Global path planning remains a critical challenge in mobile robots, with antcolonyoptimization (ACO) being widely adopted for its swarm intelligence characteristics. To address the inherent limitations of ACO, this ...
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Global path planning remains a critical challenge in mobile robots, with antcolonyoptimization (ACO) being widely adopted for its swarm intelligence characteristics. To address the inherent limitations of ACO, this study proposes an intelligently enhanced ACO (IEACO) incorporating six innovative strategies. First, the early search efficiency is improved by implementing a non-uniform initial pheromone distribution. Second, the epsilon-greedy strategy is employed to adjust the state transition probability, thereby balancing exploration and exploitation. Third, adaptive dynamic adjustment of the exponents alpha and beta is realized, dynamically balancing the pheromone and heuristic function. Fourth, a multi-objective heuristic function considering both target distance and turning angle is constructed to enhance the quality of node selection. Fifth, a dynamic global pheromone update strategy is designed to prevent the algorithm from prematurely converging to local optima. Finally, by introducing multi-objective performance indicators, the path planning problem is transformed into a multi-objective optimization problem, enabling more comprehensive path optimization. Systematic simulations and experimentation were performed to validate the effectiveness of IEACO. The simulation results confirm the efficacy of each improvement in IEACO and demonstrate its performance advantages over other algorithms. The experimental results further highlight the practical value of IEACO in solving global path planning problems for mobile robots.
In e-learning, extracting suitable learning objects (LOs) from a vast resource pool and organizing them into high-quality learning paths is crucial for helping e-learners achieve their goals. Numerous approaches have ...
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In e-learning, extracting suitable learning objects (LOs) from a vast resource pool and organizing them into high-quality learning paths is crucial for helping e-learners achieve their goals. Numerous approaches have been proposed to recommend optimal learning paths for e-learners. However, it is essential to emphasize that e-learning systems typically consist of a wide range of LOs with varying levels of granularity, ranging from fine-grained to coarse-grained. Unfortunately, current research has not adequately considered the underlying granularity structure of LOs when optimizing learning paths. Existing methods primarily focus on organizing LOs at a single granularity level, limiting their applicability in real-world e-learning systems. To address the limitations, we propose a multigranularity learning path recommendation (MGLPR) framework that aims to flexibly and effectively integrate the diverse granularity levels of LOs into high-quality learning paths. In this framework, a two-layer [knowledge point (KP) and LO layers] model is developed to formulate the MGLPR problem as a constrained optimization problem and an improved ant colony optimization algorithm (IACO) is introduced to solve it to identify optimal learning paths for e-learners. To evaluate the effectiveness of the proposed IACO, we conducted extensive computational experiments using 30 simulation datasets with varying problem sizes and complexities. The results demonstrate that our proposed IACO achieves superior performance and robustness compared with other competitors. Additionally, an empirical study was conducted to investigate the efficacy of the proposed approach in an authentic learning context, with results indicating that the proposed method outperforms the traditional self-organized ones.
Ceramic firing process parameters can directly affect mechanical properties and surface quality of ceramics. To improve the quality of ceramics, the ant colony optimization algorithm is applied. Parameters such as fir...
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Ceramic firing process parameters can directly affect mechanical properties and surface quality of ceramics. To improve the quality of ceramics, the ant colony optimization algorithm is applied. Parameters such as firing temperature, heating rate, and cooling rate are selected. Each ant represents a parameter combination. The initial pheromone is set to a constant. Simulation is carried out by random selection, and the fitness is calculated. Excellent combinations can accumulate more information, which makes subsequent ants tend to choose them and gradually converge to the optimal combination. When the fitness function value is stable, the algorithm terminates and outputs the optimal parameters. Whale optimizationalgorithm, Bayesian optimizationalgorithm, and genetic algorithm are used for comparison in the experiment. The results show that the ant colony optimization algorithm is more efficient in finding the optimal solution, reducing the number of iterations by 9% to 23% and saving 5% to 13% in time cost. The optimal solution of the ant colony optimization algorithm is used for firing, with the highest and most stable yield rate reaching 100%. The ant colony optimization algorithm can effectively optimize process parameters, improve ceramic quality, and reduce defects.
In this paper, a novel ant colony optimization algorithm with random perturbation behavior (RPACO) based on combination of general antcolonyoptimization and stochastic mechanism is developed for the solution of opti...
