scholarly journals Optimization Algorithm Design for the Taxi-Sharing Problem and Application

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yongjie Wang ◽  
Maolin Li
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Algorithm ◽  
Real World ◽  
Low Cost ◽  
Algorithm Design ◽  
Transportation Systems ◽  
The Other ◽  
Approximate Algorithm ◽  
Data Sets ◽  
Np Complete

With the development of mobility techniques, the transportation systems become smarter, pursuing higher goals, such as convenience for passengers and low cost. In this work, we investigate the taxi-sharing system, which is a promising system recently. The passengers can share the same taxis to different destinations to save cost. Considering the property of taxis’ routes, the corresponding model is established and our aim is to design the trip for each taxi to reduce the total number of taxi trips in the whole system if one taxi can be shared by several passengers. Compared with the previous work, we do not have any constraint about the taxi stations. The taxi trips have more flexibility in reality. We analyze this problem and prove it is NP-Complete. There are two proposed algorithms to solve this problem, one is a heuristic algorithm and the other is an approximate algorithm. In the experiment, two real-world taxi data sets are tested, and our algorithm shows the superiority of our taxi-sharing system. Using the taxi-sharing system, the number of trips can be reduced by about 30 % .

scholarly journals Introducing modified version of penguins search optimization algorithm (PeSOA) and its application in optimal operation of reservoir systems

2017 ◽  
Vol 18 (4) ◽  
pp. 1484-1496 ◽  
Author(s):  
Afshin Mansouri ◽  
Babak Aminnejad ◽  
Hassan Ahmadi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Real World ◽  
Global Optimum ◽  
Optimal Operation ◽  
The Other ◽  
Reservoir System ◽  
Search Optimization ◽  
Real World Problem ◽  
Reservoir Systems

Abstract In the current study, modified version of the penguins search optimization algorithm (PeSOA) was introduced, and its usage was assessed in the water resources field. In the modified version (MPeSOA), the Gaussian exploration was added to the algorithm. The MPeSOA performance was evaluated in optimal operation of a hypothetical four-reservoir system and Karun-4 reservoir as a real world problem. Also, genetic algorithm (GA) was used as a criterion for evaluating the performance of PeSOA and MPeSOA. The results revealed that in a four-reservoir system problem, the PeSOA performance was much weaker than the GA; but on the other hand, the MPeSOA had better performance than the GA. In the mentioned problem, PeSOA, GA, and MPeSOA reached 78.43, 97.46, and 98.30% of the global optimum, respectively. In the operation of Karun-4 reservoir, although PeSOA performance had less difference with the two other algorithms than four-reservoir problem, its performance was not acceptable. The average values of objective function in this case were equal to 26.49, 23.84, and 21.48 for PeSOA, GA, and MPeSOA, respectively. According to the results obtained in the operation of Karun-4 reservoir, the algorithms including MPeSOA, GA, and PeSOA were situated in ranks one to three in terms of efficiency, respectively.

scholarly journals Incremental 3-D pose graph optimization for SLAM algorithm without marginalization

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092530
Author(s):  
Feng Youyang ◽  
Wang Qing ◽  
Yang Gaochao
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Bayes Factor ◽  
Simulated Data ◽  
Schur Complement ◽  
Factor Graph ◽  
Data Sets ◽  
Loop Closure ◽  
Graph Optimization ◽  
Pose Graph Optimization

Pose graph optimization algorithm is a classic nonconvex problem which is widely used in simultaneous localization and mapping algorithm. First, we investigate previous contributions and evaluate their performances using KITTI, Technische Universität München (TUM), and New College data sets. In practical scenario, pose graph optimization starts optimizing when loop closure happens. An estimated robot pose meets more than one loop closures; Schur complement is the common method to obtain sequential pose graph results. We put forward a new algorithm without managing complex Bayes factor graph and obtain more accurate pose graph result than state-of-art algorithms. In the proposed method, we transform the problem of estimating absolute poses to the problem of estimating relative poses. We name this incremental pose graph optimization algorithm as G-pose graph optimization algorithm. Another advantage of G-pose graph optimization algorithm is robust to outliers. We add loop closure metric to deal with outlier data. Previous experiments of pose graph optimization algorithm use simulated data, which do not conform to real world, to evaluate performances. We use KITTI, TUM, and New College data sets, which are obtained by real sensor in this study. Experimental results demonstrate that our proposed incremental pose graph algorithm model is stable and accurate in real-world scenario.

