scholarly journals Search Acceleration of Evolutionary Multi-Objective Optimization Using an Estimated Convergence Point

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 129 ◽  
Author(s):  
Yan Pei ◽  
Jun Yu ◽  
Hideyuki Takagi
Keyword(s):  
Parameter Space ◽  
Statistical Tests ◽  
Distribution Characteristic ◽  
Multi Objective Optimization ◽  
Pareto Improvement ◽  
Pareto Solutions ◽  
Pareto Solution ◽  
Multi Objective ◽  
Objective Space ◽  
Convergence Point

We propose a method to accelerate evolutionary multi-objective optimization (EMO) search using an estimated convergence point. Pareto improvement from the last generation to the current generation supports information of promising Pareto solution areas in both an objective space and a parameter space. We use this information to construct a set of moving vectors and estimate a non-dominated Pareto point from these moving vectors. In this work, we attempt to use different methods for constructing moving vectors, and use the convergence point estimated by using the moving vectors to accelerate EMO search. From our evaluation results, we found that the landscape of Pareto improvement has a uni-modal distribution characteristic in an objective space, and has a multi-modal distribution characteristic in a parameter space. Our proposed method can enhance EMO search when the landscape of Pareto improvement has a uni-modal distribution characteristic in a parameter space, and by chance also does that when landscape of Pareto improvement has a multi-modal distribution characteristic in a parameter space. The proposed methods can not only obtain more Pareto solutions compared with the conventional non-dominant sorting genetic algorithm (NSGA)-II algorithm, but can also increase the diversity of Pareto solutions. This indicates that our proposed method can enhance the search capability of EMO in both Pareto dominance and solution diversity. We also found that the method of constructing moving vectors is a primary issue for the success of our proposed method. We analyze and discuss this method with several evaluation metrics and statistical tests. The proposed method has potential to enhance EMO embedding deterministic learning methods in stochastic optimization algorithms.

Application of Multi-Objective Optimization Algorithm Based on Physical Programming in Ship Conceptual Design

2014 ◽  
Vol 904 ◽  
pp. 408-413
Author(s):  
Zhai Liu Hao ◽  
Zu Yuan Liu ◽  
Bai Wei Feng
Keyword(s):  
Conceptual Design ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Uniform Design ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Physical Programming ◽  
Multi Objective ◽  
Objective Space ◽  
Preference Structures

Ship optimization design is a typical multi-objective problem. The multi-objective optimization algorithm based on physical programming is able to obtain evenly distributed Pareto front. But the number of Pareto solutions and the search positions of pseudo-preference structures still exit some disadvantages that are improved in this paper. Firstly uniform design for mixture experiments is used to arbitrarily set the number of Pareto solutions and evenly distribute the search positions of pseudo-preference structures. Then the objective space is searched by shrinking of search domain and rotation of pseudo-preference structure technology. The optimization quality is able to be improved. Finally, the improved multi-objective optimization algorithm is applied to ship conceptual design optimization and compared with the multi-objective evolutionary algorithm to verify the effectiveness of the improved algorithm.

Research on Comprehensive Optimizing for Special Vehicle Handling and Riding Based on Multi-Objective Optimization Theory

2013 ◽  
Vol 467 ◽  
pp. 549-557
Author(s):  
Jun Li ◽  
Shi Hua Yuan ◽  
Ke Chen
Keyword(s):  
Solution Method ◽  
Optimization Theory ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Pareto Solution ◽  
Vehicle Handling ◽  
Multi Objective ◽  
Optimization Parameters ◽  
Special Vehicle ◽  
Vehicle Dynamic Model

According to the comprehensive optimizing problem, a vehicle dynamic model based on whole vehicle parameters was established in this paper. Seven optimization targets which are typical means for the special vehicles handling and riding and the twelve optimization parameters of the suspension system were put forward through analysis. And the multi-objective optimization solving was completed by using the multi-objective optimization tool. The pareto solutions for comprehensive optimization were obtained. Through setting standard of the maximum scores of understeer degree and the minimum value of driver seat departments RMS of full domain weighted acceleration, the Pareto solution was obtained. It can provide a solution method for balance and coordination chassis performance of the special vehicle.

scholarly journals On the use of filters to facilitate the post-optimal analysis of the Pareto solutions in multi-objective optimization

2015 ◽  
Vol 74 ◽  
pp. 48-58 ◽  
Author(s):  
E. Antipova ◽  
C. Pozo ◽  
G. Guillén-Gosálbez ◽  
D. Boer ◽  
L.F. Cabeza ◽  
...  
Keyword(s):  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Multi Objective ◽  
Optimal Analysis

