Collective intelligence approaches in interactive evolutionary multi-objective optimization

2020 ◽  
Vol 28 (1) ◽  
pp. 95-108 ◽  
Author(s):  
Daniel Cinalli ◽  
Luis Martí ◽  
Nayat Sanchez-Pi ◽  
Ana Cristina Bicharra Garcia
Keyword(s):  
Collective Intelligence ◽  
Real Life ◽  
Decision Makers ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Trade Offs ◽  
Life Problems ◽  
Conflicting Objectives ◽  
Optimal Set

Abstract Evolutionary multi-objective optimization algorithms (EMOAs) have been successfully applied in many real-life problems. EMOAs approximate the set of trade-offs between multiple conflicting objectives, known as the Pareto optimal set. Reference point approaches can alleviate the optimization process by highlighting relevant areas of the Pareto set and support the decision makers to take the more confident evaluation. One important drawback of this approaches is that they require an in-depth knowledge of the problem being solved in order to function correctly. Collective intelligence has been put forward as an alternative to deal with situations like these. This paper extends some well-known EMOAs to incorporate collective preferences and interactive techniques. Similarly, two new preference-based multi-objective optimization performance indicators are introduced in order to analyze the results produced by the proposed algorithms in the comparative experiments carried out.

Dynamic Template Generation

2021 ◽  
pp. 1-37
Keyword(s):  
Optimization Problems ◽  
Real Life ◽  
Dynamic Test ◽  
Test Paper ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Life Problems ◽  
Conflicting Objectives ◽  
Complex Optimization ◽  
Dynamic Template

A test blueprint/test template, also known as the table of specifications, represents the structure of a test. It has been highly recommended in assessment textbook to carry out the preparation of a test with a test blueprint. This chapter focuses on modeling a dynamic test paper template using multi-objective optimization algorithm and makes use of the template in dynamic generation of examination test paper. Multi-objective optimization-based models are realistic models for many complex optimization problems. Modeling a dynamic test paper template, similar to many real-life problems, includes solving multiple conflicting objectives satisfying the template specifications.

scholarly journals A Decomposition-Based Interactive Method for Multi-Objective Evolutionary Algorithm

2012 ◽  
Author(s):  
Nguye Long ◽  
Bui Thu Lam
Keyword(s):  
Optimization Problems ◽  
Decision Makers ◽  
Optimization Process ◽  
Multi Objective Optimization ◽  
Interactive Method ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Single Solution ◽  
Optimal Set

Multi-objectivity has existed in many real-world optimization problems. In most multi-objective cases, objectives are often conflicting, there is no single solution being optimal with regards to all objectives. These problems are called Multi-objective Optimization Problems (MOPs). To date, there have been al large number of methods for solving MOPs including evolutionary methods (namly Multi-objective Evolutionary Algorithms MOEAs). With the use of a population of solutions for searching. MOEAs are naturally suitable for approximating optimal solutions (called the Pareto Optimal Set (POS) or the efficient set). There has been a popular trend in MOEAs considering the role of Decision Makers (DMs) during the optimization process (known as the human-in-loop) for checking, analyzing the results and giving the preference to guide the optimization process. This is call the interactive method.

Multi-Objective Optimization Analysis of Motor Cooling System in Articulated Dump Truck

2011 ◽  
Vol 383-390 ◽  
pp. 4715-4720
Author(s):  
Yan Zhang ◽  
Yan Hua Shen ◽  
Wen Ming Zhang
Keyword(s):  
Field Distribution ◽  
Cooling System ◽  
Dump Truck ◽  
Water Cooling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Temperature And Pressure ◽  
Water Cooling System ◽  
Optimal Set

In order to ensure the reliable and safe operation of the electric driving motor of the articulated dump truck, water cooling system is installed for each motor. For the best performance of the water cooling system, not only the heat transfer should be enhanced to maintain the motor in relatively low temperature, but also the pressure drop in the water cooling system should be reduced to save energy by reducing the power consumption of the pump. In this paper, the numerical simulation of the cooling progress is completed and the temperature and pressure field distribution are obtained. The multi-objective optimization model is established which involves the cooling system structure, temperature field distribution and pressure field distribution. To improve the computational efficiency, the surrogate model of the simulation about the cooling process is established based on the Response Surface Methodology (RSM). After the multi-objective optimization, the Pareto optimal set is obtained. The proper design point, which could make the average temperature and pressure drop of the cooling system relative desirable, is chosen from the Pareto optimal set.

Multi-Objective Pricing Optimization for a High-Speed Rail Network Under Competition

2019 ◽  
Vol 2673 (7) ◽  
pp. 215-226 ◽  
Author(s):  
Huizhuo Cao ◽  
Xuemei Li ◽  
Vikrant Vaze ◽  
Xueyan Li
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Pareto Frontier ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
High Speed Rail ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Single Objective

Multi-objective pricing of high-speed rail (HSR) passenger fares becomes a challenge when the HSR operator needs to deal with multiple conflicting objectives. Although many studies have tackled the challenge of calculating the optimal fares over railway networks, none of them focused on characterizing the trade-offs between multiple objectives under multi-modal competition. We formulate the multi-objective HSR fare optimization problem over a linear network by introducing the epsilon-constraint method within a bi-level programming model and develop an iterative algorithm to solve this model. This is the first HSR pricing study to use an epsilon-constraint methodology. We obtain two single-objective solutions and four multi-objective solutions and compare them on a variety of metrics. We also derive the Pareto frontier between the objectives of profit and passenger welfare to enable the operator to choose the best trade-off. Our results based on computational experiments with Beijing–Shanghai regional network provide several new insights. First, we find that small changes in fares can lead to a significant improvement in passenger welfare with no reduction in profitability under multi-objective optimization. Second, multi-objective optimization solutions show considerable improvements over the single-objective optimization solutions. Third, Pareto frontier enables decision-makers to make more informed decisions about choosing the best trade-offs. Overall, the explicit modeling of multiple objectives leads to better pricing solutions, which have the potential to guide pricing decisions for the HSR operators.

