Multi-Objective Optimization of Piezoelectric Actuator Placement for Shape Control of Plate Using Genetic Algorithms

Shape control of adaptive structures using piezoelectric actuators has found a wide range of applications in recent years. In this paper, the problem of finding optimal distribution of piezoelectric actuators and corresponding actuation voltages for static shape control of a plate is formulated as a multi-objective optimization problem. The two conflicting objectives considered are minimization of input control energy and minimization of mean square deviation between the desired and actuated shapes with constraints on the maximum number of actuators and maximum induced stresses. A shear lag model of the smart plate structure is created, and the optimization problem is solved using an evolutionary multi-objective optimization algorithm: nondominated sorting genetic algorithm-II. Pareto-optimal solutions are obtained for different case studies. Further, the obtained solutions are verified by comparing them with the single-objective optimization solutions. Attainment surface based performance evaluation of the proposed optimization algorithm has been carried out.

Download Full-text

Hybrid QPSO-NNIA2 Algorithm for Multi-Objective Optimization Problem

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001419590250 ◽

2019 ◽

Vol 33 (08) ◽

pp. 1959025

Author(s):

Lan Zhang

Keyword(s):

Optimization Algorithm ◽

Optimization Problem ◽

Nearest Neighbor ◽

Superior Performance ◽

Optimal Solutions ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Evolution Algebra ◽

Neighbor List

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

Download Full-text

Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

Journal of Marine Science and Engineering ◽

10.3390/jmse9080888 ◽

2021 ◽

Vol 9 (8) ◽

pp. 888

Author(s):

Qasem Al-Tashi ◽

Emelia Akashah Patah Akhir ◽

Said Jadid Abdulkadir ◽

Seyedali Mirjalili ◽

Tareq M. Shami ◽

...

Keyword(s):

Feature Selection ◽

Recovery Factor ◽

Classification Error ◽

Grey Wolf Optimizer ◽

Small Subset ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Conflicting Objectives

The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate classification of reservoir recovery factor, as the generated reservoir features are usually heterogeneous. Consequently, it is imperative to select relevant reservoir features while preserving or amplifying reservoir recovery accuracy. This phenomenon can be treated as a multi-objective optimization problem, since there are two conflicting objectives: minimizing the number of measurements and preserving high recovery classification accuracy. In this study, wrapper-based multi-objective feature selection approaches are proposed to estimate the set of Pareto optimal solutions that represents the optimum trade-off between these two objectives. Specifically, three multi-objective optimization algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Particle Swarm Optimization (MOPSO)—are investigated in selecting relevant features from the reservoir dataset. To the best of our knowledge, this is the first time multi-objective optimization has been used for reservoir recovery factor classification. The Artificial Neural Network (ANN) classification algorithm is used to evaluate the selected reservoir features. Findings from the experimental results show that the proposed MOGWO-ANN outperforms the other two approaches (MOPSO and NSGA-II) in terms of producing non-dominated solutions with a small subset of features and reduced classification error rate.

Download Full-text

Multi-objective optimization of retaining wall using genetic algorithm

Environmental engineering ◽

10.37023/ee.8.1-2.8 ◽

2021 ◽

Vol 8 (1-2) ◽

pp. 58-65

Author(s):

Filip Dodigović ◽

Krešo Ivandić ◽

Jasmin Jug ◽

Krešimir Agnezović

Keyword(s):

Genetic Algorithm ◽

Optimization Problem ◽

Retaining Wall ◽

Silty Sand ◽

Embedment Depth ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Definition Of ◽

The Cost

The paper investigates the possibility of applying the genetic algorithm NSGA-II to optimize a reinforced concrete retaining wall embedded in saturated silty sand. Multi-objective constrained optimization was performed to minimize the cost, while maximizing the overdesign factors (ODF) against sliding, overturning, and soil bearing resistance. For a given change in ground elevation of 5.0 m, the width of the foundation and the embedment depth were optimized. Comparing the algorithm's performance in the cases of two-objective and three objective optimizations showed that the number of objectives significantly affects its convergence rate. It was also found that the verification of the wall against the sliding yields a lower ODF value than verifications against overturning and soil bearing capacity. Because of that, it is possible to exclude them from the definition of optimization problem. The application of the NSGA-II algorithm has been demonstrated to be an effective tool for determining the set of optimal retaining wall designs.

