Nature-inspired Intelligent Techniques for Pap Smear Diagnosis: Ant Colony Optimization for Cell Classification

ABSTRACT: During the last years, Nature Inspired Intelligent Techniques have been very attractive. In this paper, one of the most important Nature Inspired Intelligent Techniques, the Ant Colony Optimization (ACO), is presented for the solution of the Pap Smear Cell Classification problem. ACO is derived from the foraging behaviour of real ants in nature. The main idea of ACO is to model the problem as the search for a minimum cost path in a graph. Artificial ants walk through this graph, looking for good paths. Each ant has a rather simple behaviour so that it will typically only find rather poor-quality paths on its own. Better paths are found as the emergent result of the global cooperation among ants in the colony. This algorithm is combined with a number of nearest neighbor based classifiers. The algorithm is tested in two sets of data. The first one consists of 917 images of Pap smear cells and the second set consists of 500 images, classified carefully by cyto-technicians and doctors. Each cell is described by 20 features, and the cells fall into 7 classes but a minimal requirement is to separate normal from abnormal cells, which is a 2 class problem

An Adaptive Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

A new hybridized algorithm based on Particle Swarm Optimization is proposed for the solution of the Vehicle Routing Problem with Time Windows. The algorithm uses a relative new topology, the Combinatorial Neighborhood Topology and, thus, a solution is not needed to be transformed in continuous values during the iterations, which makes Particle Swarm Optimization a competitive algorithm in solving routing problems. Also, in the proposed algorithm all the parameters (acceleration coefficients, iterations, local search iterations, upper and lower bounds of the velocities and of the positions and number of particles) are optimized during the procedure and, thus, the algorithm works independently and without any interference from the user. All parameters are randomly initialized and, afterwards, during the iterations the parameters are adapted based on a number of different conditions. The algorithm uses a number of different velocities’ equations and each particle selects randomly its velocity equation and during the iterations the particle has the possibility to change the velocity equation based on the produced quality of the solution. The algorithm is tested in known benchmark instances from the literature and gives very good results. It is also compared with other algorithms from the literature.Godkänd; 2014; 20141124 (athmig

A hybrid Dragonfly algorithm for the vehicle routing problem with stochastic demands

A number of swarm intelligence algorithms have been proposed during the last years. Most of them are suitable for the solution of continuous optimization problems. One of them is the Dragonfly Algorithm that has been proved very efficient in the problems in which it has been applied. However, few of the newly proposed algorithms have been used for the solution of a routing problem. In this paper, a new hybridized version of the Dragonfly Algorithm with the Combinatorial Expanding Neighborhood Topology is proposed and analyzed in details. The proposed Combinatorial Expanding Neighborhood Topology Dragonfly Algorithm is an algorithm that combines a very powerful swarm intelligence algorithm, the Dragonfly algorithm, with a very effective procedure, the Combinatorial Expanding Neighborhood Topology. This algorithm was used for solving a well known routing problem, the Vehicle Routing Problem with Stochastic Demands. The algorithm was tested in 40 benchmark instances from the literature and gave, in some of them new, best solutions. It was, also, compared with 10 other swarm intelligence algorithms from the literature proving its effectiveness, as it was ranked in the first place among all the algorithms

13th Learning and Intelligent Optimization Conference

This book constitutes the thoroughly refereed pChania, Crete, Greece, in May 2019. The 38 full papers presented have been carefully reviewed and selected from 52 submissions. The papers focus on advanced research developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence and describe advanced ideas, technologies, methods, and applications in optimization and machine learning

APPLICATION OF ANT COLONY OPTIMIZATION TO CREDIT RISK ASSESSMENT

This paper presents a novel approach to solve feature subset selection problems using an Ant Colony Optimization (ACO) algorithm. ACO is one of the important naturally inspired intelligent techniques. It is based on the foraging behavior of real ants in nature. The proposed ACO is combined with a number of nearest neighbor classifiers. The resulting ACO algorithm is applied to classify credit risk using data belonging to 1,411 firms obtained from a leading Greek commercial bank. The objective is to classify subject firms into several groups representing different levels of credit risk. The results of the proposed algorithm are compared with those of others including SVM, CART, and with two other metaheuristic algorithms using tabu search and genetic algorithms, both of which use nearest neighbor classifiers in the classification phase. The results suggest that the proposed method is more accurate than others that have been tested in classifying credit risk.Ant Colony Optimization, feature selection problem, credit risk assessment, nearest neighbor based classifiers

Dataset for the cumulative unmanned aerial vehicle routing problem

The Cumulative Unmanned Aerial Vehicle Routing Problem applies to area covering operations using UAVs. It is defined on a graph, the nodes of which, ensure the complete coverage of the underlying area of interest. The data generation process takes into account the characteristics of such operations, in particular, the viewing window of the UAVs’ sensor, their maximum range, the size of the UAV fleet and the unknown locations of the targets within the area of interest. Instances are created simulating different scenarios, using different values for those UAV characteristics, as well as the different locations where the search targets might be positioned in the area of interest

Assessing Simulated Annealing with Variable Neighborhoods

Simulated annealing (SA) is a well-known metaheuristic commonly used to solve a great variety of -hard problems such as the quadratic assignment problem (QAP). As commonly known, the choice and size of neighborhoods can have a considerable impact on the performance of SA. In this work, we investigate and propose a SA variant that considers variable neighborhood structures driven by the state of the search. In the computational experiments, we assess the contribution of this SA variant in comparison with the state-of-the-art SA for the QAP applied to printed circuit boards and conclude that our approach is able to report better solutions by means of short computational times