- This article deals with the problem of finding the maximum number of maximum cliques in a weighted graph with all edges between vertices from different d -division of a graph with the minimum total weight of all the...
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- This article deals with the problem of finding the maximum number of maximum cliques in a weighted graph with all edges between vertices from different d -division of a graph with the minimum total weight of all these cliques, and the problem of finding the maximum number of maximum cliques in a nonweighted graph with not all edges between vertices from different d -division of the graph. This article presents new ant algorithms with new desire functions for these problems. These algorithms were tested for their purpose with different changing input parameters, the test results were tabulated and discussed, the best algorithms were indicated.
This study addresses a capacitated facility location and task allocation problem of a multi-echelon supply chain against risky demands. Two and three-echelon networks are considered to maximize profit. The study repre...
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This study addresses a capacitated facility location and task allocation problem of a multi-echelon supply chain against risky demands. Two and three-echelon networks are considered to maximize profit. The study represents the problem by a bi-level stochastic programming model. The revised ant algorithm proposed in the study improves the existing ant algorithm by using new design of heuristic desirability and efficient greedy heuristics to solve the problem. A set of computational experiments is reported to not only allow to fine-tune the parameters of the algorithm but also to evaluate its performance for solving the problem proposed. Experiments reveal that the proposed solution algorithm can reach 95-99% of the optimal solution against risky demands while consuming only 1000th of the computational time for large-sized problems as compared to an optimization-based tool. (C) 2015 Elsevier B.V. All rights reserved.
Transport system in most cities has some problems and should be optimized. In particular, timetable of the city public transportation needs to be changed. Metaheuristic methods for timetabling were considered the most...
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ISBN:
(纸本)9783319452425;9783319452432
Transport system in most cities has some problems and should be optimized. In particular, timetable of the city public transportation needs to be changed. Metaheuristic methods for timetabling were considered the most efficient. ant algorithm was chosen as one of these methods. It was adapted for optimization of an urban public transport timetable. A timetable for one bus route in the city of Tomsk, Russia was created on the basis of the developed software. Different combinations of parameters in ant algorithm allow obtaining new variants of the timetable that better fit passengers' needs.
Intelligent robot such as Unmanned Ground Mobile Robot or Vehicle is a kind of automatic equipment without a manual operation to complete the task. This paper presents a method of trajectory planning for intelligent r...
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ISBN:
(纸本)9781509043644
Intelligent robot such as Unmanned Ground Mobile Robot or Vehicle is a kind of automatic equipment without a manual operation to complete the task. This paper presents a method of trajectory planning for intelligent robots based on the ant colony algorithm under an environment with obstacles. The robot can autonomously avoid obstacles from the starting point to reach the target point during maneuver. The ant colony algorithm is an evolutionary algorithm that by simulating ants foraging in nature. By using ant colony algorithm, this paper gets an optimal or suboptimal motion trajectory and path from starting point to the target point in a static environment based on rasterization environment model.
Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise and outliers can deteriorate the performance of such preprocessing and therefo...
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Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise and outliers can deteriorate the performance of such preprocessing and therefore impair the subsequent analysis tremendously. In manifold learning, several studies indicate solutions for removing background noise or noise close to the structure when the density is substantially higher than that exhibited by the noise. However, in many applications, including astronomical datasets, the density varies alongside manifolds that are buried in a noisy background. We propose a novel method to extract manifolds in the presence of noise based on the idea of ant colony optimization. In contrast to the existing random walk solutions, our technique captures points that are locally aligned with major directions of the manifold. Moreover, we empirically show that the biologically inspired formulation of ant pheromone reinforces this behavior enabling it to recover multiple manifolds embedded in extremely noisy data clouds. The algorithm performance in comparison to state-of-the-art approaches for noise reduction in manifold detection and clustering is demonstrated, on several synthetic and real datasets, including an N-body simulation of a cosmological volume.
This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging poin...
