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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.

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.

There are problems that are difficult to solve through mathematical programming or by classical methods. These problems are called hard problems due to their high complexity or high dimension. On the other hand, mataheuristics intends to seek a better solution to a problem. The Improvement Harmony Search algorithm is proposed under modification of the bandwidth parameter increasing the quality of the exploitation of the solutions. That is why within the state of the art, are mentioned several versions of harmonic search. The state of the art is supports the fact that the algorithm belongs to the category of those who make modifications to its parameters. This research demonstrates the ability of ImHS to solve a problem of high complexity focused on solving four-bar mechanism designs, whose solutions imply high dimension and which are also classified as hard problems. The two problems that are solved in this investigation, are problems very attacked within the state of the art by various metaheuristics. A comparison is then made against previous solutions with traditional metaheuristics and other versions of harmony search algorithm. Finally, the effectiveness of performance is demonstrated, where proposed algorithm it exceeded five metaheuristic algorithms and five harmony search versions. An optimum is provided in an easy and useful way, the parametric statistics are improved and the number of feasible solutions is exceeded in NPhard problems as in the case of problems with four-bar mechanisms.INDEX TERMS algorithms performance, four-bar mechanism, improved harmony search, optimization problems, mechatronic.

Abstract-In this paper the variation of the velocity error of a four-bar mechanism with spring and damping forces is reduced by solving a dynamic optimization problem using a differential evolution algorithm with a constraint handling mechanism. The optimal design of the velocity control for the mechanism is formulated as a dynamic optimization problem. Moreover, in order to compare the results of the differential evolution algorithm, a simulation experiment of the proposed control strategy was carried out. The simulation results and discussion are presented in order to evaluate the performance of both approaches in the control of the mechanism.