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Applying genetic algorithm and ant colony optimization algorithm into marine investigation path planning model

SOFT COMPUTING 2020年第11期24卷 8199-8210页

作者： Liang, Ye Wang, Lindong Shaanxi Normal Univ Sch Geog & Tourism Xian 710000 Shaanxi Peoples R China Beijing Normal Univ Fac Geog SciChina State Key Lab Earth Surface Pr Engn Ctr Desertificat & Blownsand ControlMinist Key Lab Environm Change & Nat DisasterMinist Edu Beijing 100000 Peoples R China

Marine resources are vital to the development of a country. Marine investigation can obtain more marine resources and acquire more marine environmental information. A common method used in the marine investigation consumes a large amount of both time and money. Thus, the scientific path planning is important for improving the efficiency and reducing the costs of the marine investigation. Currently, the most commonly used algorithms for path planning are the genetic algorithm (GA) and the ant colony optimization algorithm (ACOA). Through continuous iterations, the initial solutions obtained by GA and ACOA gradually approach the optimal solutions. However, the final solutions of both algorithms are often suboptimal solutions or local optimal solutions. In particular, in terms of the marine investigation path planning that involves enormous stations, both GA and ACOA are prone to premature and local optimal solutions, leading to the stagnation of the searching. Therefore, in order to solve these problems and save the costs of marine investigation, the ACOA and GA are combined to propose a hybrid algorithm for the further improvement in the quality of the solutions. Through the experiments and software implementation, the proposed hybrid algorithm is proved of high effectiveness and robustness, which could obtain the optimal path for single or multiple research vessels, thereby saving the time and costs of marine investigation path planning.

关键词： Genetic algorithm Marine path planning ant colony optimization algorithm Hybrid algorithm

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Structural topology optimization using ant colony optimization algorithm

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APPLIED SOFT COMPUTING 2009年第4期9卷 1343-1353页

作者： Luh, Guan-Chun Lin, Chun-Yi Tatung Univ Dept Mech Engn Taipei 104 Taiwan

The ant colony optimization (ACO) algorithm, a relatively recent bio-inspired approach to solve combinatorial optimization problems mimicking the behavior of real ant colonies, is applied to problems of continuum structural topology design. An overview of the ACO algorithm is first described. A discretized topology design representation and the method for mapping ant's trail into this representation are then detailed. Subsequently, a modified ACO algorithm with elitist ants, niche strategy and memory of multiple colonies is illustrated. Several well-studied examples from structural topology optimization problems of minimum weight and minimum compliance are used to demonstrate its efficiency and versatility. The results indicate the effectiveness of the proposed algorithm and its ability to find families of multi-modal optimal design. (C) 2009 Elsevier B. V. All rights reserved.

关键词： ant colony optimization algorithm Continuum structural topology optimization Elitist ants Niche strategy Multiple colonies

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Development of Intelligent Learning Model Based on ant colony optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第10期15卷 317-327页

作者： Guo, Xiaojing Zhu, Xiaoying Liu, Lei Yantai Nanshan Univ Coll Technol & Data Yantai 265700 Peoples R China

In the process of the gradual popularization of online courses, learners are increasingly dissatisfied with the recommendation mechanism of imprecise courses when faced with a large number of course choices. How to better recommend relevant courses to targeted users has become a current research hotspot. An intelligent learning model based on ant colony optimization algorithm is introduced, which can accurately calculate the similarity between courses and learners. After structured classification, the model recommends courses to learners in the optimal way. The results showed that the accuracy of this method reached 10-20 when tested in Sphere and Ellipse functions, and the optimal solution for problem Ulysses21 was 27, which was better than Advanced Sorting ant System (ASrank), Maximum Minimum ant System (MMAS), and ant System (AS) based on optimization sorting. The proposed ant colony optimization algorithm had better convergence performance than ASrank, MMAS, and AS algorithms, with a shortest path of 53.5. After reaching Root Mean Square Error (RMSE) and Relative Deviation (RD) distributions of 6% and 8%, the stability of the proposed method no longer decreased with increasing RMSE. The accuracy did not vary significantly with changes in the dataset, and the reproducibility performance was better than other comparison models. In the scenarios of path Block and path Naive, the proposed algorithm had an average computation time of only 1011, which was better than the ant colony optimization (ACO) and Massive Multilingual Speech (MMS) models. Therefore, the proposed algorithm improves the performance of intelligent learning models, solves the problem of local optima while enhancing the convergence efficiency of the model, and provides new solutions and directions for increasing the recommendation performance of online learning platforms.

