Projection-based objective space reduction for many-objective optimization problems: Application to an induction motor design

2016 ◽  
Author(s):  
Armin Salimi ◽  
David A. Lowther
Keyword(s):  
Induction Motor ◽  
Optimization Problems ◽  
Space Reduction ◽  
Objective Space ◽  
Motor Design

A non-differential method for solving many-objective optimization problems: An application in IPM motor design

2020 ◽  
pp. 1-12
Author(s):  
Paolo Di Barba ◽  
Maria Evelina Mognaschi ◽  
Slawomir Wiak
Keyword(s):  
Optimization Problems ◽  
Differential Method ◽  
High Dimensional ◽  
Shape Design ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Standard Methods ◽  
Objective Space ◽  
Motor Design

Many-objective problems, characterized by a high-dimensional objective space, are a source of challenge for standard methods of multi-objective optimization. An evolutionary algorithm based on the concept of least-conflict solutions is here presented; the shape design of a IPM motor is considered as the case study. In particular, optimization problems dealing with 3 and 4 objective functions have been solved, respectively.

scholarly journals Two-stage multi-tasking transform framework for large-scale many-objective optimization problems

2021 ◽  
Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma
Keyword(s):  
Large Scale ◽  
Original Problem ◽  
Optimization Problems ◽  
Reference Points ◽  
Optimization Strategy ◽  
Two Stage ◽  
Decision Space ◽  
Second Stage ◽  
Decision Variables ◽  
Objective Space

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.

scholarly journals Windings Design for Single-phase Induction Motors Base on 4-phase Induction Motor (Case study: identical windings design)

2018 ◽  
Vol 215 ◽  
pp. 01023 ◽  
Author(s):  
Zuriman Anthony ◽  
Erhaneli Erhaneli ◽  
Zulkarnaini Zulkarnaini
Keyword(s):  
Induction Motor ◽  
Power Factor ◽  
Single Phase ◽  
Induction Motors ◽  
Three Phase ◽  
Motor Design

A 1-phase induction motor usually has a complicated windings design which compares to polyphase induction motor. In addition, a large capacitor start is required to operate the motor. It is an expensive way to operate the motor if it compare to polyphase induction motor. So, a new innovation method is required to make the motor more simple and cheaper. This research is purposed to study a new winding design for a single-phase capacitor motor. Winding design of the motor was conducted to a simple winding design like a 4-phase induction motor that has four identical windings. The comparator motor that use in this study was a Three-phase induction motor with data 1400 RPM, 1.5 HP, 50Hz, 380/220V, Y/Δ, 2.74/4.7A, 4 poles, that had the same current rating which the proposed method. The result showed that the motor design on this proposed method could be operated at 88.18 % power rating with power factor close to unity.

scholarly journals A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Peng Xu ◽  
Xiaoming Wu ◽  
Man Guo ◽  
Shuai Wang ◽  
Qingya Li ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Ideal Point ◽  
Dynamic Environment ◽  
Decision Space ◽  
Center Point ◽  
Multiobjective Optimization Problems ◽  
Objective Space ◽  
Adaptive Diversity ◽  
Dynamic Multiobjective Optimization

There are many issues to consider when integrating 5G networks and the Internet of things to build a future smart city, such as how to schedule resources and how to reduce costs. This has a lot to do with dynamic multiobjective optimization. In order to deal with this kind of problem, it is necessary to design a good processing strategy. Evolutionary algorithm can handle this problem well. The prediction in the dynamic environment has been the very challenging work. In the previous literature, the location and distribution of PF or PS are mostly predicted by the center point. The center point generally refers to the center point of the population in the decision space. However, the center point of the decision space cannot meet the needs of various problems. In fact, there are many points with special meanings in objective space, such as ideal point and CTI. In this paper, a hybrid prediction strategy carried through from both decision space and objective space (DOPS) is proposed to handle all kinds of optimization problems. The prediction in decision space is based on the center point. And the prediction in objective space is based on CTI. In addition, for handling the problems with periodic changes, a kind of memory method is added. Finally, to compensate for the inaccuracy of the prediction in particularly complex problems, a self-adaptive diversity maintenance method is adopted. The proposed strategy was compared with other four state-of-the-art strategies on 13 classic dynamic multiobjective optimization problems (DMOPs). The experimental results show that DOPS is effective in dynamic multiobjective optimization.

A Culture-Based Particle Swarm Optimization Framework for Dynamic, Constrained Multi-Objective Optimization

2012 ◽  
Vol 3 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Ashwin A. Kadkol ◽  
Gary G. Yen
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Cultural Algorithms ◽  
Swarm Optimization ◽  
Objective Space ◽  
Computationally Intensive ◽  
Available Information

Real-world optimization problems are often dynamic, multiple objective in nature with various constraints and uncertainties. This work proposes solving such problems by systematic segmentation via heuristic information accumulated through Cultural Algorithms. The problem is tackled by maintaining 1) feasible and infeasible best solutions and their fitness and constraint violations in the Situational Space, 2) objective space bounds for the search in the Normative Space, 3) objective space crowding information in the Topographic Space, and 4) function sensitivity and relocation offsets (to reuse available information on optima upon change of environments) in the Historical Space of a cultural framework. The information is used to vary the flight parameters of the Particle Swarm Optimization, to generate newer individuals and to better track dynamic and multiple optima with constraints. The proposed algorithm is validated on three numerical optimization problems. As a practical application case study that is computationally intensive and complex, parameter tuning of a PID (Proportional–Integral–Derivative) controller for plants with transfer functions that vary with time and imposed with robust optimization criteria has been used to demonstrate the effectiveness and efficiency of the proposed design.

Optimization of Three-Phase Induction Motor Design. Part II: The Efficiency and Cost of an Optimal Design

1987 ◽  
Vol PER-7 (9) ◽  
pp. 42-43
Author(s):  
J. Appelbaum ◽  
I. A. Khan ◽  
E. F. Fuchs ◽  
J. C. White
Keyword(s):  
Optimal Design ◽  
Induction Motor ◽  
Three Phase ◽  
Motor Design
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