Interactive Multiobjective Optimization Design Strategy for Decision Based Design

1999 ◽  
Vol 123 (2) ◽  
pp. 205-215 ◽  
Author(s):  
Ravindra V. Tappeta ◽  
John E. Renaud
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Design ◽  
Optimization Procedure ◽  
Design Strategy ◽  
Test Problems ◽  
Efficient Design ◽  
Surface Approximation ◽  
Multi Objective ◽  
Local Preference ◽  
Preference Functions

This research focuses on multi-objective system design and optimization. The primary goal is to develop and test a mathematically rigorous and efficient interactive multi-objective optimization algorithm that takes into account the Decision Maker’s (DM’s) preferences during the design process. An interactive MultiObjective Optimization Design Strategy (iMOODS) has been developed in this research to include the Pareto sensitivity analysis, Pareto surface approximation and local preference functions to capture the DM’s preferences in an Iterative Decision Making Strategy (IDMS). This new multiobjective optimization procedure provides the DM with a formal means for efficient design exploration around a given Pareto point. The use of local preference functions allows the iMOODS to construct the second order Pareto surface approximation more accurately in the preferred region of the Pareto surface. The iMOODS has been successfully applied to two test problems. The first problem consists of a set of simple analytical expressions for the objective and constraints. The second problem is the design and sizing of a high-performance and low-cost ten bar structure that has multiple objectives. The results indicate that the class functions are effective in capturing the local preferences of the DM. The Pareto designs that reflect the DM’s preferences can be efficiently generated within IDMS.

Interactive Multiobjective Optimization Design Strategy for Decision Based Design

1999 ◽  
Author(s):  
Ravindra V. Tappeta ◽  
John E. Renaud
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Design ◽  
Optimization Procedure ◽  
Design Strategy ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Original Algorithm ◽  
Multi Objective ◽  
Local Preference ◽  
Preference Functions

Abstract This research focuses on multi-objective system design and optimization. The primary goal is to develop and test a mathematically rigorous and efficient interactive multi-objective optimization algorithm that takes into account the Decision Maker’s (DM’s) preferences during the design process. An Interactive Multi-Objective Optimization Procedure (IMOOP) developed in [12] has been modified in this research to include the DM’s local preference functions in an Iterative Decision Making Strategy (IDMS). This enhanced multiobjective optimization procedure called the interactive MultiObjective Optimization Design Strategy (iMOODS) provides the DM with a formal means for efficient design exploration around a given Pareto point. The use of local preference functions allows the original algorithm [12] to be modified such that the second order Pareto surface approximation is more accurate in the preferred region of the Pareto surface. The iMOODS has been successfully applied to two test problems. The first problem consists of a set of simple analytical expressions for the objectives and constraints. The second problem is the design and sizing of a high-performance and low-cost ten bar structure that has multiple objectives. The results indicate that the class functions are effective in capturing the local preferences of the DM. The Pareto designs that reflect the DM’s preferences can be efficiently generated within IDMS.

scholarly journals Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-37 ◽  
Author(s):  
Wenping Zou ◽  
Yunlong Zhu ◽  
Hanning Chen ◽  
Beiwei Zhang
Keyword(s):  
Multiobjective Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Difficult Problem ◽  
Standard Test ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Flight Direction ◽  
Bee Colony

Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.

scholarly journals Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

2021 ◽  
Vol 13 (4) ◽  
pp. 1929
Author(s):  
Yongmao Xiao ◽  
Wei Yan ◽  
Ruping Wang ◽  
Zhigang Jiang ◽  
Ying Liu
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Design Method ◽  
Low Cost ◽  
Route Optimization ◽  
Low Carbon ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Blank Design ◽  
Process Route

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

scholarly journals Elite Exploitation: A Combination of Mathematical Concept and EMO Approach for Multi-Objective Decision Making

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 136
Author(s):  
Wenxiao Li ◽  
Yushui Geng ◽  
Jing Zhao ◽  
Kang Zhang ◽  
Jianxin Liu
Keyword(s):  
Decision Making ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Decision Makers ◽  
Superior Performance ◽  
Test Problems ◽  
Mathematical Function ◽  
Hyperbolic Tangent ◽  
Nsga Ii ◽  
Multi Objective

This paper explores the combination of a classic mathematical function named “hyperbolic tangent” with a metaheuristic algorithm, and proposes a novel hybrid genetic algorithm called NSGA-II-BnF for multi-objective decision making. Recently, many metaheuristic evolutionary algorithms have been proposed for tackling multi-objective optimization problems (MOPs). These algorithms demonstrate excellent capabilities and offer available solutions to decision makers. However, their convergence performance may be challenged by some MOPs with elaborate Pareto fronts such as CFs, WFGs, and UFs, primarily due to the neglect of diversity. We solve this problem by proposing an algorithm with elite exploitation strategy, which contains two parts: first, we design a biased elite allocation strategy, which allocates computation resources appropriately to elites of the population by crowding distance-based roulette. Second, we propose a self-guided fast individual exploitation approach, which guides elites to generate neighbors by a symmetry exploitation operator, which is based on mathematical hyperbolic tangent function. Furthermore, we designed a mechanism to emphasize the algorithm’s applicability, which allows decision makers to adjust the exploitation intensity with their preferences. We compare our proposed NSGA-II-BnF with four other improved versions of NSGA-II (NSGA-IIconflict, rNSGA-II, RPDNSGA-II, and NSGA-II-SDR) and four competitive and widely-used algorithms (MOEA/D-DE, dMOPSO, SPEA-II, and SMPSO) on 36 test problems (DTLZ1–DTLZ7, WGF1–WFG9, UF1–UF10, and CF1–CF10), and measured using two widely used indicators—inverted generational distance (IGD) and hypervolume (HV). Experiment results demonstrate that NSGA-II-BnF exhibits superior performance to most of the algorithms on all test problems.

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