Genetic Algorithm for Multi-Objective Exergetic and Economic Optimization of Parabolic Trough Collectors Integration Into Combined Cycle System (ISCCS)

2010 ◽  
Author(s):  
Ali Baghernejad ◽  
Mahmood Yaghoubi
Keyword(s):  
Decision Making ◽  
Pareto Frontier ◽  
Optimal Solution ◽  
Combined Cycle ◽  
Exergy Destruction ◽  
Exergetic Efficiency ◽  
Economic Optimization ◽  
Cost Rate ◽  
Multi Objective ◽  
Cycle System

Thermoeconomic analyses of any thermal system design are always based on the economic objectives. However, knowledge of economic optimization may not be sufficient for decision making process, since solutions with higher thermodynamic efficiency, in spite of small increases in total costs, may result in much more interesting designs due to changes in the energy market prices or in the energy policies. In this paper a multi-objective optimization scheme is developed and applied for an Integrated Solar Combined Cycle System (ISCCS) that produces 400 MW of electricity to find solutions that simultaneously satisfy exergetic as well as economic objectives. This corresponds to search for a set of Pareto optimal solutions with respect to the two competing objectives. The optimization process is carried out by a particular class of search algorithms known as multi-objective evolutionary algorithms (MOEAs). For such MOEAs, an example of decision-making is presented and a final optimal solution has been introduced. The final optimal solution that is selected in this analysis belongs to the region of Pareto Frontier with significant sensitivity to the costing parameters. However, the region with lower sensitivity to the costing parameter is not reasonable for the final optimum solution due to a weak equilibrium of Pareto Frontier in which a small changes in exergetic efficiency of plant due to variation of operating parameters may lead to the danger of increasing the cost rate of product, drastically. The analysis shows that optimization process leads to 3.2% increasing in the exergetic efficiency and 3.82% decreasing of the rate of product cost. Also optimization leads to the 2.73% reduction on the fuel exergy, 5.71% reductions in the total exergy destruction and also 3.46% and 7.32% reductions in the fuel cost rate and cost rate relating to the exergy destruction, respectively.

A two-sided matching decision-making approach based on regret theory under intuitionistic fuzzy environment

2021 ◽  
pp. 1-18
Author(s):  
Xiang Jia ◽  
Xinfan Wang ◽  
Yuanfang Zhu ◽  
Lang Zhou ◽  
Huan Zhou
Keyword(s):  
Decision Making ◽  
Hamming Distance ◽  
Optimal Solution ◽  
Intuitionistic Fuzzy Sets ◽  
Regret Theory ◽  
Multi Objective ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Objective Model ◽  
Single Objective

This study proposes a two-sided matching decision-making (TSMDM) approach by combining the regret theory under the intuitionistic fuzzy environment. At first, according to the Hamming distance of intuitionistic fuzzy sets and regret theory, superior and inferior flows are defined to describe the comparative preference of subjects. Hereafter, the satisfaction degrees are obtained by integrating the superior and inferior flows of the subjects. The comprehensive satisfaction degrees are calculated by aggregating the satisfaction degrees, based on which, a multi-objective TSMDM model is built. Furthermore, the multi-objective TSMDM model is converted to a single-objective model, the optimal solution of the latter is derived. Finally, an illustrative example and several analyses are provided to verify the feasibility and the effectiveness of the proposed approach.

A Method for Solving Multi-Objective Optimization Problems Containing an Infinite Number of Parameterized Objectives

2020 ◽  
Author(s):  
Eliot Rudnick-Cohen
Keyword(s):  
Decision Making ◽  
Infinite Number ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Brute Force ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Brute Force Approach ◽  
Better Than

Abstract Multi-objective decision making problems can sometimes involve an infinite number of objectives. In this paper, an approach is presented for solving multi-objective optimization problems containing an infinite number of parameterized objectives, termed “infinite objective optimization”. A formulation is given for infinite objective optimization problems and an approach for checking whether a Pareto frontier is a solution to this formulation is detailed. Using this approach, a new sampling based approach is developed for solving infinite objective optimization problems. The new approach is tested on several different example problems and is shown to be faster and perform better than a brute force approach.

scholarly journals A Multi-Objective Decision-Making Approach For Mutual Fund Portfolio

2011 ◽  
Vol 4 (6) ◽  
Author(s):  
Hari P. Sharma Hari P. Sharma ◽  
Dinesh K. Sharma
Keyword(s):  
Decision Making ◽  
Mutual Fund ◽  
Investment Decision ◽  
Optimal Solution ◽  
Ideal Solution ◽  
Font Size ◽  
Distance Method ◽  
Multi Objective ◽  
Fund Portfolio ◽  
Priority Structure