关键词： Online courses ant colony optimization algorithm intelligent learning model path planning local optimum

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ant colony optimization algorithm Applied to Ship Steering Control

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Procedia Computer Science 2014年 35卷 83-92页

作者： Mirosław Tomera Department of Ship Automation Gdynia Maritime University 81-87 Morska St. 81-225 Gdynia Poland

The article describes the application of an ant algorithm to optimize parameters of the ship course controller, based on the algorithm of PID control. The ant algorithm is a method of combinatorial optimization, which utilizes the pattern of ants search for the shortest path from the nest to the place where the food is located. The procedure of parameter tuning for the ship course controller was applied to the case when the controller was changing the course of the ship and the integral action was turned off. Tuned parameters of the ship course controller are evaluated by the ant colony algorithm, which makes use of the course error based objective function and a given rudder deflection. The results were compared with equivalent results obtained using a genetic algorithm. Moreover, the effectiveness of PID controller parameter tuning was assessed using the ant colony optimization algorithm.

关键词： ant colony optimization algorithm genetic algorithm artificial intelligence PID controller ship control.

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A Novel Data Aggregation Method for Underwater Wireless Sensor Networks using ant colony optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第4期14卷 83-93页

作者： Zhang, Lianchao Qi, Jianwei Wu, Hao Tangshan Maritime Inst Tangshan Hebei Tangshan 063000 Hebei Peoples R China ShiJiaZhuang Univ Appl Technol Shijiazhuang 050081 Hebei Peoples R China

Underwater Wireless Sensor Networks (UWSNs) have a wide range of applications for monitoring the ocean and exploring the offshore environment. Sensor nodes are typically dispersed throughout the area of interest at different depths in these networks. Sensor nodes on the seabed must use a routing protocol in order to communicate with surface-level nodes. The suitability assessment considers network resources, application requirements, and environmental factors. By combining these factors, a platform for resource-aware routing strategies can be created that meet the needs of different applications in dynamic environments. Numerous challenges and problems are associated with UWSNs, including the lack of battery power, instability of topologies, a limited bandwidth, long propagation times, and interference from the ocean. These problems can be addressed through the design of routing protocols. The routing protocol facilitates the transfer of data between source and destination nodes. Data aggregation and UWSN protocols are widely used to achieve better outcomes. This paper describes an energy-aware algorithm for data aggregation in UWSNs that uses the improved ACO (ant colony optimization) algorithm to maximize the packet delivery ratio, improve the network lifetime, decrease end-to-end delay, and use less energy.

关键词： -UWSNs routing data aggregation energy efficiency ant colony optimization algorithm

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Resource utilization enhancemnet through live virtual machine migration in cloud using ant colony optimization algorithm

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INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2020年第1期23卷 79-85页

作者： Sutar, Sandeep G. Mali, Pallavi J. More, Amruta Y. Annasaheb Dange Coll Engn & Technol Dept Comp Sci & Engn Ashta India OOPRA IT Solut PVT Ltd Pune Maharashtra India

Cloud computing offers unlimited computational resources which are ready to use from anywhere, anytime on request. The achievement of maximized utilization of computational resources (physical and virtual) and minimized energy consumption of resources are goals of proposed system. The proposed system provides dynamic and energy efficient live VM (virtual machine) migration approach. This system reduces wastage of power by initiating sleep mode of idle physical machines results into energy saving. We propose a system consist with seven modules. (1) Resource monitor analyses energy consumption of resources. (2) Capacity distributor distributes maximum and minimum capacity for the physical machines. (3) Task allocator determines overloaded servers. (4) Optimizer analyses load on physical machine using ant colony optimization algorithm (5) Local Migration Agent calculates load of VMs to be migrated and select appropriate physical server. (6) Migration Orchestrator migrates the VM cosidering load. (7) Energy Manager initiates sleep mode for idle physical machine(PM)

关键词： Cloud computing Energy management Virtualization Live virtual machine migration ant colony optimization algorithm

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Adaptive Fuzzy Control Applied to Seven-Link Biped Robot Using ant colony optimization algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2019年第4期43卷 797-811页

作者： Aldair, Ammar A. Rashid, Abdulmuttalib T. Rashid, Mofeed T. Alsaedee, Eman Badee Univ Basrah Elect Engn Dept Basrah Iraq Univ Dhi Qar Dept Elect & Elect Engn Nasiriyah Iraq

