ant colony optimization is an evolutionary search procedure based on the way that ant colonies cooperate in locating shortest routes to food sources. Early implementations focussed on the travelling salesman and other...
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ant colony optimization is an evolutionary search procedure based on the way that ant colonies cooperate in locating shortest routes to food sources. Early implementations focussed on the travelling salesman and other routing problems but it is now being applied to an increasingly diverse range of combinatorial optimization problems. This paper is concerned with its application to the examination scheduling problem. It builds on an existing implementation for the graph colouring problem to produce clash-free timetables and goes on to consider the introduction of a number of additional practical constraints and objectives. A number of enhancements and modi. cations to the original algorithm are introduced and evaluated. Results based on real-examination scheduling problems including standard benchmark data ( the Carter data set) show that the final implementation is able to compete effectively with the best-known solution approaches to the problem.
In order to make full use of the slot of runway, reduce flight delay, and ensure fairness among airlines, a schedule optimization model for arrival-departure flights is established in the paper. The total delay cost a...
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In order to make full use of the slot of runway, reduce flight delay, and ensure fairness among airlines, a schedule optimization model for arrival-departure flights is established in the paper. The total delay cost and fairness among airlines are two objective functions. The ant colony algorithm is adopted to solve this problem and the result is more efficient and reasonable when compared with FCFS (first come first served) strategy. Optimization results show that the flight delay and fair deviation are decreased by 42.22% and 38.64%, respectively. Therefore, the optimization model makes great significance in reducing flight delay and improving the fairness among all airlines.
Particle filters (PF), as a kind of non-linear/non-Gaussian estimation method, are suffering from two problems in large-dimensional cases, namely particle impoverishment and sample size dependency. Previous studies fr...
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Particle filters (PF), as a kind of non-linear/non-Gaussian estimation method, are suffering from two problems in large-dimensional cases, namely particle impoverishment and sample size dependency. Previous studies from the authors have proposed a novel PF algorithm that incorporates ant colony optimisation (PFACO), to alleviate these problems. In this paper the authors will provide a theoretical foundation of this new algorithm;two theorems are introduced to validate that the PFACO introduces smaller Kullback-Leibler divergence (K-L divergence) between the proposal distribution and the optimal one compared to those produced by the generic PF. In addition, with the same threshold level, the PFACO has a higher probability than the generic PF to achieve a certain K-L divergence. A mobile robot localisation experiment is applied to examine the performance between various PF schemes.
We consider the problem of patrolling-i.e. ongoing exploration of a network by a decentralized group of simple memoryless robotic agents. The model for the network is an undirected graph, and our goal, beyond complete...
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We consider the problem of patrolling-i.e. ongoing exploration of a network by a decentralized group of simple memoryless robotic agents. The model for the network is an undirected graph, and our goal, beyond complete exploration, is to achieve close to uniform frequency of traversal of the graph's edges. A simple multi-agent exploration algorithm is presented and analyzed. It is shown that a single agent following this procedure enters, after a transient period, a periodic motion which is an extended Eulerian cycle, during which all edges are traversed an identical number of times. We further prove that if the network is Eulerian, a single agent goes into an Eulerian cycle within 2\E\D steps, \E\ being the number of edges in the graph and D being its diameter. For a team of k agents, we show that after at most 2(l + 1/k)\E\D steps the numbers of edge visits in the network are balanced up to a factor of two. In addition, various aspects of the algorithm are demonstrated by simulations.
The weapon-target assignment (WTA) problem is crucial for strategic planning in military decision-making operations. It defines the best way to assign defensive resources against threats in combat scenarios. This is a...
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The weapon-target assignment (WTA) problem is crucial for strategic planning in military decision-making operations. It defines the best way to assign defensive resources against threats in combat scenarios. This is a NP-complete problem where no exact solution is available to deal with all possible scenarios. A critical issue in modeling the WTA problem is the time performance of the developed algorithms, subject only recently contemplated in related publications. This paper presents a hybrid approach which combines an ant colony optimization with a greedy algorithm, called the Greedy ant Colony System (GACS), in which a multi colony parallel strategy was also implemented to improve the results. Aiming at large scale air combat scenarios, simulations controlling the algorithm time performance were executed achieving good quality results.
Receiving updated information about the network of roads from high resolution satellite imagery is a crucially important issue in continuously changing developing urban regions. Considering experiences in road extract...