Download Full-text

Optimal Photovoltaic System Design with Multi-Objective Optimization

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2013100104 ◽

2013 ◽

Vol 4 (4) ◽

pp. 63-89 ◽

Cited By ~ 6

Author(s):

Amin Ibrahim ◽

Farid Bourennani ◽

Shahryar Rahnamayan ◽

Greg F. Naterer

Keyword(s):

Incident Energy ◽

State Of The Art ◽

Photovoltaic System ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Conflicting Objectives ◽

Generalized Differential ◽

Maximum Field ◽

Real World Problems

Recently, several parts of the world suffer from electrical black-outs due to high electrical demands during peak hours. Stationary photovoltaic (PV) collector arrays produce clean and sustainable energy especially during peak hours which are generally day time. In addition, PVs do not emit any waste or emissions, and are silent in operation. The incident energy collected by PVs is mainly dependent on the number of collector rows, distance between collector rows, dimension of collectors, collectors inclination angle and collectors azimuth, which all are involved in the proposed modeling in this article. The objective is to achieve optimal design of a PV farm yielding two conflicting objectives namely maximum field incident energy and minimum of the deployment cost. Two state-of-the-art multi-objective evolutionary algorithms (MOEAs) called Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Generalized Differential Evolution Generation 3 (GDE3) are compared to design PV farms in Toronto, Canada area. The results are presented and discussed to illustrate the advantage of utilizing MOEA in PV farms design and other energy related real-world problems.

Download Full-text

Multi-Objective Evolutionary Algorithms

Advances in Chemical and Materials Engineering - Computational Approaches to Materials Design ◽

10.4018/978-1-5225-0290-6.ch011 ◽

2016 ◽

pp. 301-345

Author(s):

A. K. Nandi ◽

K. Deb

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithms ◽

Optimization Problem ◽

Performance Metrics ◽

Research Work ◽

Primary Objective ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Mould Material ◽

Multi Objective

The primary objective in designing appropriate particle reinforced polyurethane composite which will be used as a mould material in soft tooling process is to minimize the cycle time of soft tooling process by providing faster cooling rate during solidification of wax/plastic component. This chapter exemplifies an effective approach to design particle reinforced mould materials by solving the inherent multi-objective optimization problem associated with soft tooling process using evolutionary algorithms. In this chapter, first a brief introduction of multi-objective optimization problem with the key issues is presented. Then, after a short overview on the working procedure of genetic algorithm, a well- established multi-objective evolutionary algorithm, namely NSGA-II along with various performance metrics are described. The inherent multi-objective problem in soft tooling process is demonstrated and subsequently solved using an elitist non-dominated sorting genetic algorithm, NSGA-II. Multi-objective optimization results obtained using NSGA-II are analyzed statistically and validated with real industrial application. Finally the fundamental results of this approach are summarized and various perspectives to the industries along with scopes for future research work are pointed out.

Download Full-text

Multi-Objective Optimization of Internal Combustion Engine Valve Springs Based on NSGA-II

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.945-949.473 ◽

2014 ◽

Vol 945-949 ◽

pp. 473-477

Author(s):

You Jian Wang ◽

Guang Zhang

Keyword(s):

Genetic Algorithm ◽

Internal Combustion Engine ◽

Optimization Problem ◽

Combustion Engine ◽

Graphical Methods ◽

Multi Objective Optimization ◽

Design Quality ◽

Nsga Ii ◽

Engine Valve ◽

Multi Objective

The design of engine valve spring generally belongs to multi-objective optimum design. The traditional trying means and the graphical methods are difficult to solve the multi-objective optimization problem, and the traditional multi-objective algorithms have certain defects. The elitist non-dominated sorting genetic algorithm (NSGA-II) is an excellent multi-objective algorithm, which is widely used to solve problems of multi-objective optimization. This method can improve the design quality and efficiency, and it has much more engineering practical value.

