A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc

An Efficient feature selection algorithm for the spam email classification

The existing spam email classification systems are suffering from the problems of low accuracy due to the high dimensionality of the associated feature selection (FS) process. But being a global optimization process in machine learning, FS is mainly aimed at reducing the redundancy of dataset to create a set of acceptable and accurate results. This study presents the combination of Chaotic Particle Swarm Optimization (PSO) algorithm with Artificial Bees Colony (ABC) for the reduction of features dimensionality in a bid to improve spam emails classification accuracy. The features for each particle in this work were represented in a binary form, meaning that they were transformed into binary using a sigmoid function. The features selection was based on a fitness function that depended on the obtained accuracy using SVM. The proposed system was evaluated for performance by considering the performance of the classifier and the selected features vectors dimension which served as the input to the classifier; this evaluation was done using the Spam Base dataset and from the results, the PSO-ABC classifier performed well in terms of FS even with a small set of selected features

University of Michigan Undergraduate Research Journal, Issue 9, Winter 2012

Articlehttp://deepblue.lib.umich.edu/bitstream/2027.42/97010/1/UMURJ-Issue09_2012.pd

Signatures of a globally optimal searching strategy in the three-dimensional foraging flights of bumblebees

Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards