Ranked-Based Sensitivity Analysis for Size Optimization of Structures

2015 ◽  
Vol 137 (12) ◽  
Author(s):  
Babak Dizangian ◽  
Mohammad Reza Ghasemi
Keyword(s):  
Sensitivity Analysis ◽  
Design Space ◽  
Optimization Technique ◽  
Optimization Procedure ◽  
Global Optimum ◽  
Size Optimization ◽  
Second Phase ◽  
Local Optimum ◽  
Blast Search ◽  
Sliding Technique

This article proposes a novel ranked-based method for size optimization of structures. This method uses violation-based sensitivity analysis and borderline adaptive sliding technique (ViS-BLAST) on the margin of feasible nonfeasible (FNF) design space. ViS-BLAST maybe considered a multiphase optimization technique, where in the first phase, the first arbitrary local optimum is found by few analyses and in the second phase, a sequence of local optimum points is found through jumps and BLASTs until the global optimum is found. In fact, this technique reaches a sensitive margin zone where the global optimum is located, with a small number of analyses, utilizing a space-degradation strategy (SDS). This strategy substantially degrades the high order searching space and then proceeds with the proposed ViS-BLAST search for the optimum design. Its robustness and effectiveness are then defied by some well-known benchmark examples. The ViS-BLAST not only speeds up the optimization procedure but also it ensures nonviolated optimum designs.

scholarly journals A Novel Multi-Epoch Particle Swarm Optimization Technique

2018 ◽  
Vol 18 (3) ◽  
pp. 62-74 ◽  
Author(s):  
Romasevych Yuriy ◽  
Loveikin Viatcheslav
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Global Optimum ◽  
High Rate ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Great Possibility

Abstract Since canonical PSO method has many disadvantages which do not allow to effectively reach the global minima of various functions it needs to be improved. The article refers to a novel Multi-Epoch Particle Swarm Optimization (ME-PSO) technique which has been developed by authors. ME-PSO algorithm is based on reinitializing of the stagnant swarm with low exploration efficiency. This approach provides a high rate of global best changing. As a result ME-PSO has great possibility of finding good local (or even global) optimum and does not trap in bad local optimum. In order to prove the advantages of the ME-PSO technique numerical experiments have been carried out with ten uni- and multimodal benchmark functions. Analysis of the obtained results convincingly showed significant superiority of ME-PSO over PSO and IA-PSO algorithms. It has been set that canonical PSO is a special case of ME-PSO.

scholarly journals A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Opeoluwa Owoyele ◽  
Pinaki Pal
Keyword(s):  
Machine Learning ◽  
Design Space Exploration ◽  
Design Space ◽  
Optimization Technique ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
Optimization Approach ◽  
Ic Engine ◽  
Weak Learner

Abstract In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

scholarly journals Multiobjective Optimization of Carbon Fiber-Reinforced Plastic Composite Bumper Based on Adaptive Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Dawei Gao ◽  
Haotian Liang ◽  
Guijie Shi ◽  
Liqin Cao
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Optimization Technique ◽  
Optimization Procedure ◽  
Convergence Speed ◽  
Adaptive Genetic Algorithm ◽  
Local Optimum ◽  
Plastic Composite ◽  
Mutation Operators ◽  
Adaptive Crossover

Genetic algorithm (GA) is a common optimization technique that has two fatal limitations: low convergence speed and premature convergence to the local optimum. As an effective method to solve these drawbacks, an adaptive genetic algorithm (AGA) considering adaptive crossover and mutation operators is proposed in this paper. Verified by two test functions, AGA shows higher convergence speed and stronger ability to search the global optimal solutions than GA. To meet the crashworthiness and lightweight demands of automotive bumper design, CFRP material is employed in the bumper beam instead of traditional aluminum. Then, a multiobjective optimization procedure incorporating AGA and the Kriging surrogate model is developed to find the optimal stacking angle sequence of CFRP. Compared with the conventional aluminum bumper, the optimized CFRP bumper exhibits better crashworthiness and achieves 43.19% weight reduction.

