A Novel Hybrid Multi-objective Optimization Framework: Rotating the Objective Space

2014 ◽  
pp. 192-203
Author(s):  
Xin Qiu ◽  
Ye Huang ◽  
Jian-Xin Xu ◽  
Kay Chen Tan
Keyword(s):  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Objective Space

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.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

A Multi-variable & Multi-objective Optimization Framework for LID Layout in Sponge City

2021 ◽  
Author(s):  
Sijie Tang ◽  
Jiping Jiang ◽  
Yi Zheng
Keyword(s):  
Ecological Factors ◽  
Green Roofs ◽  
Design Tool ◽  
Distribution Area ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Sponge City ◽  
Multi Objective ◽  
Optimization Framework ◽  
City Construction

<p>Practitioners usually design the plan of Sponge City construction (SCC) by combining LID facilities (e.g., rain garden, rain barrels, green roofs, and grassed swales) according to their personal experiences or general guidelines. The layout (including selection, connection and distribution area) of LID facilities is subjective, in the risk of far from optimal combination. Previous researchers have developed some LID optimization tools, which only consider the dimension and number of LIDs in a given scenario. Therefore, it is necessary to develop a flexible and extensible design tool with the support of urban hydrological model to conduct the facilities layout optimization. This study introduced a SWMM-based multi-variable and multi-objective optimization framework called CAFID (Comprehensive Assessment and Fine Design Model of Sponge City) to meet this end. The assessment module with multi-objective couples diverse controlling end-points (e.g., total runoff, peak runoff, pollutant concentration, cost, and customized social-ecological factors) as the candidates of assessment criteria. The optimization module with multi-variable is implemented by SWMM, starting with three steps: 1) Full allocation. Based on the availability, list the candidates of LID facility for each sub-catchment; 2) Full connection. Order the potential stream direction of surface runoff from rainfall to municipal network, based on possible hierarchical structure of sub-catchments and LID facilities; 3) Full coverage. Identify all the suitable area for LID facility in sub-catchment. The optimization on the 3 variables, the selection, connection, and area, is powered by NSGA-II and TOPSIS algorithms, which make it possible that we choose a final result from the set of nondominated solutions according to special weight distribution. The effectiveness of CAFID was illustrated through a case of Sponge City in Fenghuangcheng of Shenzhen City, one of 30 national pilot sponge cities in China. As well, this new framework is expected to be widely verified and applied in Sponge City construction in China or similar concepts all over the world.</p>

Evolving a Multi-objective Optimization Framework

2021 ◽  
pp. 175-198
Author(s):  
Antonio J. Nebro ◽  
Javier Pérez-Abad ◽  
José F. Aldana-Martin ◽  
José García-Nieto
Keyword(s):  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework
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