Camelia-Mihaela Pintea

Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach

Most Machine Learning (ML) researchers focus on automatic Machine Learning (aML) where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from the availability of “big data”. However, sometimes, for example in health informatics, we are confronted not a small number of data sets or rare events, and with complex problems where aML-approaches fail or deliver unsatisfactory results. Here, interactive Machine Learning (iML) may be of help and the “human-in-the-loop” approach may be beneficial in solving computationally hard problems, where human expertise can help to reduce an exponential search space through heuristics.

Improving ant systems using a local updating rule

An algorithm based on ant colony system for solving traveling salesman problem is proposed. The new algorithm, introduces in ant colony system an inner loop aiming to update the pheromone trails. The update increases the pheromone in the trail followed by the ants and therefore generates improved tours.

The generalized traveling salesman problem solved with ant algorithms

A well known

Heterogeneous sensitive ant model for combinatorial optimization

\mathcal{NP}

A Hybrid Ant-Based System for Gate Assignment Problem

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.

Emergency management using geographic information systems: application to the first Romanian traveling salesman problem instance

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.

A sensitive metaheuristic for solving a large optimization problem

A new metaheuristic called Sensitive Ant Model (SAM) for solving combinatorial optimization problems is proposed. SAM improves and extends the Ant Colony System approach by enhancing each agent of the model with properties that induce heterogeneity. SAM agents are endowed with different pheromone sensitivity levels. Highly-sensitive agents are essentially influenced in the decision making process by stigmergic information and thus likely to select strong pheromone-marked moves. Search intensification can be therefore sustained. Agents with low sensitivity are biased towards random search inducing diversity for exploration of the environment. A heterogeneous agent model has the potential to cope with complex and/or dynamic search spaces. Sensitive agents (or ants) allow many types of reactions to a changing environment facilitating an efficient balance between exploration and exploitation.

A Hybrid Ant-Based Approach to the Economic Triangulation Problem for Input-Output Tables

Abstract Combinatorial problems arising in diverse application domains and the growing complexity of many real-world situationsemphasize the need for efficient solving methods. One such problem is the bandwidth minimization problem having broadapplications in engineering, science, logistics or information recovery. This well-known NP -complete problem refers to theminimization of the bandwidth for non-zero entries of a sparse symmetric matrix by permuting its rows and columns. A newant-based approach to matrix bandwidth minimization is proposed. The introduced model is based on the hybridization ofthe Ant Colony System technique with new problem-tailored local search mechanisms. Computational experiments show agood behaviour of the proposed method for the considered set of bandwidth minimization problem instances. Keywords : Matrix bandwidth minimization problem, ant colony optimization, local search, hybrid models. 1 Introduction Intensively studied for more than 50 years [10], Combinatorial Optimization Problems (COPs)develop in many and diverse areas including network design, storage and retrieval, sequencing andscheduling, algebra theory, automata and language theory, program optimization and game theory.Many of these