Analyzing metaheuristic methods to build an optimal composite foot prosthesis insole

This paper uses metaheuristic algorithms to develop and optimize composite materials. To calculate the characteristics that allow the planned item to withstand particular loads, ABC and Differential Evolution algorithms are utilized. One of the key challenges in these designs is determining the piece's thickness. Designing a carbon fibre insole for Latin American users will improve the design process by generating functional solutions that are feasible to manufacture and in less time than traditional design methods. The results reported in this work demonstrate that a functional design may be developed, validated by finite element method, with minimal material waste and in a reasonable period.

Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution for Optimal Design of Fuzzy Controllers

This article mainly focuses on the utilization of shadowed type-2 fuzzy systems used to achieve the goal of dynamically adapting the parameters of two already known algorithms in the literature: the harmony search and the differential evolution algorithms. It has already been established that type-2 fuzzy logic enhances the performance of metaheuristics by enabling parameter adaptation; however, the utilization of fuzzy logic results in an increased execution time. For this reason, in this article, the shadowed type-2 fuzzy approach is put forward as a way of reducing execution time, while maintaining the good results that the complete type-2 fuzzy model produces. The harmony search and differential evolution algorithms with shadowed type-2 parameter adaptations were applied to the problem of optimally designing fuzzy controllers. The simulations were performed with the controllers working in an ideal situation, and then with a real situation under different noise levels in order to reach a conclusion regarding the performance of each of the algorithms that were applied.

Ranking Methods for Multicriteria Decision-Making: Application to Benchmarking of Solvers and Problems

Evaluating the performance assessments of solvers (e.g., for computation programs), known as the solver benchmarking problem, has become a topic of intense study, and various approaches have been discussed in the literature. Such a variety of approaches exist because a benchmark problem is essentially a multicriteria problem. In particular, the appropriate multicriteria decision-making problem can correspond naturally to each benchmark problem and vice versa. In this study, to solve the solver benchmarking problem, we apply the ranking-theory method recently proposed for solving multicriteria decision-making problems. The benchmarking problem of differential evolution algorithms was considered for a case study to illustrate the ability of the proposed method. This problem was solved using ranking methods from different areas of origin. The comparisons revealed that the proposed method is competitive and can be successfully used to solve benchmarking problems and obtain relevant engineering decisions. This study can help practitioners and researchers use multicriteria decision-making approaches for benchmarking problems in different areas, particularly software benchmarking.

Use Improved Differential Evolution Algorithms to Handle the Inverse Kinetics Problem for Robots with Residual Degrees of Freedom

In this study, the Self-adaptive strategy algorithm for controlling parameters in Differential Evolution algorithm (ISADE) improved from the Differential Evolution (DE) algorithm, as well as the upgraded version of the algorithms has been applied to solve the Inverse Kinetics (IK) problem for the redundant robot with 7 Degree of Freedom (DoF). The results were compared with 4 other algorithms of DE and Particle Swarm Optimization (PSO) as well as Pro-DE and Pro-PSO algorithms. These algorithms are tested in three different Scenarios for the motion trajectory of the end effector of in the workspace. In the first scenario, the IK results for a single point were obtained. 100 points randomly generated in the robot’s workspace was input parameters for Scenario 2, while Scenario 3 used 100 points located on a spline in the robot workspace. The algorithms were compared with each other based on the following criteria: execution time, endpoint distance error, number of generations required and especially quality of the joints’ variable found. The comparison results showed 2 main points: firstly, the ISADE algorithm gave much better results than the other DE and PSO algorithms based on the criteria of execution time, endpoint accuracy and generation number required. The second point is that when applying Pro-ISADE, Pro-DE and Pro-PSO algorithms, in addition to the ability to significantly improve the above parameters compared to the ISADE, DE and PSO algorithms, it also ensures the quality of solved joints’ values.