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Using genetic programming on GPS trajectories for travel mode detection

IET INTELLIGENT TRANSPORT SYSTEMS 2022年第1期16卷 99-113页

作者： Namdarpour, Farnoosh Mesbah, Mahmoud Gandomi, Amir H. Assemi, Behrang Amirkabir Univ Technol Dept Civil & Environm Engn Tehran *** Iran Univ Queensland Sch Civil Engn Brisbane Qld Australia Univ Technol Sydney Fac Engn & Informat Technol Ultimo Australia Queensland Univ Technol QUT Sch Built Environm Brisbane Qld Australia

The widespread and increased use of smartphones, equipped with the global positioning system (GPS), has facilitated the automation of travel data collection. Most studies on travel mode detection that used GPS data have employed hand-crafted features that may not have the capabilities to detect all complex travel behaviours since their performance is highly dependent on the skills of domain experts and may limit the performance of classifiers. In this study, a genetic programming (GP) approach is proposed to select and construct features for GPS trajectories. GP increased the macro-average of the F1-score from 77.3 to 80.0 in feature construction when applied to the GeoLife dataset. It could transform the decision tree into a competitive classifier with support vector machines (SVMs) and neural networks that are both able to extract high-level features. Simplicity, interpretability, and a relatively lower risk of overfitting allow the proposed model to be readily used for passive travel data collection even on smartphones with limited computational capacities. The model is validated by a second dataset from Australia and New Zealand, which indicated that a decision tree with the GP constructed features as its input has a considerably higher transferability than SVMs and neural networks.

关键词： global positioning system GPS trajectories decision trees statistical analysis decision tree travel industry neural networks feature construction complex travel behaviours passive travel data collection neural nets hand-crafted features genetic programming travel mode detection competitive classifier feature extraction evolutionary computation high-level feature extraction SVMs pattern classification F1-score macro-average Global Positioning System support vector machines GPS data smartphones genetic algorithms

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Decision Making in evolutionary Multiobjective Clustering: A Machine Learning Challenge

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IEEE ACCESS 2022年 10卷 117281-117303页

作者： Garza-Fabre, Mario Sanchez-Martinez, Aaron L. Aldana-Bobadilla, Edwin Landa, Ricardo CINVESTAV Ctr Res & Adv Studies Campus Tamaulipas Ciudad Victoria 87130 Tamaulipas Mexico Natl Council Sci & Technol Mexico CONACyT Mexico City 03940 DF Mexico

evolutionary multiobjective algorithms have become a popular choice to tackle the clustering problem. On the one hand, the simultaneous optimization of complementary clustering criteria offers an increased robustness to changes in data characteristics. On the other hand, the evolutionary search is able to approximate the Pareto optimal front and deliver a set of trade-offs between these criteria in a single algorithm execution. Decision making is the concluding stage of the pipeline, having as its goal the selection of a single, final solution from the set of candidate trade-offs produced. This is a complex task for which a definitive answer does not seem to be available, as the underlying assumptions of existing techniques may not hold for all applications. In this paper, we investigate an alternative approach to address this challenge: posing it as a learning problem. The key idea is to build a model that, given a proper characterization of solutions and their context (defined by the full approximation solution set and the specific clustering task at hand), is able to estimate quality and facilitate the identification of the best choice. To evaluate the suitability of this approach, we conduct a series of experiments over diverse synthetic and real-world datasets, including comparisons against a range of representative decision-making strategies from the literature. Our proposal exhibits greater flexibility in dealing with problems of varying characteristics, consistently outperforming the reference methods considered. This study demonstrates that it is possible to learn from the decision-making process in example settings and generalize the acquired knowledge to new scenarios.

关键词： Clustering algorithms multiobjective clustering decision making evolutionary computation machine learning pareto optimization

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Multi-Objective Material Generation Algorithm (MOMGA) for Optimization Purposes

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IEEE ACCESS 2022年 10卷 107095-107115页

作者： Nouhi, Behnaz Khodadadi, Nima Azizi, Mahdi Talatahari, Siamak Gandomi, Amir H. Univ Tabriz Dept Math Sci Tabriz 51666 Iran Florida Int Univ Coll Engn & Comp Dept Civil & Environm Engn Miami FL 33199 USA Univ Tabriz Dept Civil Engn Tabriz 51666 Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary RMIT Univ Sch Engn Melbourne Vic 3001 Australia

Optimization is a process of decision-making in which some iterative procedures are conducted to maximize or minimize a predefined objective function representing the overall behavior of a considered system problem. Most of the time, one specific function cannot represent the overall behavior of a system with particular levels of complexity, so the multiple objective functions should be determined for this purpose which requires an algorithm with adaptability to this situation. Multi-objective optimization is a process of decision making in which maximization or minimization of multiple objective functions is considered for reaching the acceptable levels of performance for the considered system problem. In this paper, the multi-objective version of the Material Generation Algorithm (MGA) is proposed as MOMGA, one of the recently developed metaheuristic algorithms for single-objective optimization. To evaluate the overall performance of the MOMGA, the benchmark multi-objective optimization problems of the Competitions on evolutionary computation (CEC) are considered alongside the real-world engineering problems. Based on the results, the MOMGA is capable of providing very acceptable results in dealing with multi-objective optimization problems.

