We propose a solution to the menu planning problem adapting the bacterial foraging-based optimization algorithm. We design a mathematical model which satisfies the nutritional needs of individuals while enforcing the “Laws of Nutrition”, a set of postulates used by virtually all nutrition scientists in Latin America. This model corresponds to a constrained numerical optimization problem. We design a menu generator software prototype to set up a custom menu for 15 users with different characteristics. Results obtained by our proposal were satisfactory from an expert's point of view.

This paper presents the mechanical synthesis of a four-bar mechanism, its definition as a constrained optimisation problem in the presence of one dynamic constraint and its solution with a swarm intelligence algorithm based on the bacteria foraging process. The algorithm is adapted to solve the optimisation problem by adding a suitable constraint-handling technique that is able to incorporate a selection criterion for the two objectives stated by the kinematic analysis of the problem. Moreover, a diversity mechanism, coupled with the attractor operator used by bacteria, is designed to favour the exploration of the search space. Four experiments are designed to validate the proposed model and to test the performance of the algorithm regarding constraint-satisfaction, sub-optimal solutions obtained, performance metrics and an analysis of the solutions based on the simulation of the four-bar mechanism. The results are compared with those provided by four algorithms found in the specialised literature used to solve mechanical design problems. On the basis of the simulation analysis, the solutions obtained by the proposed algorithm lead to a more suitable design based on motion generation and operation quality.

An Isolated Microgrid (IMG) is an electrical distribution network combined with modern information technologies aiming at reducing costs and pollution to the environment. In this article, we implement the Bacterial Foraging Optimization Algorithm (BFOA) to optimize an IMG model, which includes renewable energy sources, such as wind and solar, as well as a conventional generation unit based on diesel fuel. Two novel versions of the BFOA were implemented and tested: Two-Swim Modified BFOA (TS-MBFOA), and Normalized TS-MBFOA (NTS-MBFOA). In a first experiment, the TS-MBFOA parameters were calibrated through a set of 87 independent runs. In a second experiment, 30 independent runs of both TS-MBFOA and NTS-MBFOA were conducted to compare their performance on minimizing the IMG using the best parameter tuning. Results showed that TS-MBFOA obtained better numerical solutions compared to NTS-MBFOA and LSHADE-CV, an Evolutionary Algorithm, found in the literature. However, the best solution found by NTS-MBFOA is better from a mechatronic point of view because it favors the lifetime of the IMG, resulting in economic savings in the long term.

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.

Automatic image description, also known as image captioning, aims to describe the elements included in an image and their relationships. This task involves two research fields: computer vision and natural language processing; thus, it has received much attention in computer science. In this review paper, we follow the Kitchenham review methodology to present the most relevant approaches to image description methodologies based on deep learning. We focused on works using convolutional neural networks (CNN) to extract the characteristics of images and recurrent neural networks (RNN) for automatic sentence generation. As a result, 53 research articles using the encoder-decoder approach were selected, focusing only on supervised learning. The main contributions of this systematic review are: (i) to describe the most relevant image description papers implementing an encoder-decoder approach from 2014 to 2022 and (ii) to determine the main architectures, datasets, and metrics that have been applied to image description.