Multiobjective Optimization and Multiattribute Decision Making Study of Ship’s Principal Parameters in Conceptual Design

2009 ◽  
Vol 53 (02) ◽  
pp. 83-92
Author(s):  
Li Xuebin
Keyword(s):  
Decision Making ◽  
Multiobjective Optimization ◽  
Conceptual Design ◽  
Hybrid Approach ◽  
Ship Design ◽  
Subsequent Stage ◽  
Entropy Weight ◽  
Pareto Solution ◽  
Analysis Process ◽  
Conflicting Objectives

Numerous real-world problems relating to ship design are characterized by many alternatives as well as multiple conflicting objectives. Ship design is a complex endeavor requiring the successful coordination of many different disciplines, both technical and nontechnical. Conceptual design is the least defined stage of the ship design process and seeks to define the basic payloads and ship size characteristics. A hybrid approach for multiobjective optimization study of ship's principal parameters in conceptual design is proposed in the present analysis. In the first stage, a multiple objective genetic algorithm (MOGA) is employed to approximate the set of Pareto solution through an evolutionary optimization process. In the subsequent stage, a multiattribute decision making (MADM) approach is adopted to rank these solutions from best to worst and to determine the best solution in a deterministic environment with a single decision maker. A bulk carrier example, with 6 parameters, 3 criteria, and 14 constraints is conducted to illustrate the analysis process in present study. Pareto frontiers are obtained, and the ranking of the Pareto solution set is based on entropy weight and TOPSIS method. The ideal solution is compared with those from classic multiobjective methods.

scholarly journals Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1262 ◽  
Author(s):  
Xiaoping Fang ◽  
Yaoming Cai ◽  
Zhihua Cai ◽  
Xinwei Jiang ◽  
Zhikun Chen
Keyword(s):  
Multiobjective Optimization ◽  
Extreme Learning Machine ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Spectral Band ◽  
Feature Learning ◽  
Pareto Solution ◽  
Conflicting Objectives ◽  
Learning Machine ◽  
Hidden Layer

Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.

Multi-Objective Optimization and Parameter Tuning for Turbine PID Controller

2013 ◽  
Vol 779-780 ◽  
pp. 971-976
Author(s):  
Yuan Sheng Lin ◽  
Yong Li ◽  
Fei Fei Song ◽  
Da Wei Teng
Keyword(s):  
Decision Making ◽  
Pid Controller ◽  
Optimization Technique ◽  
Disturbance Attenuation ◽  
Subsequent Stage ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Acceptable Solution ◽  
Multi Objective ◽  
Multi Attribute Decision Making

The tuning of PID controller parameters is the most important task in PID design process. A new tuning method is presented for PID parameters, based on multi-objective optimization technique and multi-attribute decision making method. Three performances of a PID controller, i.e. the accurate set point tracking, disturbance attenuation and robust stability are studied simultaneously. These specifications are usually competitive and any acceptable solution requires a tradeoff among them. A hybrid approaches is proposed. In the first stage, a Non-dominated Sorting Genetic Algorithm II (NSGA II) is employed to approximate the set of Pareto solution through an evolutionary optimization process. In the subsequent stage, a multi-attribute decision making (MADM) approach is adopted to rank these solutions from best to worst and to determine the best solution in a deterministic environment with a single decision maker. The ranking of Pareto solution is based on entropy weight and TOPSIS method. A turbine PID design example is conducted to illustrate the analysis process in present study. The effectiveness of this universal framework is supported by the simulation results.

scholarly journals Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process

2021 ◽  
Author(s):  
Luciano Ferreira Cruz ◽  
Flavia Bernardo Pinto ◽  
Lucas Camilotti ◽  
Angelo Marcio Oliveira Santanna ◽  
Roberto Zanetti Freire ◽  
...  
Keyword(s):  
3D Printing ◽  
Multiobjective Optimization ◽  
Differential Evolution ◽  
Hybrid Approach ◽  
Machining Process ◽  
Nsga Ii ◽  
Printing Technology ◽  
3D Printing Technology ◽  
Conflicting Objectives ◽  
Engineering Problems

Abstract Multiobjective optimization approaches have allowed the improvement of technical features in industrial processes, focusing on more accurate approaches for solving complex engineering problems and support decision-making. This paper proposes a hybrid approach to optimize the 3D printing technology parameters, integrating the design of experiments and multiobjective optimization methods, as an alternative to classical parametrization design used in machining processes. Alongside the approach, a multiobjective differential evolution with uniform spherical pruning (usp-MODE) algorithm is proposed to serve as an optimization tool. The parametrization design problem considered in this research has the following three objectives: to minimize both surface roughness and dimensional accuracy while maximizing the mechanical resistance of the prototype. A benchmark with non-dominated sorting genetic algorithm II (NSGA-II) and with the classical sp-MODE is used to evaluate the performance of the proposed algorithm. With the increasing complexity of engineering problems and advances in 3D printing technology, this study demonstrates the applicability of the proposed hybrid approach, finding optimal combinations for the machining process among conflicting objectives regardless of the number of decision variables and goals involved. To measure the performance and to compare the results of metaheuristics used in this study, three Pareto comparison metrics have been utilized to evaluate both the convergence and diversity of the obtained Pareto approximations for each algorithm: hyper-volume (H), g-Indicator (G), and inverted generational distance (IGD). To all of them, ups-MODE outperformed, with significant figures, the results reached by NSGA-II and sp-MODE algorithms.