A Low-Cost Real-World Planning Strategy for Deploying a Dedicated Short-Range Communications Roadside Unit on a Highway Off-Ramp

2018 ◽  
Vol 2672 (19) ◽  
pp. 124-134 ◽  
Author(s):  
Jonathan B. Walker ◽  
Kevin Heaslip
Keyword(s):  
Real World ◽  
Intelligent Transportation Systems ◽  
Short Range ◽  
Environmental Changes ◽  
Low Cost ◽  
Transportation Systems ◽  
Planning Strategy ◽  
Dedicated Short Range Communications ◽  
Roadside Unit ◽  
World Environment

The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment, such as a traffic light’s signal phase and timing, using vehicle-to-infrastructure communication. Several scholarly papers exist on planning strategies for DSRC RSU deployments using simulation without accounting for wireless communication constraints and environmental changes. This paper proposes an empirical-based planning strategy for a highway off-ramp in a real-world environment. The research goal focuses on developing a low-cost and structured deployment plan for DSRC RSUs with the following objectives: use free planning tools; apply the deployment strategy in a real-world environment; utilize publicly available DSRC RSU data measurements; and leverage existing intelligent transportation systems infrastructure when possible. The proposed planning strategy includes three steps: (1) conduct a virtual site survey, (2) gather baseline performance data for the DSRC RSU equipment, and (3) generate a predictive radio frequency signal. The planning strategy was successfully applied on a highway off-ramp at exit 19A of the Capital Beltway, which encircles Washington, DC.

scholarly journals Sampling for Approximate Maximum Search in Factorized Tensor

2017 ◽  
Author(s):  
Zhi Lu ◽  
Yang Hu ◽  
Bing Zeng
Keyword(s):  
Theoretical Analysis ◽  
Collaborative Filtering ◽  
Real World ◽  
State Of The Art ◽  
The Other ◽  
Data Sets ◽  
Real World Data ◽  
Parafac Model ◽  
The Matrix ◽  
Special Case

Factorization models have been extensively used for recovering the missing entries of a matrix or tensor. However, directly computing all of the entries using the learned factorization models is prohibitive when the size of the matrix/tensor is large. On the other hand, in many applications, such as collaborative filtering, we are only interested in a few entries that are the largest among them. In this work, we propose a sampling-based approach for finding the top entries of a tensor which is decomposed by the CANDECOMP/PARAFAC model. We develop an algorithm to sample the entries with probabilities proportional to their values. We further extend it to make the sampling proportional to the $k$-th power of the values, amplifying the focus on the top ones. We provide theoretical analysis of the sampling algorithm and evaluate its performance on several real-world data sets. Experimental results indicate that the proposed approach is orders of magnitude faster than exhaustive computing. When applied to the special case of searching in a matrix, it also requires fewer samples than the other state-of-the-art method.

scholarly journals Design of 2-Dof Pid Controller for Load Frequency Control of Two Area Power System using Mfo Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 158-161
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Frequency Control ◽  
Load Frequency Control ◽  
The Other ◽  
Load Frequency ◽  
Integral Controller ◽  
Moth Flame Optimization Algorithm ◽  
Tie Line

The paper endeavours to analyse the load frequency control for two area system. In this paper, two areas has been considered in which non-reheated type of turbine in both area are used and whose secondary loop consists a latest controller called 2 degree-of-freedom PID (2-DOF-PID) controller. The parameter of the this controller is been optimized by the latest meta heuristic algorithm also called Moth flame optimization algorithm (MFO) to minimize the deviation in frequency of area and tie-line power respectively. The same processes are repeated with PID controller and Integral controller whose parameters are also optimized by MFO. A comparison is made among the result of these and 2-DOF-PID controller prove its superiority over the other controller for minimizing the deviation which occurs in frequency of the area as well as the tie-line power.

Multivariant Optimization Algorithm for the 0-1 Knapsack Problem

2014 ◽  
Vol 556-562 ◽  
pp. 3514-3518
Author(s):  
Lan Juan Liu ◽  
Bao Lei Li ◽  
Qin Hu Zhang ◽  
Dan Jv Lv ◽  
Xin Ling Shi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Heuristic Algorithm ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Global Search ◽  
Particle Swarm Algorithm ◽  
Data Sets ◽  
Better Than

In this paper, a novel heuristic algorithm named Multivariant Optimization Algorithm (MOA) is presented to solve the 0-1 Knapsack Problem (KP). In MOA, multivariant search groups (locate and global search groups) execute the global exploration and local exploitation iteratively to locate the optimal solution automatically. The presented algorithm has been compared with Genetic Algorithm (GA) and Particle swarm algorithm (PSO) based on five data sets, results show that the optimization of MOA is better than GA and PSO when the dimension of problem is high.

