11,425 research outputs found
Планирование маршрутизации по доставке товаров в городе на основе оптимизации муравьиного алгоритма
For any company that sells its products in the networks of city stores, the urgent issue is the optimal delivery of their goods. During routing it is necessary to take into account many restrictions caused by specific conditions of transportation process in the city: number of cargoes, nature of cargoes, delivery time, structure of fleet and its presence, work time of enterprises for load matching, drivers’ working hours, loading capacity, road congestion etc. These days, the process of efficient manual routing is difficult because of many restrictions and delivery points wherein it is almost impossible to take into account the road congestion for specific routes. Today's companies are increasingly interested in outsourcing. One of the options for routes planning for enterprises is to use special software products that allow to plan optimal routes according to the chosen criteria and under specific conditions. The process of formation of routes using the Ant Logistics service, based on the Ant Colony optimization algorithm are analysed in the paper. Comparing the two options of forming routes to serve one of the largest retail chains in Kharkiv with the application of Ant Logistics service, it has been elucidated that the application of Ant Colony algorithm is more optimal than the Clarke-Wright algorithm based on delivery routes indicators
Adaptive multimodal continuous ant colony optimization
Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima
A new sequential covering strategy for inducing classification rules with ant colony algorithms
Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not coping with the problem of rule interaction, i.e., the outcome of a rule affects the rules that can be discovered subsequently since the search space is modified due to the removal of examples covered by previous rules. This paper proposes a new sequential covering strategy for ACO classification algorithms to mitigate the problem of rule interaction, where the order of the rules is implicitly encoded as pheromone values and the search is guided by the quality of a candidate list of rules. Our experiments using 18 publicly available data sets show that the predictive accuracy obtained by a new ACO classification algorithm implementing the proposed sequential covering strategy is statistically significantly higher than the predictive accuracy of state-of-the-art rule induction classification algorithms
A Deterministic Model for Analyzing the Dynamics of Ant System Algorithm and Performance Amelioration through a New Pheromone Deposition Approach
Ant Colony Optimization (ACO) is a metaheuristic for solving difficult
discrete optimization problems. This paper presents a deterministic model based
on differential equation to analyze the dynamics of basic Ant System algorithm.
Traditionally, the deposition of pheromone on different parts of the tour of a
particular ant is always kept unvarying. Thus the pheromone concentration
remains uniform throughout the entire path of an ant. This article introduces
an exponentially increasing pheromone deposition approach by artificial ants to
improve the performance of basic Ant System algorithm. The idea here is to
introduce an additional attracting force to guide the ants towards destination
more easily by constructing an artificial potential field identified by
increasing pheromone concentration towards the goal. Apart from carrying out
analysis of Ant System dynamics with both traditional and the newly proposed
deposition rules, the paper presents an exhaustive set of experiments performed
to find out suitable parameter ranges for best performance of Ant System with
the proposed deposition approach. Simulations reveal that the proposed
deposition rule outperforms the traditional one by a large extent both in terms
of solution quality and algorithm convergence. Thus, the contributions of the
article can be presented as follows: i) it introduces differential equation and
explores a novel method of analyzing the dynamics of ant system algorithms, ii)
it initiates an exponentially increasing pheromone deposition approach by
artificial ants to improve the performance of algorithm in terms of solution
quality and convergence time, iii) exhaustive experimentation performed
facilitates the discovery of an algebraic relationship between the parameter
set of the algorithm and feature of the problem environment.Comment: 4th IEEE International Conference on Information and Automation for
Sustainability, 200
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