Download Full-text

Improved hybrid Strength Pareto Evolutionary Algorithms for multi-objective optimization

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-12-2016-0063 ◽

2018 ◽

Vol 11 (1) ◽

pp. 20-46 ◽

Cited By ~ 4

Author(s):

K. Shankar ◽

Akshay S. Baviskar

Keyword(s):

Engineering Design ◽

Nsga Ii ◽

Design Problems ◽

Content Type ◽

Multi Objective ◽

Decision Variables ◽

Engineering Design Problems ◽

Wide Range ◽

Conflicting Objectives ◽

Utopia Point

Purpose The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems. Design/methodology/approach This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search toward PF. In second method, boundary points of the PF are calculated and their decision variables are seeded to the initial population. Findings The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics. Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms (such as NSGA-II and SPEA2) and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions. It is also tested on an engineering design problem and compared with a currently used algorithm. Practical implications The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives. A complex example of Welded Beam has been shown at the end of the paper. Social implications The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives. Originality/value This paper presents two novel hybrid algorithms involving SPEA2 based on: local search; and Utopia point directed search principles. This concept has not been investigated before.

Download Full-text

Multi-Objective Evolutionary Algorithms

Materials Science and Engineering ◽

10.4018/978-1-5225-1798-6.ch008 ◽

2017 ◽

pp. 185-229

Author(s):

A. K. Nandi ◽

K. Deb

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithms ◽

Optimization Problem ◽

Performance Metrics ◽

Research Work ◽

Primary Objective ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Mould Material ◽

Multi Objective

The primary objective in designing appropriate particle reinforced polyurethane composite which will be used as a mould material in soft tooling process is to minimize the cycle time of soft tooling process by providing faster cooling rate during solidification of wax/plastic component. This chapter exemplifies an effective approach to design particle reinforced mould materials by solving the inherent multi-objective optimization problem associated with soft tooling process using evolutionary algorithms. In this chapter, first a brief introduction of multi-objective optimization problem with the key issues is presented. Then, after a short overview on the working procedure of genetic algorithm, a well- established multi-objective evolutionary algorithm, namely NSGA-II along with various performance metrics are described. The inherent multi-objective problem in soft tooling process is demonstrated and subsequently solved using an elitist non-dominated sorting genetic algorithm, NSGA-II. Multi-objective optimization results obtained using NSGA-II are analyzed statistically and validated with real industrial application. Finally the fundamental results of this approach are summarized and various perspectives to the industries along with scopes for future research work are pointed out.

Download Full-text

Utilizing Multiple Objectives for the Optimization of the Pultrusion Process Based on a Thermo-Chemical Simulation

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.554-557.2165 ◽

2013 ◽

Vol 554-557 ◽

pp. 2165-2174 ◽

Cited By ~ 17

Author(s):

Cem C. Tutum ◽

Ismet Baran ◽

Jesper Hattel

Keyword(s):

Optimization Problem ◽

Operating Conditions ◽

Die Design ◽

Process Efficiency ◽

Optimum Process ◽

Pareto Optimal ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Pultrusion Process ◽

Multi Objective

Pultrusion is one of the most effective manufacturing processes for producing composites with constant cross-sectional profiles. This obviously makes it more attractive for both researchers and practitioners to investigate the optimum process parameters, i.e. pulling speed, power and dimensions of the heating platens, length and width of the heating die, design of the resin injection chamber, etc., to provide better understanding of the process, consequently to improve the efficiency of the process as well the product quality. Numerous simulation approaches have been presented until now. However, optimization studies had been limited with either experimental cases or determining only one objective to improve one aspect of the performance of the process. This objective is either augmented by other process related criteria or subjected to constraints which might have had the same importance of being treated as objectives. In essence, these approaches convert a true multi-objective optimization problem (MOP) into a single-objective optimization problem (SOP). This transformation obviously results in only one optimum solution and it does not support the efforts to get more out of an optimization study, such as relations between variables and objectives or constraints. In this study, an MOP considering thermo-chemical aspects of the pultrusion process (e.g. cure degree, temperatures), in which the pulling speed is maximized and the heating power is minimized simultaneously (without defining any preference between them), has been formulated. An evolutionary multi-objective optimization (EMO) algorithm, non-dominated sorting genetic algorithm (NSGA-II [Deb et al., 2002]), has been used to solve this MOP in an ideal way where the outcome is the set of multiple solutions (i.e. Pareto-optimal solutions) and each solution is theoretically an optimal solution corresponding to a particular trade-off among objectives. Following the solution process, in other words obtaining the Pareto-optimal front, a further postprocessing study has been performed to unveil some common principles existing between the variables, the objectives and the constraints either along the whole front or in some portion of it. These relationships will reveal a design philosophy not only for the improvement of the process efficiency, but also a methodology to design a pultrusion die for different operating conditions.

Download Full-text