关键词： Metaheuristics evolutionary computation Chemical reactions Chemical compounds Heuristic algorithms Algorithm design and analysis Machine learning algorithms Material generation algorithm multi-objective optimization real-world engineering problems competitions on evolutionary computation

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Discrete bacterial foraging optimization for community detection in networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 128卷 192-204页

作者： Yang, Bo Huang, Xuelin Cheng, Weizheng Huang, Tao Li, Xu Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

An essential mesoscopic concept in network analysis is that of community structure. However, conventional nature-inspired optimization algorithms encounter serious challenges and difficulties when used directly to seek communities in networks, due to the large amount of data and the NP hard combinatorial nature of the problem. Thus in this paper, we introduce a novel bacterial foraging optimization approach to uncovering community structure in networks. Instead of using the original bacterial foraging designed traditionally for continuous optimization, the problem of community detection is exquisitely embedded into a redefined discrete framework. The evolutionary principles for the bacterial foraging are developed from a topological perspective. Furthermore, two specific local updating rules, namely the greedy strategy and the stochastic strategy, are designed to steer the swarm of bacteria to the favored regions. The extensive experimental results on both synthetic and real-world networks indicate that the proposed approach outperforms the baseline algorithms and can achieve a high accuracy on the uncovered community structure. The integration of the proposed approach into the analysis of power grids and its explicit utility are also discussed in detail, showing that our method has high accuracy and practicability. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Networks Community detection Bacterial foraging optimization evolutionary computation

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A Survey of Decomposition Based evolutionary Algorithms for Many-Objective Optimization Problems

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IEEE ACCESS 2022年 10卷 72825-72838页

作者： Guo, Xiaofang Xian Technol Univ Sch Sci Xian 710021 Peoples R China

The framework of decomposition-based multi-objective evolutionary algorithms(MOEA/D) has evolved for more than ten years, and it has become irreplaceable tool for solving multi-objective optimization problems. In recent years, many scholars have investigated improved strategies from different directions. This paper gives a systematic comparison of six different components for decomposition-based algorithms, including framework analysis, weight vector generation scheme, aggregation evaluation function construction, reproduction operator, individual selection and update strategy, and the characteristics and application scope of various algorithms are also analyzed in detail in the survey. Different from previous survey on decomposition-based multi-objective evolutionary algorithms, a more detailed classification and experimental comparison are elaborated in the proposed paper.

关键词： Statistics Sociology evolutionary computation Convergence Shape Optimization Licenses Many-objective decomposition evolutionary algorithm

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Resetting Weight Vectors in MOEA/D for Multiobjective Optimization Problems With Discontinuous Pareto Front

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 9770-9783页

作者： Zhang, Chunjiang Gao, Liang Li, Xinyu Shen, Weiming Zhou, Jiajun Tan, Kay Chen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

When a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is applied to solve problems with discontinuous Pareto front (PF), a set of evenly distributed weight vectors may lead to many solutions assembling in boundaries of the discontinuous PF. To overcome this limitation, this article proposes a mechanism of resetting weight vectors (RWVs) for MOEA/D. When the RWV mechanism is triggered, a classic data clustering algorithm DBSCAN is used to categorize current solutions into several parts. A classic statistical method called principal component analysis (PCA) is used to determine the ideal number of solutions in each part of PF. Thereafter, PCA is used again for each part of PF separately and virtual targeted solutions are generated by linear interpolation methods. Then, the new weight vectors are reset according to the interrelationship between the optimal solutions and the weight vectors under the Tchebycheff decomposition framework. Finally, taking advantage of the current obtained solutions, the new solutions in the decision space are updated via a linear interpolation method. Numerical experiments show that the proposed MOEA/D-RWV can achieve good results for bi-objective and tri-objective optimization problems with discontinuous PF. In addition, the test on a recently proposed MaF benchmark suite demonstrates that MOEA/D-RWV also works for some problems with other complicated characteristics.

