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Optimal path planning for unmanned surface vehicle using new modified local search ant colony optimization

JOURNAL OF MARINE SCIENCE AND TECHNOLOGY 2022年第4期27卷 1207-1219页

作者： Vahid, Soroush Dideban, Abbas Semnan Univ Elect & Comp Engn Fac Semnan Iran

Optimal path planning is required for unmanned surface vehicle (USV) operation. This paper proposes two new path planning methods based on ant colony algorithm considering variables such as minimum energy consumption, lowest collision probability with fixed and moving obstacles and the least travelling time. These two methods are called ant colony optimization local search (ACOLS) Out Teta and ACOLS Curve Path. In the ACOLS Out Teta, path is optimized using a new pheromone updating method based on distance with obstacles. In the ACOLS Curve Path method, an innovative way is presented to eliminate curvatures of the path, which reduces the robot's maneuverability, decreasing the path length, energy consumption and travel time. In this paper, collision probability variable is used, providing the ability to present methods for providing an optimal path by paying a reasonable fee. Their performance is compared with each other, and with ACO and PSO.

关键词： Optimal path planning Unmanned surface vehicle (USV) ant colony optimization algorithm Local search

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Solve the Continuous optimization Models with ant colony optimization algorithms

Solve the Continuous Optimization Models with Ant Colony Opt...

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第一届中国智能计算大会

作者： Yuanguo Zhu Department of Applied Mathematics,Nanjing University of Science and Technology,Nanjing, Jiangsu 210094,China

ant colony optimization algorithm is a heuristic ap- proach for the solution of combinatorial optimization *** order to solve continuous optimization models,a new ant colony optimization algorithm is de- *** Benchmark... 详细信息

关键词： ant colony optimization algorithm Continuous optimization Model Benchmark Problem

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An ant colony optimization approach for the preference-based shortest path search

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2011年第2期34卷 181-196页

作者： Ok, Seung-Ho Seo, Woo-Jin Ahn, Jin-Ho Kang, Sungho Moon, Byungin Kyungpook Natl Univ Sch Elect Engn Taegu 702701 South Korea Kyungpook Natl Univ Sch Elect Engn & Comp Sci Taegu 702701 South Korea Hoseo Univ Dept Elect Engn Asan 336795 Chungnam South Korea Yonsei Univ Dept Elect & Elect Engn Seoul 120749 South Korea

This article proposes a modified ant colony system algorithm for finding the shortest path with preferred links. Most of the shortest path search algorithms focus on finding the distance or time shortest paths. However, these shortest paths may not necessarily be the optimum path for drivers who prefer choosing a less short, yet more reliable or flexible path. Accordingly, a preference-based shortest path search algorithm is proposed that uses the properties of the links in a map, as specified by a set of data provided by the user of the car navigation system. The proposed algorithm is implemented in C and experiments are performed using maps that include 64, 128, 192, and 256 nodes with 118, 242, 362, and 484 links, respectively. The simulation results with various parameter sets confirm that the proposed algorithm is effective for finding the preference-based shortest path.

关键词： shortest path search algorithm car navigation system ant colony optimization algorithm artificial intelligence

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Learning path planning methods based on learning path variability and ant colony optimization

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SYSTEMS AND SOFT COMPUTING 2024年 6卷

作者： Zhao, Jing Mao, Haitao Mao, Panpan Hao, Junyong Xinyang Normal Univ Coll Liberal Arts Xinyang 464000 Henan Peoples R China Xinyang Normal Univ Coll Asset Management Div Xinyang 464000 Henan Peoples R China Xinyang Normal Univ Sch Math & Stat Xinyang 464000 Henan Peoples R China Cent China Normal Univ Sch Educ Wuhan 430079 Hubei Peoples R China

With the advancement of education information, the scale of online education has been expanding, which brings challenges to students' learning path planning, i.e., course and learning method planning. To address the limitations of learning path planning such as insufficient personalization, the study proposes a learning path planning method based on learning path variability and ant colony optimization. First, dynamic time regularization is used to obtain learning path variability, and the K-means algorithm is used to classify students' learning types. Subsequently, an ant colony optimization algorithm is used to generate learning paths. Finally, the effectiveness of the method is tested. The results show that the loss value of the ant colony optimization algorithm converges to a minimum value of 0.1, which has the best stability of the loss function curve and the fastest convergence speed compared to other algorithms. Under the same experimental environment, the accuracy of the algorithm is as high as 0.9, which is conducive to the search for the optimal solution. The path planning method designed by the research can effectively grasp the learning characteristics and habits of students, and the accurate classification degree can reach 96.6%. With this learning path planning method, the average video learning time of students reaches a maximum of 80 min, while the average completion rate of students' course objectives is stable at 90%, which is about 20% higher than that of the GA-based learning path planning method. The method can significantly improve academic performance and educational outcomes. The method thus grasps the type of student learning, stimulates students' interest in learning, improves the effect of online learning, helps to promote education informatization and provides a boost to the deep reform of education.