Referring to the fact that the nth links bipedal walking robot has high nonlinearity and uncertainty parameters;therefore robust controllers for walking robot should be properly designed. This paper proposes a new robust control scheme based on fuzzy system and ant colony optimization algorithm. Adaptive fuzzy controllers (AFCs) based on ant colony optimization (ACO) algorithm are proposed to eliminate the chattering phenomenon that occurs when the walking robot moves on rough surfaces. Six rotational joints are used to connect the seven links of the bipedal walking robot. Those joints are assumed frictionless and actuated by six independent servomotors. Therefore, six adaptive fuzzy controllers are designed in this work (one for each joint). To design robust fuzzy controllers, the ant colony optimization algorithm is utilized to tune and find the best parameters of the output membership function of the fuzzy controller. For comparison purposes, optimal PID controllers (OPIDCs) are designed with optimal parameters that are chosen by utilizing the ant colony algorithm. The performances of the two proposed controllers (AFCs and OPIDCs) are tested under significant disturbances situations such as carrying different weighted things by the walking biped robot. In addition, the stability of the adaptive fuzzy controller is studied and proved by applying Lyapunov theory.

关键词： Seven-link biped robot Adaptive fuzzy control Optimal PID control ant colony optimization algorithm Nonlinear system

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An ant colony optimization algorithm for Knight's Tour Problem on the chessboard with holes

An Ant Colony Optimization Algorithm for Knight's Tour Probl...

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1st International Workshop on Education Technology and Computer Science

作者： Jiang Delei Bai Sen Dong Wenming Chongqing Commun Inst Image Commuicat Lab Chongqing Chongqing 400035 Peoples R China

ISBN: (纸本)9780769535579

Knight's tour problem (KTP) on the chessboard with holes is an especial but interest tour problem. We improve the basic ant colony optimization,and introduce more better pheromone resource update and sharing mechanism,, and find its tour path solutions on the chessboard with holes with the help of colonies sharing the information resource by their intercommunion. We have used the algorithm to encumerate the knight's tours on different chessboard with holes, and made the particular analysis for produce. Experiment results show that the algorithm not only produce the path solutions quickly on the chessboard with holes, but also is easy to control the path starting square and path style.

关键词： KTP chessboard with holes ant colony optimization algorithm muti-ant-colony optimization algorithm

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ant colony optimization algorithm for Robot Path Planning

Ant Colony Optimization Algorithm for Robot Path Planning

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2015 International Conference on Electrical, Automation and Mechanical Engineering(EAME 2015)

作者： Q.L.Xu D.X.Zhang College of Automation and Electronic Engineering Qingdao University of Science and Technology

ISBN: (纸本)9781510806528

In this article two different optimization algorithms are presented to solve the deficiency of ant colony algorithm such as slow convergence rate and easy to fall into local *** method based on Max-Min ant System,established an adaptive model for pheromone evaporation coefficient adjusted adaptively and avoided the ants falling into local *** the same time,this optimization algorithm used the strategy of the survival of the fittest way to optimize the pheromone update mechanism to accelerate the convergence ***,by comparison with ant colony algorithm,the simulation results show that,both the optimal path and routing time are optimized,and proved that the optimization algorithm is valid and feasible.

关键词： path planning ant colony optimization algorithm robot Max-Min ant System

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A Differential Pheromone Grouping ant colony optimization algorithm for the 1-D Bin Packing Problem

A Differential Pheromone Grouping Ant Colony Optimization Al...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Ali, Aseel Ismael Keedwell, Edward Helal, Ayah Univ Exeter Exeter Devon England

ISBN: (纸本)9798400704949

The bin packing problem (BPP) is a well-researched and important NP-hard problem with many contemporary applications (e.g. stock cutting, machine scheduling), which requires a set of items with variable sizes to be packed into a set of fixed-capacity containers. Many metaheuristic approaches have been successfully trialled on this problem, including evolutionary algorithms, ant colony optimization and local search techniques. The most successful variants of these approaches use grouping techniques whereby the algorithm considers sets of items together rather than as separate decision variables. This paper presents an ant colony optimization integrated with a grouping technique and a novel differential pheromone procedure for bin packing. The proposed differential pheromone grouping ACO shows state-of-the-art results for ACO approaches in BPP and approaches the performance of the best evolutionary methods.

关键词： ant colony optimization algorithm One-dimensional bin packing Grouping problem Differential pheromone

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