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.

Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high-dimensional transaction sets. Traditional and classical algorithms such as the Apriori and FP-Growth algorithms, as well as their derivative algorithms, are unacceptable in practical big data analysis in an explosive solution space because of their huge consumption of storage space and running time. A multi-objective optimization algorithm was proposed to mine the frequent itemset of high-dimensional data. First, all frequent 2-itemsets were generated by scanning transaction sets based on which new items were added in as the objects of population evolution. Algorithms aim to search for the maximal frequent itemset to gather more non-void subsets because non-void subsets of frequent itemsets are all properties of frequent itemsets. During the operation of algorithms, lethal gene fragments in individuals were recorded and eliminated so that individuals may resurge. Finally, the set of the Pareto optimal solution of the frequent itemset was gained. All non-void subsets of these solutions were frequent itemsets, and all supersets are non-frequent itemsets. Finally, the practicability and validity of the proposed algorithm in big data were proven by experiments.

Virulence effector SidJ evolution in Legionella pneumophila is driven by positive selection and intragenic recombination

Effector proteins translocated by the Dot/Icm type IV secretion system determine the virulence of Legionella pneumophila (L. pneumophila). Among these effectors, members of the SidE family (SidEs) regulate several cellular processes through a unique phosphoribosyl ubiquitination mechanism mediated by another effector, SidJ. Host-cell calmodulin (CaM) activates SidJ to glutamylate the SidEs of ubiquitin (Ub) ligases and to make a balanced Ub ligase activity. Given the central role of SidJ in this regulatory process, studying the nature of evolution of sidJ is important to understand the virulence of L. pneumophila and the interaction between the bacteria and its hosts. By studying sidJ from a large number of L. pneumophila strains and using various molecular evolution algorithms, we demonstrated that intragenic recombination drove the evolution of sidJ and contributed to sidJ diversification. Additionally, we showed that four codons of sidJ which are located in the N-terminal (NTD) (codons 58 and 200) and C-terminal (CTD) (codons 868 and 869) domains, but not in the kinase domain (KD) had been subjected to strong positive selection pressure, and variable mutation profiles of these codons were identified. Protein structural modeling of SidJ provided possible explanations for these mutations. Codons 868 and 869 mutations might engage in regulating the interactions of SidJ with CaM through hydrogen bonds and affect the CaM docking to SidJ. Mutation in codon 58 of SidJ might affect the distribution of main-chain atoms that are associated with the interaction with CaM. In contrast, mutations in codon 200 might influence the α-helix stability in the NTD. These mutations might be important to balance Ub ligase activity for different L. pneumophila hosts. This study first reported that intragenic recombination and positive Darwinian selection both shaped the genetic plasticity of sidJ, contributing to a deeper understanding of the adaptive mechanisms of this intracellular bacterium to different hosts.

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.

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Traditional evolution algorithms tend to start the search from scratch. However, real-world problems seldom exist in isolation and humans effectively manage and execute multiple tasks at the same time. Inspired by this concept, the paradigm of multi-task evolutionary computation (MTEC) has recently emerged as an effective means of facilitating implicit or explicit knowledge transfer across optimization tasks, thereby potentially accelerating convergence and improving the quality of solutions for multi-task optimization problems. An increasing number of works have thus been proposed since 2016. The authors collect the abundant specialized literature related to this novel optimization paradigm that was published in the past five years. The quantity of papers, the nationality of authors, and the important professional publications are analyzed by a statistical method. As a survey on state-of-the-art of research on this topic, this review article covers basic concepts, theoretical foundation, basic implementation approaches of MTEC, related extension issues of MTEC, and typical application fields in science and engineering. In particular, several approaches of chromosome encoding and decoding, intro-population reproduction, inter-population reproduction, and evaluation and selection are reviewed when developing an effective MTEC algorithm. A number of open challenges to date, along with promising directions that can be undertaken to help move it forward in the future, are also discussed according to the current state. The principal purpose is to provide a comprehensive review and examination of MTEC for researchers in this community, as well as promote more practitioners working in the related fields to be involved in this fascinating territory.