Binary Ant Colony Optimization Research Articles

Tropical cyclone spiral band extraction and center locating by binary ant colony optimization

Tropical cyclone (TC) center locating is crucial because it lays the foundation for TC forecasting. Locating TC centers, usually by manual means, continues to present many difficulties. Not least is the problem of inconsistency between TC center locations forecast by different agencies. In this paper, an objective TC center locating scheme is developed, using infrared satellite images. We introduce a pattern-matching concept, which we illustrate using a spiral curve model. A spiral band model, based on a spiral band region, is designed to extract the spiral cloud-rain bands (SCRBs) of TCs. We propose corresponding criteria on which to score the fitting value of a candidate template defined by our models. In the proposed scheme, TC location is an optimization problem, solved by an ant colony optimization algorithm. In numerical experiments, a minimal mean distance error of 17.9 km is obtained when the scheme is tested against best-track data. The scheme is suitable for TCs with distinct SCRBs or symmetrical central dense overcast, and for TCs both with and without clear eyes.

Determination Optimum SVMs Classifiers for Hyperspectral Imagery Based on Ant Colony Optimization

Referring to robustness of SVMs in high dimensional space, they are reliable tools for classification of hyperspectral imagery. However their performance is directly affected by two aspects: parameter determination and optimum feature subset selection. According to capacity of population based meta-heuristic optimization algorithm such as Ant Colony Optimization (ACO), they can find optimum or near optimum solution in complex optimization problems. This paper evaluates the potential of Binary ACO (BACO) in parameter determination, feature selection and both of them simultaneously in SVMs based classification system for hyperspectral imagery. Obtained results in comparison with genetic algorithm show superiority of BACO.

A Binary Ant Colony Optimization Classifier for Molecular Activities

Chemical fingerprints encode the presence or absence of molecular features and are available in many large databases. Using a variation of the Ant Colony Optimization (ACO) paradigm, we describe a binary classifier based on feature selection from fingerprints. We discuss the algorithm and possible cross-validation procedures. As a real-world example, we use our algorithm to analyze a Plasmodium falciparum inhibition assay and contrast its performance with other machine learning paradigms in use today (decision tree induction, random forests, support vector machines, artificial neural networks). Our algorithm matches established paradigms in predictive power, yet supplies the medicinal chemist and basic researcher with easily interpretable results. Furthermore, models generated with our paradigm are easy to implement and can complement virtual screenings by additionally exploiting the precalculated fingerprint information.

A Novel Binary Ant Colony Optimization: Application to the Unit Commitment Problem of Power Systems

This paper proposes a novel binary ant colony optimization (NBACO) method. The proposed NBACO is based on the concept and principles of ant colony optimization (ACO), and developed to solve the binary and combinatorial optimization problems. The concept of conventional ACO is similar to Heuristic Dynamic Programming. Thereby ACO has the merit that it can consider all possible solution sets, but also has the demerit that it may need a big memory space and a long execution time to solve a large problem. To reduce this demerit, the NBACO adopts the state probability matrix and the pheromone intensity matrix. And the NBACO presents new updating rule for local and global search. The proposed NBACO is applied to test power systems of up to 100-unit along with 24-hour load demands.

Recourse-Based Facility-Location Problems in Hybrid Uncertain Environment

The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.