关键词： Optimization Principal component analysis evolutionary computation Clustering algorithms Heuristic algorithms Machine learning algorithms Machine learning DBSCAN multiobjective evolutionary algorithm based on decomposition (MOEA D) multiobjective evolutionary algorithm (MOEA) principal component analysis (PCA) weight vectors

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Scale-free network-based differential evolution to solve function optimization and parameter estimation of photovoltaic models

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SWARM AND evolutionary computation 2022年 74卷

作者： Yu, Yang Gao, Shangce Zhou, MengChu Wang, Yirui Lei, Zhenyu Zhang, Tengfei Wang, Jiahai Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Univ Toyama Fac Engn Toyama 9308555 Japan New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Ningbo Univ Fac Elect Engn & Comp Sci Zhejiang 315211 Peoples R China Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Peoples R China

Some recent research reveals that a topological structure in meta-heuristic algorithms can effectively enhance the interaction of population, and thus improve their performances. Inspired by it, we creatively investigate the effectiveness of using a scale-free network in differential evolution algorithm, and propose a scale-free network -based differential evolution method. The novelties of this paper include a scale-free network-based population structure and a new mutation operator designed to fully utilize the neighborhood information provided by a scale-free structure. The elite individuals and population at the latest generation are both employed to guide a global optimization process. In this manner, the proposed algorithm owns balanced exploration and exploitation capabilities to alleviate the drawbacks of premature convergence. Experimental and statistical analyses are performed on the CEC'17 benchmark function suite and the parameter estimation of photovoltaic models. Re-sults demonstrate its superior effectiveness and efficiency in comparison with its competitive peers.

关键词： Differential evolution Scale -free network Population structure evolutionary computation

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Fast Greedy Subset Selection From Large Candidate Solution Sets in evolutionary Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2022年第4期26卷 750-764页

作者： Chen, Weiyu Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Subset selection plays an important role in the field of evolutionary multiobjective optimization (EMO). Especially, in an EMO algorithm with an unbounded external archive (UEA), subset selection is an essential post-processing procedure to select a prespecified number of solutions as the final result. In this article, we discuss the efficiency of greedy subset selection for the hypervolume, inverted generational distance (IGD), and IGD plus (IGD+) indicators. Greedy algorithms usually efficiently handle the subset selection. However, when a large number of solutions are given (e.g., subset selection from tens of thousands of solutions in a UEA), they often become time consuming. Our idea is to use the submodular property, which is known for the hypervolume indicator, to improve their efficiency. First, we prove that the IGD and IGD+ indicators are also submodular. Next, based on the submodular property, we propose an efficient greedy inclusion algorithm for each indicator. We demonstrate through computational experiments that the proposed algorithms are much faster than the standard greedy subset selection algorithms. The proposed algorithms also help the research on performance indicators.

关键词： Optimization Greedy algorithms Statistics Sociology Approximation algorithms evolutionary computation Clustering algorithms evolutionary many-objective optimization evolutionary multiobjective optimization (EMO) performance indicators submodularity subset selection

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A survey, taxonomy and progress evaluation of three decades of swarm optimisation

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第5期55卷 3607-3725页

作者： Liu, Jing Anavatti, Sreenatha Garratt, Matthew Tan, Kay Chen Abbass, Hussein A. Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

While the concept of swarm intelligence was introduced in 1980s, the first swarm optimisation algorithm was introduced a decade later, in 1992. In this paper, nineteen representative original swarm optimisation algorithms are analysed to extract their common features and design a taxonomy for swarm optimisation. We use twenty-nine benchmark problems to compare the performance of these nineteen algorithms in the form they were first introduced in the literature against five state-of-the-art swarm algorithms. This comparison reveals the advancements made in this field over three decades. It reveals that, while the state-of-the-art swarm optimisation algorithms are indeed competitive in terms of the quality of solutions they find, their complexities have evolved to be more computationally demanding when compared to the nineteen original algorithms of swarm optimisation. The investigation suggests that there is an urge to continue to design swarm optimisation algorithms that are simpler, while maintaining their current competitive performance.

关键词： Swarm intelligence Optimisation algorithm Taxonomy evolutionary computation

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evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation

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IEEE ACCESS 2022年 10卷 84664-84679页

作者： Xiong, Minghui Xiong, Wei Liu, Zheng Space Engn Univ Sci & Technol Complex Elect Syst Simulat Lab Beijing 101416 Peoples R China

Most existing evolutionary approaches to satellite range scheduling seek optimal solution in terms of the request satisfaction. The scheduling demand of managing ground station resource is seldom considered, which restricts their real-world applications. To effectively generate a set of more rational satellite range schedules, this paper establishes the multi-objective satellite range scheduling mathematical model. Unlike existing approaches, we propose a general population generation approach to solve the problem without relying on any specific kind of evolutionary algorithm, so different types of evolutionary algorithms can be extended to satellite range scheduling without modifying the original framework and search strategy. The idea is to utilize the request satisfaction and resource utilization knowledge learnt from parent schedules to guide the generation and updating of new solutions. Furthermore, an iterative rewriting operator is designed to guide a biased faster convergence towards the low request failure region in objective space. The proposed approach has been applied to five different types of classical and state-of-the-art evolutionary algorithms and examined on benchmark problems. Experimental results illustrate the search efficiency enhancement and good adaptability to different evolutionary algorithms, which show the broad application prospect for satellite range scheduling.

关键词： Satellites Search problems Scheduling evolutionary computation Statistics Learning systems Satellite range scheduling multi-objective optimization evolutionary algorithm learning-guided

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