-ADMM) where the decision variables in each block are randomly assembled. We discuss the theoretical properties of RAC-ADMM and show when random assembling helps and when it hurts, and develop a criterion to guarantee that it converges almost surely. Secondly, using the theoretical guidance on RAC-ADMM, we conduct multiple numerical tests on solving both randomly generated and large-scale benchmark quadratic optimization problems, which include continuous, and binary graph-partition and quadratic assignment, and selected machine learning problems. Our numerical tests show that the RAC-ADMM, with a variable-grouping strategy, could significantly improve the computation efficiency on solving most quadratic optimization problems.

A Memetic Optimization Strategy Based on Dimension Reduction in Decision Space

2015 ◽  
Vol 23 (1) ◽  
pp. 69-100 ◽  
Author(s):  
Handing Wang ◽  
Licheng Jiao ◽  
Ronghua Shang ◽  
Shan He ◽  
Fang Liu
Keyword(s):  
Dimension Reduction ◽  
Optimization Problems ◽  
State Of The Art ◽  
Search Space ◽  
Optimization Strategy ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Decision Space ◽  
Multi Objective ◽  
Decision Variables

There can be a complicated mapping relation between decision variables and objective functions in multi-objective optimization problems (MOPs). It is uncommon that decision variables influence objective functions equally. Decision variables act differently in different objective functions. Hence, often, the mapping relation is unbalanced, which causes some redundancy during the search in a decision space. In response to this scenario, we propose a novel memetic (multi-objective) optimization strategy based on dimension reduction in decision space (DRMOS). DRMOS firstly analyzes the mapping relation between decision variables and objective functions. Then, it reduces the dimension of the search space by dividing the decision space into several subspaces according to the obtained relation. Finally, it improves the population by the memetic local search strategies in these decision subspaces separately. Further, DRMOS has good portability to other multi-objective evolutionary algorithms (MOEAs); that is, it is easily compatible with existing MOEAs. In order to evaluate its performance, we embed DRMOS in several state of the art MOEAs to facilitate our experiments. The results show that DRMOS has the advantage in terms of convergence speed, diversity maintenance, and portability when solving MOPs with an unbalanced mapping relation between decision variables and objective functions.

SURFACE EFFECT CORRECTION OF MOISTURE DETERMINATION BY NEUTRON PROBE USING PSO TECHNIQUE AND MCNP

2009 ◽  
Vol 06 (02) ◽  
pp. 247-255
Author(s):  
ALI ASGHAR MOWLAWI ◽  
HADI SADOGHI YAZDI ◽  
MEHDI ARGHIANI ◽  
JABER ROOHI ◽  
RAHIM KOOHI-FAYEGH ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Surface Effect ◽  
Soil Surface ◽  
Moisture Determination ◽  
Swarm Optimization ◽  
Convex Optimization Problem ◽  
Neutron Probe ◽  
Decision Variables ◽  
Paraffin Block

The usefulness of the neutron meter is limited for moisture measurement near the soil surface. In this present work, optimum dimension of a paraffin block has been calculated to correct the surface effect in order to use neutron probe near the soil surface by MCNP4C code and Particle Swarm Optimization (PSO) technique. PSO is chiefly a technique to find a global or quasi-minimum for a nonlinear and non-convex optimization problem, and there have been few studies into optimization problems with discrete decision variables. The results show a paraffin block 23.55 × 23.55 cm2 square base with 4.84, 4.92, 5.10, 5.23, and 5.47 cm thickness which can correct the surface effect fairly for 0.10, 0.20, 0.30, 0.40, and 0.50 g/g moisture.

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