Opinion Dynamics Optimization by Varying Susceptibility to Persuasion via Non-Convex Local Search

2021 ◽  
Vol 16 (2) ◽  
pp. 1-34
Author(s):  
Rediet Abebe ◽  
T.-H. HUBERT Chan ◽  
Jon Kleinberg ◽  
Zhibin Liang ◽  
David Parkes ◽  
...  
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Iterative Process ◽  
Large Scale ◽  
Opinion Dynamics ◽  
Theoretical Models ◽  
Global Optimum ◽  
Local Optimum ◽  
Opinion Formation ◽  
Long Line

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.

Numerical Simulation of Gas Lift Optimization Using Artificial Intelligence for a Middle Eastern Oil Field

2021 ◽  
Author(s):  
Mohammed Ahmed Al-Janabi ◽  
Omar F. Al-Fatlawi ◽  
Dhifaf J. Sadiq ◽  
Haider Abdulmuhsin Mahmood ◽  
Mustafa Alaulddin Al-Juboori
Keyword(s):  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Middle Eastern ◽  
Optimization Technique ◽  
Injection Rate ◽  
Oil Field ◽  
Oil Fields ◽  
Reservoir Pressure ◽  
Artificial Lift ◽  
Gas Lift

Abstract Artificial lift techniques are a highly effective solution to aid the deterioration of the production especially for mature oil fields, gas lift is one of the oldest and most applied artificial lift methods especially for large oil fields, the gas that is required for injection is quite scarce and expensive resource, optimally allocating the injection rate in each well is a high importance task and not easily applicable. Conventional methods faced some major problems in solving this problem in a network with large number of wells, multi-constrains, multi-objectives, and limited amount of gas. This paper focuses on utilizing the Genetic Algorithm (GA) as a gas lift optimization algorithm to tackle the challenging task of optimally allocating the gas lift injection rate through numerical modeling and simulation studies to maximize the oil production of a Middle Eastern oil field with 20 production wells with limited amount of gas to be injected. The key objective of this study is to assess the performance of the wells of the field after applying gas lift as an artificial lift method and applying the genetic algorithm as an optimization algorithm while comparing the results of the network to the case of artificially lifted wells by utilizing ESP pumps to the network and to have a more accurate view on the practicability of applying the gas lift optimization technique. The comparison is based on different measures and sensitivity studies, reservoir pressure, and water cut sensitivity analysis are applied to allow the assessment of the performance of the wells in the network throughout the life of the field. To have a full and insight view an economic study and comparison was applied in this study to estimate the benefits of applying the gas lift method and the GA optimization technique while comparing the results to the case of the ESP pumps and the case of naturally flowing wells. The gas lift technique proved to have the ability to enhance the production of the oil field and the optimization process showed quite an enhancement in the task of maximizing the oil production rate while using the same amount of gas to be injected in the each well, the sensitivity analysis showed that the gas lift method is comparable to the other artificial lift method and it have an upper hand in handling the reservoir pressure reduction, and economically CAPEX of the gas lift were calculated to be able to assess the time to reach a profitable income by comparing the results of OPEX of gas lift the technique showed a profitable income higher than the cases of naturally flowing wells and the ESP pumps lifted wells. Additionally, the paper illustrated the genetic algorithm (GA) optimization model in a way that allowed it to be followed as a guide for the task of optimizing the gas injection rate for a network with a large number of wells and limited amount of gas to be injected.

Primena Lagranžove optimizacije na problematiku proizvodnje energije iz obnovljivih izvora

2020 ◽  
Vol 22 (1-2) ◽  
pp. 145-152
Author(s):  
Vladan Ristić ◽  
◽  
Nikola Rajaković
Keyword(s):  
Power Plants ◽  
Production Systems ◽  
Optimization Technique ◽  
Optimization Procedure ◽  
Sole Source ◽  
Renewable Sources ◽  
Hydro Power ◽  
Ecological Awareness ◽  
Hydro Power Plants ◽  
Feed In Tariff