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-family: Times New Roman;"><span style="font-size: 10pt; mso-bidi-font-style: italic;">Investment decision-making problems ar</span><span style="font-size: 10pt;">e<span style="mso-bidi-font-style: italic;"> generally multi-objective in nature such as minimization of the risk and maximization of the expected return.<span style="mso-spacerun: yes;">&nbsp; </span>These problems can be solved efficiently and effectively using multi-objective decision making (MODM) tools such as a lexicographic goal programming (LGP).<span style="mso-spacerun: yes;">&nbsp; </span>This paper applies the LGP model for selecting an optimum mutual fund portfolio for an investor, while taking into account specific parameters including risk, return, expense ratio and others.<span style="mso-spacerun: yes;">&nbsp; </span>Sensitivity analysis on the assigned weights in a priority structure of the goals identifies all possible solutions for decision-making.<span style="mso-spacerun: yes;">&nbsp; </span>The Euclidean distance method is then used, to measure distances of all possible solutions from the identified ideal solution.<span style="mso-spacerun: yes;">&nbsp; </span>The optimal solution is determined by the minimum distance between the ideal solution and other possible solutions of the problem. The associated weights with the optimal solution will be the most appropriate weights in a given priority structure.<span style="mso-spacerun: yes;">&nbsp; </span>The effectiveness and applicability of the LGP model is demonstrated via a case example from broad categories of mutual funds.</span></span></span></p>

scholarly journals A Survey of Multi-Objective Sequential Decision-Making

2013 ◽  
Vol 48 ◽  
pp. 67-113 ◽  
Author(s):  
D. M. Roijers ◽  
P. Vamplew ◽  
S. Whiteson ◽  
R. Dazeley
Keyword(s):  
Decision Making ◽  
Pareto Front ◽  
Optimal Solution ◽  
Multiple Objectives ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Multi Objective ◽  
Scalarization Function ◽  
Future Work ◽  
Single Objective

Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-objective problems. Therefore, we identify three distinct scenarios in which converting such a problem to a single-objective one is impossible, infeasible, or undesirable. Furthermore, we propose a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function (which projects multi-objective values to scalar ones), and the type of policies considered. We show how these factors determine the nature of an optimal solution, which can be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we survey the literature on multi-objective methods for planning and learning. Finally, we discuss key applications of such methods and outline opportunities for future work.

Multi-Objective Optimization Design of Steel Frames Based on Multiple Attribute Decision-Making Method

2013 ◽  
Vol 368-370 ◽  
pp. 830-837
Author(s):  
Mao Qiao Cui ◽  
Hai Yan Huang ◽  
Fu Lai Wang ◽  
Yong Qiu
Keyword(s):  
Decision Making ◽  
Optimization Design ◽  
Intuitionistic Fuzzy Set ◽  
Optimal Solution ◽  
Multiple Attribute Decision Making ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multiple Attribute ◽  
High Level

This paper describes in detail a multi-objective optimization strategy and decision-making method in the process of steel frame optimization design. A step-by-step analysis process integrating optimization algorithm and model analysis is proposed to solve the present problem. A multi-objective algorithm method using fast Non-dominated Sorting Genetic Algorithm is employed to obtain the Pareto-optimal solution set through an evolutionary optimization process. A high-level multiple attribute decision-making method based on intuitionistic fuzzy set theory is adopted to rank these solutions from the best to worst, and to determine the best solution. An example is used to demonstrate the proposed optimization model and decision-making method.

Research of Data Storage Technique Based on Cloud Storage Environment

2014 ◽  
Vol 602-605 ◽  
pp. 3160-3164
Author(s):  
Ping Zong ◽  
Rui Min Hu ◽  
Jie Wang
Keyword(s):  
Decision Making ◽  
Data Storage ◽  
Cloud Storage ◽  
Comprehensive Evaluation ◽  
Optimal Solution ◽  
Multi Objective ◽  
Multiple Attributes ◽  
Cloud Storage Environment ◽  
Storage Technique

According to the diversity in the nodes of cloud storage, this paper puts forward a replication layout strategy based on TOPSIS. We can apply it in the replication layout strategy in order to make full use of its advantages in multi-objective decision-making. The replication layout strategy, based on TOPSIS, uses HDFS’s racks awareness function; it makes the comprehensive evaluation of multiple attributes of multiple nodes, and by calculating the “distance” from each node to the optimal solution and the worst solution, it can choose the deployment of nodes to realize the efficiency of replication layout.

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