关键词： Dynamic time regularization K -means algorithm ant colony optimization algorithm Learning paths Online education

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Nested optimization method combining complex method and ant colony optimization to solve JSSP with complex associated processes

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JOURNAL OF INTELLIGENT MANUFACTURING 2017年第8期28卷 1801-1815页

作者： Luo, Yabo Wuhan Univ Technol Sch Mech & Elect Engn Dept Ind Engn Wuhan 430070 Peoples R China

Job Shop Scheduling Problem (JSSP) is one of classic combinatorial optimization problems and has a long research history. Modern job shop has following characteristics: increasingly complicated processes, small batch and personalized requirement, which lead to complex correlations among processes. Complex correlations of processes, involving nested correlations besides serial and parallel correlations, propose a new task for JSSP research. Decomposing JSSP into two nested sub problems of order of arranging processes and machine arrangement, this research integrates the traditional thought of complex method into the ant colony optimization (ACO) to develop a nested optimization method in order to solve the new task. This paper is divided into four parts: first, the model of JSSP with complex associated processes is constructed and the difficulties to solve which are analyzed and listed;second, the definition of "order of arranging processes" is originally proposed, based on which the mathematical model available for the complex method is developed, taking process starting time as design variables of the first level optimization. The steps of the first level optimization and the secondary nested flow chart are detailed with the demonstration of the effectiveness of the complex method's iteration mechanism;third, based on the representation of features the order of arranging processes obtained by the first level optimization combined with the first-in first-out rule owns, the corresponding modified ACO algorithm, involving pheromone positive perception and reverse spreading mechanism, is put forward to realize the second level optimization, which result is taken as the objective function value of the complex vertex to realize the secondary nested optimization strategy;finally, taking plentiful JSSP with complex associated processes as study cases, a serial of comparative experiments are done respectively adopting the genetic algorithm, ACO algorithm, particle swarm opt

关键词： Job Shop Scheduling Problem Complex method ant colony optimization algorithm Heuristic algorithm

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An Improved ant colony optimization with Optimal Search Library for Solving the Traveling Salesman Problem

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2015年第7期12卷 1440-1444页

作者： Pang, Shanchen Ma, Tongmao Liu, Ting China Univ Petr Coll Comp & Commun Engn Qingdao 266580 Shandong Peoples R China Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao 266590 Shandong Peoples R China

ant colony optimization algorithm is a kind of heuristic algorithm, which has been widely applied to solve the problem such as TSP (traveling salesman problem). This paper uses the proximity to construct the optimal search library. Based on the idea, an improved ant colony optimization algorithm is put forward. We also define the new heuristic information and the improved pheromone update rules. Thus an improved ant colony optimization algorithm is put forward that provides a better solution to the combinatorial optimization of MTSP (multiple traveling salesman problem) problem with E-mail traffic constraints.

关键词： Optimal Search Library ant colony optimization algorithm Heuristic Information The Improved Pheromone Update MTSP Problem

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A Novel Multitasking ant colony optimization Method for Detecting Multiorder SNP Interactions

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2022年第4期14卷 814-832页

作者： Tuo, Shouheng Li, Chao Liu, Fan Zhu, YanLing Chen, TianRui Feng, ZengYu Liu, Haiyan Li, Aimin Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Shaanxi Key Lab Network Data Anal & Intelligent P Xian 710121 Shaanxi Peoples R China Xian Key Lab Big Data & Intelligent Comp Xian 710121 Shaanxi Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Shaanxi Peoples R China

