A Symmetric Points Search and Variable Grouping Method for Large-scale Multi-objective Optimization

2020 ◽  
Author(s):  
Dandan Tang ◽  
Yuping Wang ◽  
Xiangjuan Wu ◽  
Yiu-ming Cheung
Keyword(s):  
Large Scale ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Grouping Method ◽  
Symmetric Points

Improved NSGAII with a New Distribution and its Application in Multi-Objective Reservoir Operation

2011 ◽  
Vol 90-93 ◽  
pp. 2734-2739
Author(s):  
Ruan Yun ◽  
Cui Song Yu
Keyword(s):  
Genetic Algorithms ◽  
Control Strategy ◽  
Reservoir Operation ◽  
Large Scale ◽  
Pareto Front ◽  
Optimization Problem ◽  
Shape Parameter ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
New Distribution

Non-dominated sorting genetic algorithms II (NSGAII) has been widely used for multi- objective optimizations. To overcome its premature shortcoming, an improved NSGAII with a new distribution was proposed in this paper. Comparative to NSGAII, improved NSGAII uses an elitist control strategy to protect its lateral diversity among current non-dominated fronts. To implement elitist control strategy, a new distribution (called dogmatic distribution) was proposed. For ordinary multi-objective optimization problem (MOP), an ordinary exploration ability of improved NSGAII should be maintained by using a larger shape parameter r; while for larger-scale complex MOP, a larger exploration ability of improved NSGAII should be maintained by using a less shape parameter r. The application of improved NSGAII in multi-objective operation of Wohu reservoir shows that improved NSGAII has advantages over NSGAII to get better Pareto front especially for large-scale complex multi-objective reservoir operation problems.

scholarly journals Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1287
Author(s):  
Xingsi Xue ◽  
Pei-Wei Tsai ◽  
Yucheng Zhuang
Keyword(s):  
Evolutionary Algorithm ◽  
Large Scale ◽  
Biomedical Ontology ◽  
Biomedical Data ◽  
Biomedical Ontologies ◽  
Ontology Alignment ◽  
Multi Objective Optimization ◽  
Matching Problem ◽  
Multi Objective ◽  
Matching Techniques

To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment’s quality, which calculate the alignment’s statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the biomedical ontology matching problem. By analyzing the essence of this problem, we point out that it is a large-scale Multi-modal Multi-objective Optimization Problem (MMOP) with sparse Pareto optimal solutions. Then, we propose a problem-specific aMMOEA to solve this problem, which uses the Guiding Matrix (GM) to adaptively guide the algorithm’s convergence and diversity in both objective and decision spaces. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s biomedical tracks to test aMMOEA’s performance, and comparisons with two state-of-the-art MOEA-based matching techniques and OAEI’s participants show that aMMOEA is able to effectively determine diverse solutions for decision makers.

A Lion’s Pride Inspired Algorithm for VLSI Floorplanning

2019 ◽  
Vol 29 (01) ◽  
pp. 2050003
Author(s):  
Lalin L. Laudis ◽  
N. Ramadass
Keyword(s):  
Design Process ◽  
Integrated Circuit ◽  
Large Scale ◽  
Optimization Problem ◽  
Standard Test ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Scale Integration ◽  
Vlsi Floorplanning

The complexity of any integrated circuit pushes the researchers to optimize the various parameters in the design process. Usually, the Nondeterministic Polynomial problems in the design process of Very Large Scale Integration (VLSI) are considered as a Single Objective Optimization Problem (SOOP). However, due to the increasing demand for the multi-criterion optimization, researchers delve up on Multi-Objective Optimization methodologies to solve a problem with multiple objectives. Moreover, it is evident from the literature that biologically inspired algorithm works very well in optimizing a Multi-Objective Optimization Problem (MOOP). This paper proposes a new Lion’s pride inspired algorithm to solve any MOOP. The methodologies mimic the traits of a Lion which always strives to become the Pride Lion. The Algorithm was tested with VLSI floorplanning problem wherein the area and dead space are the objectives. The algorithm was also tested with several standard test problems. The tabulated results justify the ruggedness of the proposed algorithm in solving any MOOP.

Evolutionary Large-Scale Multi-Objective Optimization: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-34
Author(s):  
Ye Tian ◽  
Langchun Si ◽  
Xingyi Zhang ◽  
Ran Cheng ◽  
Cheng He ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Future Research ◽  
Decision Variable ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Space Reduction ◽  
Decision Variables ◽  
Comprehensive Survey

Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.

scholarly journals Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

2021 ◽  
Vol 13 (13) ◽  
pp. 2611
Author(s):  
Huaiyu Qin ◽  
Buhui Zhao ◽  
Leijun Xu ◽  
Xue Bai
Keyword(s):  
Time Delay ◽  
Petri Net ◽  
Information Transfer ◽  
Large Scale ◽  
Information Collection ◽  
Multi Objective Optimization ◽  
Wireless Power ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Uav

Power consumption in wireless sensor networks is high, and the lifetime of a battery has become a bottleneck, restricting network performance. Wireless power transfer with a ground mobile charger is vulnerable to interference from the terrain and other factors, and hence it is difficult to deploy in practice. Accordingly, a novel paradigm is adopted where a multi-UAV (unmanned aerial vehicle) with batteries can transfer power and information to SDs (sensor devices) in a large-scale sensor network. However, there are discrete events, continuous process, time delay, and decisions in such a complicated system. From the perspective of a hybrid system, a hybrid colored cyber Petri net system is proposed here to depict and analyze this problem. Furthermore, the energy utilization rate and information collection time delay are conflict with each other; therefore, UAV-aided wireless power and information transfer is formulated as a multi-objective optimization problem. For this reason, the MAC-NSGA II (multiple ant colony-nondominated sorting genetic algorithm II) is proposed in this work. Firstly, the optimal trajectory of multiple UAVs was obtained, and on this basis, the above two objectives were optimized simultaneously. Large-scale simulation results show that the proposed algorithm is superior to NSGA II and MOEA/D in terms of energy efficiency and information collection delay.

Sign in / Sign up

Export Citation Format

Share Document