Conceptual Design and Optimization of Submarine Escape Capsule

2013 ◽  
Vol 744 ◽  
pp. 143-146
Author(s):  
Xiang Bing Huang ◽  
Xing Ling Huang
Keyword(s):  
Decision Making ◽  
Conceptual Design ◽  
Design Process ◽  
Multiple Attribute Decision Making ◽  
Performance Parameters ◽  
Entropy Weight ◽  
Design And Optimization ◽  
General Performance ◽  
Multiple Attribute ◽  
Technology Level

Submarine escape capsule is an effective equipment to improve submariners viability when accident. In order to design a reasonable escape system, three alternatives, sphere chamber, ellipsoid chamber and combined chamber, were presented in the conceptual design process. The factors which influenced decision making were researched subsequently, such as general performance parameters, dependence on submarines position when releasing, safety of the product in hazardous conditions and the products influence on submarine. Then the influenced factors and their attributes were evaluated in three alternatives, and a multiple attribute decision making approach based on entropy weight was used to optimize the conceptual design. The results showed that combined chamber was the best choice for submarine escape capsule because its overall technology level was superior to others.

scholarly journals Integrated Multi-Criteria Decision-Making Methods for Selection of Supply Chain Partner for Supply Chain Management

2021 ◽  
Vol 9 (VII) ◽  
pp. 952-957
Author(s):  
Prem K. Soni
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supplier Selection ◽  
Multi Criteria Decision Making ◽  
Entropy Weight ◽  
Business World ◽  
Manufacturing Firm ◽  
Piv Method ◽  
Conflicting Objectives ◽  
Entropy Weight Method

In today’s competitive business world, it is extremely important for decision makers to have access to decision support tools in order to make quick, right and accurate decisions. One of these decision-making areas is supplier or service provider selection. Supplier selection is a multi – criteria decision making process that deals with the optimization of conflicting objectives such as quality, services, cost, and delivery time. Although numbers of multiple criteria decisions making (MCDM) methods are available for solving MCDM problem, it’s observed that in most of these methods the ranking results are very sensitive. This work proposes a multi-criteria decision making (MCDM) based framework that is used to evaluate supplier selection by using an entropy weight method (EWM) for calculation of weightage of each criterion, once the weightage is calculated the EWM is combined with Proximity Indexed Value (PIV) Method for calculating the supplier rank. Finally, the ranking performance of PIV method is compared with other MCDM Methods for same set of alternative and criterions. A numerical example along with graphical illustrations is considered and comparison analysis is provided to test the feasibility of the proposed method. In the illustrative example a manufacturing firm is looking for select most suitable supplier for supply among the ten-supplier based on four different criteria such as Price/Cost, Service, Quality and Delivery, in which Price/Cost is non-beneficial and the attributes pertaining to other criteria are beneficial one.

A Soft Hierarchical Process Approach for Decision Making in A Supply Chain

2015 ◽  
Vol 4 (1and2) ◽  
Author(s):  
Rajeev Dhingra ◽  
Preetvanti Singh
Keyword(s):  
Decision Making ◽  
Process Approach ◽  
Decision Makers ◽  
Analytic Hierarchy ◽  
Conflicting Objectives ◽  
Realistic View ◽  
Hierarchy Process ◽  
Pair Wise Comparison ◽  
Supplier Selection Problem ◽  
Selection Of

Decision problems are usually complex and involve evaluation of several conflicting criteria (parameters). Multi Criteria Decision Making (MCDM) is a promising field that considers the parallel influence of all criteria and aims at helping decision makers in expressing their preferences, over a set of predefined alternatives, on the basis of criteria (parameters) that are contradictory in nature. The Analytic Hierarchy Process (AHP) is a useful and widespread MCDM tool for solving such type of problems, as it allows the incorporation of conflicting objectives and decision makers preferences in the decision making. The AHP utilizes the concept of pair wise comparison to find the order of criteria (parameters) and alternatives. The comparison in a pairwise manner becomes quite tedious and complex for problems having eight alternatives or more, thereby, limiting the application of AHP. This paper presents a soft hierarchical process approach based on soft set decision making which eliminates the least promising candidate alternatives and selects the optimum(potential) ones that results in the significant reduction in the number of pairwise comparisons necessary for the selection of the best alternative using AHP, giving the approach a more realistic view. A supplier selection problem is used to illustrate the proposed approach.

scholarly journals Evaluating the Security Impact of Healthcare Web Applications Through Fuzzy Based Hybrid Approach of Multi-Criteria Decision-Making Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 135770-135783
Author(s):  
Alka Agrawal ◽  
Abhishek Kumar Pandey ◽  
Abdullah Baz ◽  
Hosam Alhakami ◽  
Wajdi Alhakami ◽  
...  
Keyword(s):  
Decision Making ◽  
Web Applications ◽  
Hybrid Approach ◽  
Multi Criteria Decision Making

Application of Unascertained Measure-Entropy Evaluation Model for Program Selection of Shaft Construction under Complex Conditions

2011 ◽  
Vol 225-226 ◽  
pp. 407-410 ◽  
Author(s):  
Wan Qing Li ◽  
Mu Jie Chen ◽  
Wen Qing Meng
Keyword(s):  
Decision Making ◽  
Evaluation Model ◽  
Entropy Weight ◽  
Program Selection ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Measure Entropy ◽  
Selection Of

An unascertained measure-entropy evaluation model for the program selection of shaft construction under complex conditions is established so that a scientific and effective decision making method is provided in this paper, the evaluation model of shaft construction is established based on unascertained measure and entropy weight theory, then, the model proposed in this paper is applied to evaluate three shaft construction program comprehensively, and the evaluation results show validity and applicability of the model.

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