Semi-Supervised Outlier Detection with Only Positive and Unlabeled Data Based on Fuzzy Clustering

2015 ◽  
Vol 24 (03) ◽  
pp. 1550003 ◽  
Author(s):  
Armin Daneshpazhouh ◽  
Ashkan Sami
Keyword(s):  
Intrusion Detection ◽  
Outlier Detection ◽  
Fuzzy Clustering ◽  
Real World ◽  
State Of The Art ◽  
Real Data ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Real World Applications

The task of semi-supervised outlier detection is to find the instances that are exceptional from other data, using some labeled examples. In many applications such as fraud detection and intrusion detection, this issue becomes more important. Most existing techniques are unsupervised. On the other hand, semi-supervised approaches use both negative and positive instances to detect outliers. However, in many real world applications, very few positive labeled examples are available. This paper proposes an innovative approach to address this problem. The proposed method works as follows. First, some reliable negative instances are extracted by a kNN-based algorithm. Afterwards, fuzzy clustering using both negative and positive examples is utilized to detect outliers. Experimental results on real data sets demonstrate that the proposed approach outperforms the previous unsupervised state-of-the-art methods in detecting outliers.

scholarly journals Evaluation of Topological Consistency in CityGML

2019 ◽  
Vol 8 (6) ◽  
pp. 278 ◽  
Author(s):  
Anna Giovanella ◽  
Patrick Erik Bradley ◽  
Sven Wursthorn
Keyword(s):  
Real World ◽  
Line Segment ◽  
Geometric Model ◽  
Database System ◽  
Boundary Representation ◽  
The Other ◽  
Data Sets ◽  
Real World Data ◽  
Incidence Graph ◽  
City Model

Boundary representation models are data models that represent the topology of a building or city model. This leads to an issue in combination with geometry, as the geometric model necessarily has an underlying topology. In order to allow topological queries to rely on the incidence graph only, a new notion of topological consistency is introduced that captures possible topological differences between the incidence graph and the topology coming from geometry. Intersection matrices then describe possible types of topological consistency and inconsistency. As an application, it is examined which matrices can occur as intersection matrices, and how matrices from topologically consistent data look. The analysis of CityGML data sets stored in a spatial database system then shows that many real-world data sets contain many topologically inconsistent pairs of polygons. It was observed that even if data satisfy the val3dity test, they can still be topologically inconsistent. On the other hand, it is shown that the ISO 19107 standard is equivalent to our notion of topological consistency. In the case when the intersection is a point, topological inconsistency occurs because a vertex lies on a line segment. However, the most frequent topological inconsistencies seem to arise when the intersection of two polygons is a line segment. Consequently, topological queries in present CityGML data cannot rely on the incidence graph only, but must always make costly geometric computations if correct results are to be expected.

scholarly journals Multiple Sequence Alignment by Differential Evolutionary Algorithm with New Mutant

2019 ◽  
Vol 8 (4) ◽  
pp. 9892-9897
Keyword(s):  
Evolutionary Algorithm ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
The Other ◽  
Bench Mark ◽  
Data Sets ◽  
Multiple Sequence ◽  
Differential Evolutionary Algorithm ◽  
Np Complete ◽  
Multiple Species

Multiple Sequence Alignment (MSA) is vital in Bioinformatics, helps in finding evolutionary relationships among multiple species. MSA is a NP-complete problem. Though there are a number of tools recent Meta-heuristics are found to be effective in solving MSA problem. Differential Evolutionary Algorithm (DE) is one of the optimization algorithms with various mutants. This work proposes a new mutant for DE, defined using local best and worst chromosomes with current generation population. The performance of the new mutant is evaluated using 50 well known bench mark data sets in sabre (SABMARK v1.65). The results are matched with all the other DE mutants, Genetic Algorithm (GA) and recent Teacher Learner Based Optimization algorithm (TLBO). The proposed DE mutant outperformed all the other DE mutants, GA and TLBO in solving MSA problem.

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