Efficient Computation of Probabilistic Dominance in Multi-objective Optimization

2021 ◽  
Vol 1 (4) ◽  
pp. 1-26
Author(s):  
Faramarz Khosravi ◽  
Alexander Rass ◽  
Jürgen Teich
Keyword(s):  
Optimization Problems ◽  
Statistical Tests ◽  
Optimization Techniques ◽  
Simultaneous Optimization ◽  
Sampled Data ◽  
Efficient Computation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Comparison Operator

Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions. To cope with such uncertainties, stochastic and robust optimization techniques are widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions, sampled data, or uncertainty sets. In this scope, this article first introduces a novel empirical approach for the comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions. The comparison is performed through accurate and efficient calculations of the probability that one solution dominates the other in terms of each uncertain objective. Second, such an operator can be flexibly used and combined with many existing multi-objective optimization frameworks and techniques by just substituting their standard comparison operator, thus easily enabling the Pareto front optimization of problems with multiple uncertain objectives. Third, a new benchmark for evaluating uncertainty-aware optimization techniques is introduced by incorporating different types of uncertainties into a well-known benchmark for multi-objective optimization problems. Fourth, the new comparison operator and benchmark suite are integrated into an existing multi-objective optimization framework that features a selection of multi-objective optimization problems and algorithms. Fifth, the efficiency in terms of performance and execution time of the proposed comparison operator is evaluated on the introduced uncertainty benchmark. Finally, statistical tests are applied giving evidence of the superiority of the new comparison operator in terms of \epsilon -dominance and attainment surfaces in comparison to previously proposed approaches.

scholarly journals Multi-Objective Bee Swarm Optimization Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 133
Author(s):  
Nien-Che Yang ◽  
Danish Mehmood
Keyword(s):  
Power Systems ◽  
Optimization Problems ◽  
Filter Design ◽  
Manhattan Distance ◽  
Multi Objective Optimization ◽  
Optimal Method ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Passive Power

Harmonic distortion in power systems is a significant problem, and it is thus necessary to mitigate critical harmonics. This study proposes an optimal method for designing passive power filters (PPFs) to suppress these harmonics. The design of a PPF involves multi-objective optimization. A multi-objective bee swarm optimization (MOBSO) with Pareto optimality is implemented, and an external archive is used to store the non-dominated solutions obtained. The minimum Manhattan distance strategy was used to select the most balanced solution in the Pareto solution set. A series of case studies are presented to demonstrate the efficiency and superiority of the proposed method. Therefore, the proposed method has a very promising future not only in filter design but also in solving other multi-objective optimization problems.

scholarly journals Walking with EMO

2011 ◽  
pp. 300-332
Author(s):  
Jason Teo ◽  
Lynnie D. Neri ◽  
Minh H. Nguyen ◽  
Hussein A. Abbass
Keyword(s):  
Legged Locomotion ◽  
Operational Performance ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Uncertain Environments ◽  
Multi Objective ◽  
Limb Dynamics

This chapter will demonstrate the various robotics applications that can be achieved using evolutionary multi-objective optimization (EMO) techniques. The main objective of this chapter is to demonstrate practical ways of generating simple legged locomotion for simulated robots with two, four and six legs using EMO. The operational performance as well as complexities of the resulting evolved Pareto solutions that act as controllers for these robots will then be analyzed. Additionally, the operational dynamics of these evolved Pareto controllers in noisy and uncertain environments, limb dynamics and effects of using a different underlying EMO algorithm will also be discussed.

scholarly journals Study on application of MSOT method for lightweight design of automobile body structure

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096504
Author(s):  
Li Jixiong ◽  
Wang Daoyong
Keyword(s):  
Weight Reduction ◽  
Lightweight Design ◽  
The Body ◽  
Body Structure ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Multi Objective ◽  
Automobile Body ◽  
Collision Safety ◽  
Side Collision

In this study, the integrated MSOT (M-Multi-dimensional factor autobody model, S-Screening autobody component, O-Optimization of plate thickness, T-Testing, and validation) integration method is adopted to optimize the automobile body structure design for weight reduction. First, a multi-dimensional factor body model is established, then components of the vehicle are screened for the most important targets related to weight reduction and performance, and a multi-objective optimization is performed. Virtual experiments were carried out to validate the analysis and the MSOT method were proposed for lightweight design of the automobile body structure. A multi-dimensional performance model that considers stiffness, modality, strength, frontal offset collision, and side collision of a domestic passenger car body structure. Components affecting the weight of the vehicle were identified. Sheet metal thickness was selected as the main optimization target and a multi-objective optimization was carried out. Finally, simulations were performed on the body structure. The comprehensive performance, in terms of fatigue strength, frontal offset collision safety, and side collision safety, was verified using the optimized Pareto solution set. The results show that the established MSOT method can be used to comprehensively explore the weight reduction of the body structure, shorten the development process, and reduce development costs.

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