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Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm

Blood vessels in ophthalmoscope images play an important role in diagnosis of some serious pathologies on retinal images. Hence, accurate extraction of vessels is becoming a main topic of this research area. Matched filter (MF) implementation for blood vessel detection is one of the methods giving more accurate results. Using this filter alone might not recover all the vessels (especially the capillaries). In this paper, a novel approach (MF/ant algorithm) is proposed to overcome the deficiency of the MF. The proposed method is a hybrid model of matched filter and ant colony algorithm. In this work, the accuracy and parameters of the hybrid algorithm are also discussed. The proposed method shows its success using the well known reference ophthalmoscope images of DRIVE database.

An incremental ant colony algorithm with local search for continuous optimization

ACOR is one of the most popular ant colony optimization algorithms for tackling continuous optimization problems. In this paper, we propose IACOR-LS, which is a variant of ACOR that uses local search and that features a growing solution archive. We experiment with Powells conjugate directions set, Powells BOBYQA, and Lin-Yu Tsengs Mtsls1 methods as local search procedures. Automatic parameter tuning results show that IACOR-LS with Mtsls1 (IACOR-Mtsls1) is not only a significant improvement over ACOR, but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal. Further experimentation with IACOR-Mtsls1 on an extended benchmark functions suite, which includes functions from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation, demonstrates its good performance on continuous optimization problems.

An interactive simulation and analysis software for solving TSP using Ant Colony Optimization algorithms

Traveling salesman problem (TSP) is one of the extensively studied combinatorial optimization problems and tries to find the shortest route for salesperson which visits each given city precisely once. Ant colony optimization (ACO) algorithms have been used to solve many optimization problems in various fields of engineering. In this paper, a web-based simulation and analysis software (TSPAntSim) is developed for solving TSP using ACO algorithms with local search heuristics. Algorithms are tested on benchmark problems from TSPLIB and test results are presented. Importance of TSPAntSim providing also interactive visualization with real-time analysis support for researchers studying on optimization and people who have problems in form of TSP is discussed.

An incremental particle swarm for large-scale continuous optimization problems: an example of tuning-in-the-loop (re)design of optimization algorithms

The development cycle of high-performance optimization algorithms requires the algorithm designer to make several design decisions. These decisions range from implementation details to the setting of parameter values for testing intermediate designs. Proper parameter setting can be crucial for the effective assessment of algorithmic components because a bad parameter setting can make a good algorithmic component perform poorly. This situation may lead the designer to discard promising components that just happened to be tested with bad parameter settings. Automatic parameter tuning techniques are being used by practitioners to obtain peak performance from already designed algorithms. However, automatic parameter tuning also plays a crucial role during the development cycle of optimization algorithms. In this paper, we present a case study of a tuning-in-the-loop approach for redesigning a particle swarm-based optimization algorithm for tackling large-scale continuous optimization problems. Rather than just presenting the final algorithm, we describe the whole redesign process. Finally, we study the scalability behavior of the final algorithm in the context of this special issue.

Solution to non-convex economic dispatch problem with valve point effects by incremental artificial bee colony with local search

In literature, economic power dispatch problems are generally categorized as convex and non-convex optimization problems. In this study, incremental artificial bee colony (IABC) and incremental artificial bee colony with local search (IABC-LS) have been used for the solution of the economic dispatch problem with valve point effect. In these kind of problems, fuel cost curve increases as sinusoidal oscillations. In the solution of the problem B loss matrix has been used for the calculation of the line losses. Total fuel cost has been minimized under electrical constraints. IABC and IABC-LS methods have been applied to four different test systems one with 6 buses 3 generators, the other with 14 buses 5 generators (IEEE), the third one with 30 buses 6 generators (IEEE) and the last one is 40-generator system. The obtained best values have been compared with different methods in literature and the results of them have been discussed.

Artificial bee colonies for continuous optimization: Experimental analysis and improvements

The artificial bee colony (ABC) algorithm is a recent class of swarm intelligence algorithms that is loosely inspired by the foraging behavior of honeybee swarms. It was introduced in 2005 using continuous optimization problems as an example application. Similar to what has happened with other swarm intelligence techniques, after the initial proposal, several researchers have studied variants of the original algorithm. Unfortunately, often these variants have been tested under different experimental conditions and different fine-tuning efforts for the algorithm parameters. In this article, we review various variants of the original ABC algorithm and experimentally study nine ABC algorithms under two settings: either using the original parameter settings as proposed by the authors, or using an automatic algorithm configuration tool using a same tuning effort for each algorithm. We also study the effect of adding local search to the ABC algorithms. Our experimental results show that local search can improve considerably the performance of several ABC variants and that it reduces strongly the performance differences between the studied ABC variants. We also show that the best ABC variants are competitive with recent state-of-the-art algorithms on the benchmark set we used, which establishes ABC algorithms as serious competitors in continuous optimization.

Improving performance via population growth and local search: the case of the artificial bee colony algorithm

We modify an artificial bee colony algorithm as follows: we make the population size grow over time and apply local search on strategically selected solutions. The modified algorithm obtains very good results on a set of large-scale continuous optimization benchmark problems. This is not the first time we see that the two aforementioned modifications make an initially non-competitive algorithm obtain state-of-the-art results. In previous work, we have shown that the same modifications substantially improve the performance of particle swarm optimization and ant colony optimization algorithms. Altogether, these results suggest that population growth coupled with local search help obtain high-quality results.

Extraction of flower regions in color images using ant colony optimization

Abstract Extraction of flower regions from complex background is a difficult task and it is an important part of a flower image retrieval and recognition. In this article, we propose an Ant Colony Optimization (ACO) algorithm as a general color clustering method, and test it on flower images as a case study of object boundary extraction. The segmentation methodology on flower images consists of six steps: color space conversion, generation of candidate color cluster centers, ant colony optimization method to select optimum color cluster centers, merging of cluster centers which are close to each other, image segmentation by clustering, and extraction of flower region from the image. To evince that ACO algorithm can be a general segmentation method, some results of natural images in Berkeley segmentation benchmark have been presented. The method as a case study on flower region extraction has also been tested on the images of Oxford-17 Flowers dataset, and the results have confronted with other well established flower region extraction approaches.

ABC-X: a generalized, automatically configurable artificial bee colony framework

The artificial bee colony (ABC) algorithm is a popular metaheuristic that was originally conceived for tackling continuous function optimization tasks. Over the last decade, a large number of variants of ABC have been proposed, making it by now a well-studied swarm intelligence algorithm. Typically, in a paper on algorithmic variants of ABC algorithms, one or at most two of its algorithmic components are modified. Possible changes include variations on the search equations, the selection of candidate solutions to be explored, or the adoption of features from other algorithmic techniques. In this article, we propose to follow a different direction and to build a generalized ABC algorithm, which we call ABC-X. ABC-X collects algorithmic components available from known ABC algorithms into a common algorithm framework that allows not only to instantiate known ABC variants but, more importantly, also many ABC algorithm variants that have never been explored before in the literature. Automatic algorithm configuration techniques can generate from this template new ABC variants that perform better than known ABC algorithms, even when their numerical parameters are fine-tuned using the same automatic configuration process.

An efficient ant-based edge detector

An efficient ant-based edge detector is presented. It is based on the distribution of ants on an image, ants try to find possible edges by using a state transition function based on 5x5 edge structures. Visual comparisons show that the proposed method gives finer details and thinner edges at lesser computational times when compared to earlier ant-based approaches. When compared to standard edge detectors, it shows robustness to Gaussian and Salt & Pepper noise and provides finer details than others with same parameter set in both clear and noisy images.