Distributed Learning with Biogeography-Based Optimization

We present hardware testing of an evolutionary algorithm known as biogeography-based optimization (BBO) and extend it to distributed learning. BBO is an evolutionary algorithm based on the theory of biogeography, which describes how nature geographically distributes organisms. We introduce a new BBO algorithm that does not use a centralized computer, and which we call distributed BBO. BBO and distributed BBO have been developed by mimicking nature to obtain an algorithm that optimizes solutions for different situations and problems. We use fourteen common benchmark functions to obtain results from BBO and distributed BBO, and we also use both algorithms to optimize robot control algorithms. We present not only simulation results, but also experimental results using BBO to optimize the control algorithms of mobile robots. The results show that centralized BBO generally gives better optimization results and would generally be a better choice than any of the newly proposed forms of distributed BBO. However, distributed BBO allows the user to find a less optimal solution to a problem while avoiding the need for centralized, coordinated control

Application of opposition-based learning concepts in reducing the power consumption in wireless access networks

The reduction of power consumption in wireless access networks is a challenging and important issue. In this paper, we apply Opposition-Based Learning (OBL) concepts for reducing the power consumption of LTE base stations. More specifically, we present a new Modified Biogeography Based Optimization (BBO) algorithm enhanced with OBL techniques. We apply both the original BBO and the new Modified Opposition BBO (MOBBO) to network design cases to the city of Ghent, Belgium, with 75 possible LTE base station locations. We optimize the network towards two objectives: coverage maximization and power consumption minimization. Preliminary results indicate the advantages and applicability of our approach

Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network

The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.European Union (EU) under Erasmus+ project entitled “Fostering Internationalization in Agricultural Engineering in Iran and Russia” [FARmER] with grant number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JPinfo:eu-repo/semantics/publishedVersio