Motivation Linear or nonlinear interactions of multiple single-nucleotide polymorphisms (SNPs) play an important role in understanding the genetic basis of complex human diseases. However, combinatorial analytics in high-dimensional space makes it extremely challenging to detect multiorder SNP interactions. Most classic approaches can only perform one task (for detecting k-order SNP interactions) in each run. Since prior knowledge of a complex disease is usually not available, it is difficult to determine the value of k for detecting k-order SNP interactions. Methods A novel multitasking ant colony optimization algorithm (named MTACO-DMSI) is proposed to detect multiorder SNP interactions, and it is divided into two stages: searching and testing. In the searching stage, multiple multiorder SNP interaction detection tasks (from 2nd-order to kth-order) are executed in parallel, and two subpopulations that separately adopt the Bayesian network-based K2-score and Jensen-Shannon divergence (JS-score) as evaluation criteria are generated for each task to improve the global search capability and the discrimination ability for various disease models. In the testing stage, the G test statistical test is adopted to further verify the authenticity of candidate solutions to reduce the error rate. Result Three multiorder simulated disease models with different interaction effects and three real age-related macular degeneration (AMD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) datasets were used to investigate the performance of the proposed MTACO-DMSI. The experimental results show that the MTACO-DMSI has a faster search speed and higher discriminatory power for diverse simulation disease models than traditional single-task algorithms. The results on real AMD data and RA and T1D datasets indicate that MTACO-DMSI has the ability to detect multiorder SNP interactions at a genome-wide scale. [GRAPHICS] .

关键词： Multitasking ant colony optimization algorithm Single-nucleotide polymorphisms SNP interaction

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Corporate governance, fraud learning cycles, and financial fraud detection: Evidence from Chinese listed firms

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RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE 2025年 76卷

作者： Li, Jing Shandong Management Univ Business Sch 3500 Dingxiang Rd Jinan 250357 Peoples R China

Corporate governance indicators play an important role in detecting financial fraud. Compared to the multilayer perceptron neural network (MLP NN) model, the extreme gradient boosting (XGBoost) model detects financial fraud more reliably, but suffers from a parameter search problem. An ant colony optimization algorithm can effectively optimize the model and increase its accuracy. Using data from 1660 Chinese listed firms between 2015 and 2021, adding corporate governance indicators considerably increased the XGBoost model's accuracy. Model optimization and empirical evidence show that fraud detection accuracy is higher in the early fraud learning cycle than in the most recent cycle. Moreover, the accuracy of detecting fraud is higher in the short fraud learning cycle than in the long cycle, while two years is the optimal fraud learning cycle. This study also analyzes the mechanisms through which corporate governance indicators affect financial fraud detection.

关键词： Financial fraud Corporate governance XGBoost model MLP NN model ant colony optimization algorithm Fraud learning cycle

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An ant colony optimization Approach for the Preference-Based Shortest Path Search

An Ant Colony Optimization Approach for the Preference-Based...

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International Conference on Future Generation Communication and Networking/International Conference on Advanced Communication and Networking (FGCN/ACN 2009)

作者： Ok, Seung-Ho Seo, Woo-Jin Ahn, Jin-Ho Kang, Sungho Moon, Byungin Kyungpook Natl Univ Sch Elect Eng & Comp Sci Daegu South Korea Hoseo Univ Dept Elect Engn Chungcheongnam South Korea Yonsei Univ Dept Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9783642108433

In this paper, a modified ant colony system (ACS) algorithm is proposed to find a shortest path based on the preference of links. Most of the shortest path search algorithms aim at finding the distance or time shortest paths. However, these shortest paths are not surely an optimum path for the drivers who prefer choosing a less short, but more reliable or flexible path. For this reason, we propose the preference-based shortest path search algorithm which uses the properties of the links of the map. The properties of the links are specified by a set of data provided by the user of the car navigation system. The proposed algorithm was implemented in C and experiments were performed upon the map that includes 64 nodes with 118 links.

关键词： heuristic shortest path algorithm car navigation system ant colony optimization algorithm artificial intelligence

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An Improved ant colony optimization for VRP with Time Windows

An Improved Ant Colony Optimization for VRP with Time Window...

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International Conference on Information Technology and Management Innovation (ICITMI2012)

作者： Yu, Su-Ping Li, Ya-Ping Luoyang Inst Sci & Technol Comp & Informat Engn Dept Luoyang Peoples R China

ISBN: (纸本)9783037855744

The Vehicle Routing Problem (VRP) is an important problem occurring in many distribution systems, which is also defined as a family of different versions such as the Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time Windows (VRPTW). The ant colony optimization (ACO) is a metaheuristic for combinatorial optimization problems. Given the ACO inadequacy, the vehicle routing optimization model is improved and the transfer of the algorithm in corresponding rules and the trajectory updated regulations is reset in this paper, which is called the Improved ant colony optimization (I-ACO). Compared to the calculated results with genetic algorithm (GA) and particle swarm optimization (PSO), the correctness of the model and algorithm is verified. Experimental results show that the I-ACO can quickly and effectively obtain the optimal solution of VRFTW.

关键词： Vehicle Routing Problem With Time Windows ant colony optimization algorithm Evenness of Solution ant Attracion

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