scholarly journals Multi-Objective Optimization of Impedance Parameters in a Prosthesis Test Robot

2015 ◽  
Author(s):  
Poya Khalaf ◽  
Hanz Richter ◽  
Antonie J. van den Bogert ◽  
Dan Simon
Keyword(s):  
Control System ◽  
Motion Tracking ◽  
Optimization Problem ◽  
Peak Load ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Rms Error ◽  
Optimal Set

We design a control system for a prosthesis test robot that was previously developed for transfemoral prosthesis design and test. The robot’s control system aims to mimic human walking in the sagittal plane. It has been seen in previous work that trajectory control alone fails to produce human-like forces. Therefore, we utilize an impedance controller to achieve reasonable tracking of motion and force simultaneously. However, these objectives conflict. Impedance control design can therefore be viewed as a multi-objective optimization problem. We use an evolutionary multi-objective strategy called Multi-Objective Invasive Weed Optimization (MOIWO) to design the impedance controller. The multi-objective optimization problem admits a set of equally valid alternative solutions known as the Pareto optimal set. We use a pseudo weight vector approach to select a single solution from the Pareto optimal set. Simulation results show that a solution that is selected for pure motion tracking performs very accurate motion tracking (RMS error of 0.06 cm) but fails to produce the desired forces (RMS error of 70% peak load). On the other hand, a solution that is selected for pure force tracking successfully tracks the desired force (RMS error of 12.7% peak load) at the expense of motion trajectory errors (RMS error of 4.5 cm).

scholarly journals Optimization using evolutionary metaheuristic techniques: a brief review

2018 ◽  
Vol 15 (1) ◽  
pp. 44-53 ◽  
Author(s):  
Sajja Radhika ◽  
Aparna Chaparala
Keyword(s):  
Optimization Problems ◽  
Real Life ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Life Problems ◽  
Evolutionary Metaheuristic ◽  
Teaching Learning Based Optimization ◽  
Metaheuristic Techniques ◽  
Teaching Learning

Optimization is necessary for finding appropriate solutions to a range of real life problems. Evolutionary-approach-based meta-heuristics have gained prominence in recent years for solving Multi Objective Optimization Problems (MOOP). Multi Objective Evolutionary Approaches (MOEA) has substantial success across a variety of real-world engineering applications. The present paper attempts to provide a general overview of a few selected algorithms, including genetic algorithms, ant colony optimization, particle swarm optimization, and simulated annealing techniques. Additionally, the review is extended to present differential evolution and teaching-learning-based optimization. Few applications of the said algorithms are also presented. This review intends to serve as a reference for further work in this domain.

scholarly journals Comparative Study of Knee-Based Algorithms for Many-Objective Optimization Problems

2018 ◽  
Vol 12 (1) ◽  
pp. 7-16
Author(s):  
Saad M. Alzahrani ◽  
Naruemon Wattanapongsakorn
Keyword(s):  
Optimization Problems ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Trade Off ◽  
Knee Region ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Small Set ◽  
Optimum Solution

Nowadays, most real-world optimization problems consist of many and often conflicting objectives to be optimized simultaneously. Although, many current Multi-Objective optimization algorithms can efficiently solve problems with 3 or less objectives, their performance deteriorates proportionally with the increasing of the objectives number. Furthermore, in many situations the decision maker (DM) is not interested in all trade-off solutions obtained but rather interested in a single optimum solution or a small set of those trade-offs. Therefore, determining an optimum solution or a small set of trade-off solutions is a difficult task. However, an interesting method for finding such solutions is identifying solutions in the Knee region. Solutions in the Knee region can be considered the best obtained solution in the obtained trade-off set especially if there is no preference or equally important objectives. In this paper, a pruning strategy was used to find solutions in the Knee region of Pareto optimal fronts for some benchmark problems obtained by NSGA-II, MOEA/D-DE and a promising new Multi-Objective optimization algorithm NSGA-III. Lastly, those knee solutions found were compared and evaluated using a generational distance performance metric, computation time and a statistical one-way ANOVA test.

A Multi-Objective Optimization Approach on Spiral Grooves for Gas Mechanical Seals

2018 ◽  
Vol 140 (4) ◽  
Author(s):  
Xiuying Wang ◽  
Liping Shi ◽  
Wei Huang ◽  
Xiaolei Wang
Keyword(s):  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Mechanical Seals ◽  
Independent Variables ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Conflicting Objectives ◽  
Collinearity Diagnostics ◽  
Film Stiffness ◽  
Spiral Grooves

Spiral groove is one of the most common types of structures on gas mechanical seals. Numerical research demonstrated that the grooves designed for improving gas film lift or film stiffness often lead to the leakage increase. Hence, a multi-objective optimization approach specially for conflicting objectives is utilized to optimize the spiral grooves for a specific sample in this study. First, the objectives and independent variables in multi-objective optimization are determined by single objective analysis. Then, a set of optimal parameters, i.e., Pareto-optimal set, is obtained. Each solution in this set can get the highest dimensionless gas film lift under a specific requirement of the dimensionless leakage rate. Finally, the collinearity diagnostics is performed to evaluate the importance of different independent variables in the optimization.

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