In this paper, an improved harmony search (ImHS) algorithm is presented. HS is a simple but efficient metaheuristic method explored in recent literature, that simulates the process of musical improvisation. Two modifications for parameter tuning are proposed to enhance the algorithm performance in the solution of constrained numerical optimization problems, maintaining the simplicity of its original design. Metaheuristics are methods for solving optimization problems, and are based in two processes: exploration (diversification) and exploitation (intensification). The proposed modifications improve both processes in HS, without breaking their balance. A well-known ideal problem set was used as a reference to compare the efficiency of the developed algorithm ImHS with HS and three of its most successful variants, and also with two other metaheuristics of different nature, artificial bee colony (ABC) and modified ABC (MABC). Various techniques were applied to evaluate the algorithm performance with the proposed modifications, in order to validate the reliability of the comparison. In most case studies, ImHS far surpassed the results of HS and ABC, also improving the performance of the selected variants. Additionally, its results reached a similar quality than the obtained with MABC but with a significantly lower computational cost, suggesting that it can be a useful tool for solving real-world optimization problems if they are modeled as constrained numerical cases.

In this paper, a method for image encryption is proposed based on a novel approach using chaos and transcendental numbers for generating the substitution box and the ciphering keys. The Chaotic Pi Ciphering (CPC) is a robust symmetric cryptosystem of the substitution-permutation-network type, that ciphers images in ten rounds. The objective of CPC is to avoid some general weakness points in cryptosystems, such as the vulnerability to certain types of attacks or the encryption with data loss. The number of rounds can be increased up to 14, with an even further increment of the ciphering security. An 8×8 substitution box is constructed using chaos, and its value of the Differential Power Analysis is lower than the corresponding to the Advanced Encryption Standard (AES). It makes CPC more resistant to differential and linear attacks than AES. CPC has approximately 2 ⁵¹² keys, presenting a high resistance to brute-force attacks. Chaos is produced by a nonlinear differential equation, and the Walsh function is applied to measure the nonlinearity of the box. The transcendental number pi and a positive integer are employed to generate the schedule keys, and the key length is equal to the image size. The randomness of the encrypted images is evaluated with four tests: entropy, correlation, Discrete Fourier Transform, and a goodness-of-fit test using the chi square distribution χ ² . CPC was tested using well-known images from the related literature, and its performance was compared to the corresponding to AES. The results show a good entropy and a high randomness, indicating a high-quality ciphering.

Although different physiological signals such as Electrooculography (EOG) have been widely used in the control of assistance systems for people with disabilities, customizing the signal classification system remains a challenge. In most interfaces, the user must adapt to the classification parameters, although ideally, the systems must adapt to the user parameters. Therefore, in this work is presented the use of a Multilayer Neural Network (MNN) to model the EOG signal as a mathematical function, which is optimized using genetic algorithms, in order to obtain the maximum and minimum amplitude threshold of the EOG signal of each person that uses the designed interface. The problem of the variation of the voltage threshold of the physiological signals is addressed by means of an intelligent calibration, if an assistance system is not calibrated it loses functionality. Artificial intelligence techniques such as machine learning and fuzzy logic are used for classification of the EOG signal, but they need calibration parameters that are obtained through databases generated through prior user training, depending on the effectiveness of the algorithm, the learning curve and the response time of the system. In this work, by optimizing the parameters of the EOG signal, the classification is customized and the domain time of the system is reduced without the need for a database and the training time of the user is minimized, significantly reducing the time of the learning curve. The results are implemented in an HMI for the generation of points in a Cartesian space ( , , ) in order to control a manipulator robot that follows a desired trajectory by means of the movement of the user's eyeball.

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.

In this paper, a graph-theory-based approach for representing planar mechanisms is presented, the Santiago Portilla method (SPM). From the corresponding adjacency matrix, SPM generates an extended matrix containing the complete characterization of a planar mechanism, including all the information about both topology and geometry. This matrix representation can be used for the optimal design of mechanisms, allowing simultaneously the topological and dimensional synthesis by means of computational tools such as the metaheuristic algorithms. A case study corresponding to the design of a fixed-linear-trajectory tracker mechanism is included in order to test the efficiency of the proposed approach. It was carried out by addressing the design as an optimization problem and solving it with the differential evolution algorithm, representing the individuals in its population by the matrix form generated by the SPM. The results of the case study show that the SPM and its matrix representation constitute a useful and flexible tool for the solution of the real engineering problems involving the design of planar mechanisms.

Nowadays, industrial robot applications are required to customize the manufacturing of diverse products to reduce both downtime and standoff variability. Two methods for robot programming are regularly implemented to carry out that goal. The first one, online programming, requires a specialized operator to guide the robot through desired poses, and the quality of the result is directly limited by his skill level. On the other side, off-line programming uses software packaging to simulate robot applications before their implementation. It reduces downtime with respect to online programming but requires additional calibration steps. In this paper, a novel procedure is presented to obtain accurate surface approximations by combining linear interpolations generated during online programming with a triangulated surface reconstruction of a workpiece surface representation. The method uses a point cloud instead of a predefined mesh to reduce the standoff variability between the robotic tool center point and the surface. Additionally, a technique based on a penalized least squares method was implemented to smooth the trajectory, including position and orientation. The proposed methodology was validated with three well-known case studies involving real trajectories, with simulations in Matlab® and RobotStudio®, as well as by experimentation with an industrial ABB robot. The quality of the results demonstrates a great efficiency of this method for path generation based on surface reconstruction.