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This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points' operation was presented as the probability of the occurrence of an emergency situation hindering a point's operation, e.g., a breakdown or lack of energy supply. The problem is how to calculate the driving routes of electric vehicles so that they will arrive at charging points at times at which there is a minimal probability of breakdowns. The second aspect of this problem to be solved is that the designated routes are supposed to ensure the minimum energy expenditure that is needed for the vehicles to complete the tasks assigned. The developed method is based on two heuristic algorithms, i.e., the ant algorithm and genetic algorithms. These algorithms work in a hybrid combination, i.e., the ant algorithm generates the initial population for the genetic algorithm. An important element of this method is the decision-making model for defining the driving routes of electric vehicles with various restrictions, e.g., their battery capacity or the permissible risk of charging point breakdown along the routes of the vehicles. The criterion function of the model was defined as the minimisation of the energy expenditure needed by the vehicles to perform their transport tasks. The method was verified against real-life data, and its effectiveness was confirmed. The authors presented a method of calibrating the developed optimisation algorithms. Theoretical distributions of the probability of charging point failure were determined based on the Statistica 13 program, while a graphical implementation of the method was carried out using the PTV Visum 23 software.
The work describes a hybrid algorithm for the dynamic formation of a robot's movement route in nondeterministic environments with bypassing stationary and nonstationary obstacles for two-dimensional space, based o...
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The work describes a hybrid algorithm for the dynamic formation of a robot's movement route in nondeterministic environments with bypassing stationary and nonstationary obstacles for two-dimensional space, based on the integration of wave and ant algorithms, which makes it possible to build trajectories of minimum length in real time with simultaneous optimization of a number of criteria for the quality of the constructed path. Restrictions preventing the construction of a trajectory from the current position are identified during the construction process. The trajectory is constructed step by step. The entire trajectory connecting the robot's initial position with the target position is a collection of individual sections. The time complexity of the algorithm depends on the lifespan of the colony, l (number of iterations);the number of graph vertices, n;the number of ants, m;and is estimated as O(ln2m).
There are more and more researches on fuzzy control. Fuzzy controllers in all walks of life have very successful application cases, but they can be affected by quantification factors in the development process, so mos...
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There are more and more researches on fuzzy control. Fuzzy controllers in all walks of life have very successful application cases, but they can be affected by quantification factors in the development process, so most of the control rules obtained are based on personal experience and have great uncertainty. To solve these problems, in this paper, the intelligent device fuzzy controller was designed and studied with the help of advanced reduced instruction set computing (RISC). The optimal control rules were searched by advanced RISC machines (ARM). These rules were used to generate the corresponding fuzzy controller. The experimental results suggested that the fuzzy controller based on embedded ARM was more accurate for the regulation of computer intelligence devices than the controllers based on ant algorithm and genetic algorithm. The accuracy of the controller studied in this paper was above 94%, while the other two adjustments were below 91% and 92%, respectively. The performance of the controller studied in this paper is also better, which is conducive to improve the performance of computer intelligent equipment, improve the use value of equipment, better improve the accuracy of equipment adjustment, improve the processing speed of fuzzy controller for subset rules, and the running speed is faster.
Applying ICT devices to WDS makes it possible to introduce also the new concept of Smart WAter Network (SWAN), as a key Smart City subsystem, improving the traditional management of WDS. The possibility of inserting r...
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Applying ICT devices to WDS makes it possible to introduce also the new concept of Smart WAter Network (SWAN), as a key Smart City subsystem, improving the traditional management of WDS. The possibility of inserting remote-controlled valves and flow meters in a WDS allows to divide a water network into k smaller subsystems, in order to improve the management and protection of WDS. This study proposes a novel technique for water network partitioning based on an ant algorithm that allows to obtain a network partitioning compatible with the hydraulic performance. The technique is applied to a real water network. (C) 2013 The Authors. Published by Elsevier Ltd.
A problem related to energy consumption of a mobile robot involves finding out what route the robot can take that uses the least energy. An ant colony optimization algorithm (ACO) can solve this problem. However, it i...
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A problem related to energy consumption of a mobile robot involves finding out what route the robot can take that uses the least energy. An ant colony optimization algorithm (ACO) can solve this problem. However, it is applicable only for route on a flat terrain. This paper proposes an adapted ant colony optimization (adapted ACO) algorithm that is applicable for route on a rough terrain as well. This adaptation introduces a weight that is the energy expended on a route that may have upward slopes, downward slopes, and flat surfaces. Experiments were conducted to test the algorithm. The experimental results show that our adapted ACO did successfully find a route that expended the least energy, though it was not the shortest one. We also found the following interesting facts: an energy-efficient route has more downward slopes than upward ones;the energy expended increases with the steepness of the slopes along a route;and the energy expended is likely to be lower if the robot's velocity is not constrained to be constant throughout the route.
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