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Receiving updated information about the network of roads from high resolution satellite imagery is a crucially important issue in continuously changing developing urban regions. Considering experiences in road extraction and also exploiting distributed evolutionary computational approaches, in this paper a new framework for road map updating from remotely sensed data is proposed. Three main computational entities of ant-agent, seed extractor and algorithm library are designed and road map updating is performed through three main stages of verification of the old map, extraction of possible roads and grouping of the results of both stages. Extracting corresponding pixels to each road element in the map, an object level supervised classification or any available road verification algorithm from the library capable of producing a road likeliness value is applied. Since road extraction is a simple and also a complex problem, more comprehensive algorithms are chosen from library iteratively by ant-agents so the decision about verification and rejection of each road element is finally made. ant-agents facilitate choosing road elements and moving of ant agents via stigmergic communication by pheromone cast and evaporation. The proposed method is developed and tested using GeoEye-1 pan-sharpen imagery and 1:2000 corresponding digital vector map of the region. As observed, the results are satisfactory in terms of detection, verification and extraction of roads and generation of the updated map specifically in case of inspection of main roads. Besides, some missed road items are reported in case of inspection of bystreets and alleys specially when situated at the margin of the image. Completeness, correctness and quality measures are computed for evaluation of the initial and the resulted updated maps. The computed measures verify the improvement of the updated map. (C) 2012 Elsevier Ltd. All rights reserved.
This article proposes to solve the oversaturated network traffic signal coordination problem using the ant Colony Optimization (ACO) algorithm. The traffic networks used are discrete time models which use green times ...
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This article proposes to solve the oversaturated network traffic signal coordination problem using the ant Colony Optimization (ACO) algorithm. The traffic networks used are discrete time models which use green times at all the intersections throughout the considered period of oversaturation as the decision variables. The ACO algorithm finds intelligent timing plans which take care of dissipation of queues and removal of blockages as opposed to the sole cost minimization usually performed for undersaturation conditions. Two scenarios are considered and results are rigorously compared with solutions obtained using the genetic algorithm (GA), traditionally employed to solve oversaturated conditions. ACO is shown to be consistently more effective for a larger number of trials and to provide more reliable solutions. Further, as a master-slave parallelism is possible for the nature of ACO algorithm, its implementation is suggested to reduce the overall execution time allowing the opportunity to solve real-time signal control systems.
We solve the problem of constructing extended finite state machines with execution scenarios and temporal formulas. We propose a new algorithm pstMuACO that combines a scenario filtering procedure, an exact algorithm ...
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We solve the problem of constructing extended finite state machines with execution scenarios and temporal formulas. We propose a new algorithm pstMuACO that combines a scenario filtering procedure, an exact algorithm efsmSAT for constructing finite state machines from execution scenarios based on a reduction to the Boolean satisfiability problem, and a parallel ant colony algorithm pMuACO. Experiments show that constructing several initial solutions for the ant colony algorithm with reduced sets of scenarios significantly reduces the total time needed to find optimal solutions. The proposed algorithm can be used for automated construction of reliable control systems.
Regression testing is applied whenever a code changes, ensuring that the modifications fixed the fault and no other faults are introduced. Due to a large number of test cases to be run, test case prioritization is one...
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Regression testing is applied whenever a code changes, ensuring that the modifications fixed the fault and no other faults are introduced. Due to a large number of test cases to be run, test case prioritization is one of the strategies that allows to run the test cases with the highest fault rate first. The aim of the paper is to present an optimized test case prioritization method inspired by ant colony optimization, test case prioritization-ant. The criteria used by the optimization algorithm are the number of faults not covered yet by the selected test cases and the sum of severity of the faults. The cost, i.e. time execution, for test cases is considered in the computation of the pheromone deposited on the graph's edges. The average percentage of fault detected metric, as best selection criterion, is used to uncover maximum faults with the highest severity, and reducing the regression testing time. Several experiments are considered, detailed and discussed, comparing various algorithm parameter's alternatives. A benchmark project is also used to validate the proposed approach. The obtained results are encouraging, being a cornerstone for new perspectives to be considered.
This paper proposes a new part clustering algorithm that uses the concept of ant-based clustering in order to resolve machine cell formation problems. The three-phase algorithm mainly utilizes distributed agents which...
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This paper proposes a new part clustering algorithm that uses the concept of ant-based clustering in order to resolve machine cell formation problems. The three-phase algorithm mainly utilizes distributed agents which mimic the way real ants collect similar objects to form meaningful piles. In the first phase, an ant-based clustering model is adopted to form the initial part families. For the purpose of part clustering, a part similarity coefficient is modified and used in the similarity density function of the model. In the second phase, the K-means method is employed in order to achieve a better grouping result. In the third phase, artificial ants are used again to merge the small, refined part families into larger part families in a hierarchical manner. This would increase the flexibility of determining the number of final part families for the factory layout designer. The proposed algorithm has been developed into a software system called the ant-based part clustering system (APCS). In addition to part family formation, APCS performs the tasks of machine assignment and performance evaluation. Finally, performance evaluation of the proposed algorithm was conducted by testing some well-known problems from literature. The evaluation results show that the algorithm is able to solve the cell formation problems effectively.
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