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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. [ABSTRACT FROM AUTHOR]

Recent advances in automatic machine learning (aML) allow solving problems without any human intervention. However, sometimes a human-in-the-loop can be beneficial in solving computationally hard problems. In this paper we provide new experimental insights on how we can improve computational intelligence by complementing it with human intelligence in an interactive machine learning approach (iML). For this purpose, we used the Ant Colony Optimization (ACO) framework, because this fosters multi-agent approaches with human agents in the loop. We propose unification between the human intelligence and interaction skills and the computational power of an artificial system. The ACO framework is used on a case study solving the Traveling Salesman Problem, because of its many practical implications, e.g. in the medical domain. We used ACO due to the fact that it is one of the best algorithms used in many applied intelligence problems. For the evaluation we used gamification, i.e. we implemented a snake-like game called Traveling Snakesman with the MAX–MIN Ant System (MMAS) in the background. We extended the MMAS–Algorithm in a way, that the human can directly interact and influence the ants. This is done by "traveling" with the snake across the graph. Each time the human travels over an ant, the current pheromone value of the edge is multiplied by 5. This manipulation has an impact on the ant's behavior (the probability that this edge is taken by the ant increases). The results show that the humans performing one tour through the graphs have a significant impact on the shortest path found by the MMAS. Consequently, our experiment demonstrates that in our case human intelligence can positively influence machine intelligence. To the best of our knowledge this is the first study of this kind. [ABSTRACT FROM AUTHOR]

A well known $$\mathcal{NP}$$ -hard problem called the generalized traveling salesman problem ( GTSP) is considered. In GTSP the nodes of a complete undirected graph are partitioned into clusters. The objective is to find a minimum cost tour passing through exactly one node from each cluster. An exact exponential time algorithm and an effective meta-heuristic algorithm for the problem are presented. The meta-heuristic proposed is a modified Ant Colony System ( ACS) algorithm called reinforcing Ant Colony System which introduces new correction rules in the ACS algorithm. Computational results are reported for many standard test problems. The proposed algorithm is competitive with the other already proposed heuristics for the GTSP in both solution quality and computational time. [ABSTRACT FROM AUTHOR]

During the last years several ant-based techniques were involved to solve hard and complex optimization problems. The current paper is a short study about the influence of artificial ant species in solving optimization problems. There are studied the artificial Pharaoh Ants, Lasius Niger and also artificial ants with no special specificity used commonly in Ant Colony Optimization. [ABSTRACT FROM AUTHOR]

Combinatorial problems arising in diverse application domains and the growing complexity of many real-world situations emphasize the need for efficient solving methods. One such problem is the bandwidth minimization problem having broad applications in engineering, science, logistics or information recovery. This well-known -complete problem refers to the minimization of the bandwidth for non-zero entries of a sparse symmetric matrix by permuting its rows and columns. A new ant-based approach to matrix bandwidth minimization is proposed. The introduced model is based on the hybridization of the Ant Colony System technique with new problem-tailored local search mechanisms. Computational experiments show a good behaviour of the proposed method for the considered set of bandwidth minimization problem instances. [ABSTRACT FROM PUBLISHER]

The idea of sensitivity in ant colony systems has been exploited in hybrid ant-based models with promising results for many combinatorial optimization problems. Heterogeneity is induced in the ant population by endowing individual ants with a certain level of sensitivity to the pheromone trail. The variable pheromone sensitivity within the same population of ants can potentially intensify the search while in the same time inducing diversity for the exploration of the environment. The performance of sensitive ant models is investigated for solving the generalized vehicle routing problem. Numerical results and comparisons are discussed and analysed with a focus on emphasizing any particular aspects and potential benefits related to hybrid ant-based models. [ABSTRACT FROM AUTHOR]

Nowadays, demicontractive operators in terms of admissible perturbation are used to solve difficult tasks. The current research uses several demicontractive operators in order to enhance the quality of the edge detection results when using ant-based algorithms. Two new operators are introduced, χ -operator and K H -operator, the latter one is a Krasnoselskij admissible perturbation of a demicontractive operator. In order to test the efficiency of the new operators, a comparison is made with a trigonometric operator. Ant Colony Optimization (ACO) is the solver chosen for the images edge detection problem. Demicontractive operators in terms of admissible perturbation are used during the construction phase of the matrix of ants artificial pheromone, namely the edge information of an image. The conclusions of statistical analysis on the results shows a positive influence of proposed operators for image edge detection of medical images. [ABSTRACT FROM AUTHOR]