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In this paper, a novel ant colony optimization algorithm with random perturbation behavior (RPACO) based on combination of general antcolonyoptimization and stochastic mechanism is developed for the solution of optimal unit commitment (UC) with probabilistic spinning reserve determination. In general, the purpose of UC is to enhance the economical efficiency as could as possible while simultaneously satisfying physical and operation constraints of individual unit. Consider the possibility of generating unit failure, the requirement, the sufficient spinning reserve capacity to ensure adequate reliability levels, must be satisfied by the commitment schedule. The security function approach is applied to evaluate the desired level of system security, and the proposed method in this paper, RPACO, is adopted to solve the UC problems. The effectiveness of the proposed method has been demonstrated on the corresponding numerical results. Further, the sensitivity of the desired security level to the optima during optimization is investigated in this paper. (C) 2003 Elsevier B.V. All rights reserved.
The article describes the application of an antalgorithm to optimize parameters of the ship course controller, based on the algorithm of PID control. The antalgorithm is a method of combinatorial optimization, which...
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The article describes the application of an antalgorithm to optimize parameters of the ship course controller, based on the algorithm of PID control. The antalgorithm 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 antcolonyalgorithm, 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. (C) 2014 The Authors. Published by Elsevier B.V.
This paper studies container loading optimization problem. This problem is a subset of rectangular boxes loaded into a rectangular container with fixed dimensions such that maximize container's utilization ratio. ...
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ISBN:
(纸本)9781424451821
This paper studies container loading optimization problem. This problem is a subset of rectangular boxes loaded into a rectangular container with fixed dimensions such that maximize container's utilization ratio. A mathematical model is given. Some principles which include space division, space merger, residual subspace omitted and loading rule are presented. A hybrid algorithm which integrate ant colony optimization algorithm with above principles is used to solve the container loading problem. The simulation results show that the model and the algorithm are effective.
The development of educational informatization has greatly changed the current educational model of colleges and universities (CAU). The use of informatization technology combined with the characteristics of disciplin...
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ISBN:
(纸本)9781450397889
The development of educational informatization has greatly changed the current educational model of colleges and universities (CAU). The use of informatization technology combined with the characteristics of disciplines to build a TP to promote the development of informatization teaching. This paper adopts the B/S model to design the framework of the university subject TP. The user only needs to install a browser to carry out teaching and learning activities, which simplifies the use of the system. With the support of Web technology, the teaching platform (TP) can not only realize teaching evaluation, but also manage and store subject resources. In the process of platform login, this paper uses the characteristics of antcolonyoptimization (ACO) algorithm to match users with password strings with high security factors, ensuring the system Login security.
Due to the complex variations in slope within deep-sea mining areas, effective path planning for mining vehicle operations is crucial for minimizing energy consumption. However, traditional antcolonyalgorithms (ACO)...
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Due to the complex variations in slope within deep-sea mining areas, effective path planning for mining vehicle operations is crucial for minimizing energy consumption. However, traditional antcolonyalgorithms (ACO) neglect the effect of a terrain slope in mining areas. Additionally, these algorithms exhibit limitations such as slow convergence and susceptibility to local optima. To address these issues, this study proposes an enhanced antcolonyalgorithm, called DYACO, for mining vehicle path optimization. This algorithm dynamically adjusts heuristic information, pheromone volatilization factor, pheromone update strategy, and state transition probability during the iterative process to enhance traditional ACO. Simulation experiments were conducted to comprehensively assess the proposed model, revealing that DYACO not only generates optimal solutions but also demonstrates significant advantages in terms of convergence speed and turning times. Furthermore, DYACO converts the time required for mining vehicles to traverse different slope regions into distances, then incorporating slope effects to path planning for deep-sea mining vehicles. In comparison to ACO, DYACO achieves a 15.3% reduction in the length of an optimal path and a 70.0% decrease in the number of turn times.
Artificial life uses biological knowledge and techniques to solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems ...
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Artificial life uses biological knowledge and techniques to solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. In this study, artificial life based approaches are handled and incorporated to enable a real-time water level control. The process was first modelled using NARX type Artificial Neural Network. A fuzzy controller was then attached to the model. For a better performance, fuzzy controller membership function boundary values and action values were optimized simultaneously. The optimization process was performed using genetic algorithm and ant colony optimization algorithm, respectively. Finally, the performance of the controllers was discussed further by considering the system outputs. The developed structure replaces the tedious process of trial-and-error for better combination of fuzzy parameters and can settle the problem of designing fuzzy controller without an expert's experience.
Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid antcolony ...
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Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid ant colony optimization algorithm (hACO), an improved metaheuristic algorithm to conduct model specification searches in SEM. This data mining algorithm combines aspects of the ant colony optimization algorithm with the Tabu search algorithm to increase both accuracy and efficiency. A Monte Carlo simulation study showed that the hACO algorithm provided accurate and efficient SEM specification searches across all designed simulation conditions. The hACO algorithm can help applied researchers conduct specification searches while avoiding potential model misspecifications.
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