The apparent increase of ecological awareness during the previous decade has led to the need for adaptation of numerous areas of everyday human lives, so they fit the newly developed environmental tendencies. One of the areas in which this necessity was prominent was the power engineering, where, above all, the understanding of production systems was affected, with the turnabout regarding the distancing from the conventional sources and inclining towards the more acceptable renewable sources taking place. However, alongside the undeniable ecological sustainability of these sources, the financial sustainability of their utilization needs to be considered as well, which is why the subject of this paper is the minimization of costs of energy production in these capacities, if the fictious demand area needed to be supplied. It was assumed that this area is separated from transmission and distribution network, with the sole source of energy for the analyzed consumption being the wind, solar and hydro power plants, located in the close vicinity of the examined area. As the objective of the optimization, performed by the method of Lagrange multipliers, the appropriate apportioning of the generation powers in these plants for every hour during the year was selected, so the total yearly costs of supplying the demand were as low as possible. Also, considering the currently promoted methods intended for encouragement of the potential investors to proceed with the projects that encompass renewable sources, the different cases for which the share of renewables included in the feed-in tariff was varied. Finally, in order to provide the full insight into the optimization procedure, for the randomly selected hour of the year and share of feed-in tariff in the generation capacities, the process of calculation of production powers in the described plants and multipliers needed for application of the selected optimization technique was enclosed.

Application of Honey-Bee Mating Optimization to Naphtha Reforming Reactor

2013 ◽  
Vol 11 (1) ◽  
pp. 293-308 ◽  
Author(s):  
Somayeh Karimi ◽  
Navid Mostoufi ◽  
Rahmat Sotudeh-Gharebagh
Keyword(s):  
Objective Function ◽  
Honey Bee ◽  
Process Model ◽  
Global Optimum ◽  
Inlet Temperature ◽  
Local Optimum ◽  
Naphtha Reforming ◽  
Decision Variables ◽  
Algorithm Comparison ◽  
Honey Bee Mating Optimization

Abstract Modeling and optimization of the process of continuous catalytic reforming (CCR) of naphtha was investigated. The process model is based on a network of four main reactions which was proved to be quite effective in terms of industrial application. Temperatures of the inlet of four reactors were selected as the decision variables. The honey-bee mating optimization (HBMO) and the genetic algorithm (GA) were applied to solve the optimization problem and the results of these two methods were compared. The profit was considered as the objective function which was subject to maximization. Optimization of the CCR moving bed reactors to reach maximum profit was carried out by the HBMO algorithm and the inlet temperature reactors were considered as decision variables. The optimization results showed that an increase of 3.01% in the profit can be reached based on the results of the HBMO algorithm. Comparison of the performance of optimization by the HBMO and the GA for the naphtha reforming model showed that the HBMO is an effective and rapid converging technique which can reach a better optimum results than the GA. The results showed that the HBMO has a better performance than the GA in finding the global optimum with fewer number of objective function evaluations. Also, it was shown that the HBMO is less likely to get stuck in a local optimum.

scholarly journals A Comparison between Dedicated and Flexible Manufacturing Systems: Optimization and Sensitivity Analysis

2021 ◽  
Vol 40 (1) ◽  
pp. 130-139
Author(s):  
Abdul Salam Khan ◽  
Khawer Naeem ◽  
Raza Ullah Khan
Keyword(s):  
Sensitivity Analysis ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Cost Minimization ◽  
Process Time ◽  
Future Research ◽  
Second Phase ◽  
Solution Approach ◽  
The Impact

An abrupt change requires a robust and flexible response from a manufacturing system. Dedicated Manufacturing System (DMS) has been a long practiced taxonomy for mass production and minimum varieties. In contrast, Flexible Manufacturing System (FMS) has been introduced for responding to quantity as well as variety issues. This study considers both production taxonomics by using a multi objective model of cost and time. An Integer Linear Programming (ILP) formulation is presented and subsequently validated. The analysis procedure is administered in two phases. In the first phase, comparison of production cost and process time in DMS and FMS is presented. The model is implemented by using an exact solution approach and results show that FMS is a viable option, compared to DMS, according to the criteria of cost, time, and productivity. In the second phase, sensitivity analysis is performed by using several FMS (n) and the impact of cells selection on the performance of system is studied. It is concluded that n=1 (single cell-based FMS) is more relevant for cost minimization; however, n = 6 is a suitable candidate for producing more quantity in given time horizon (process time minimization). Lastly, key findings are reported, and future research avenues are provided.

scholarly journals A Multiple Mitosis Genetic Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 252
Author(s):  
K. Kamil ◽  
K.H Chong ◽  
H. Hashim ◽  
S.A. Shaaya
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Total Population ◽  
Global Optimum ◽  
Natural Process ◽  
Local Optimum ◽  
Simple Genetic Algorithm ◽  
Cell Reproduction ◽  
Solve Optimization Problem ◽  
High Diversity

<p>Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum.  This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.</p>

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