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To cope with increasingly complex models of engineering design problems and to obtain more accurate design solutions, this paper proposed an improved population-based, bio-inspired optimization algorithm, called the pity beetle algorithm based on pheromone dispersion model (PBA-PDM). PBA-PDM enables a local and global search for optimization problems through the pheromone release mechanisms in female beetles and the interaction relationship between male beetles. The experimental results compared with other state-of-the-art metaheuristic optimization algorithms show that PBA-PDM has an ideal performance when dealing with both classical test functions and CEC2017 benchmark test functions. Then, the PBA-PDM is applied in dealing with real-world constrained engineering design problems to verify the effectiveness and applicability. The above experimental results show that the PBA-PDM proposed in this paper is an effective and efficient algorithm for solving real-world optimization problems. [ABSTRACT FROM AUTHOR]

The Internet of Things is widely used, which results in the collection of enormous amounts of data with numerous redundant, irrelevant, and noisy features. In addition, many of these features need to be managed. Consequently, developing an effective feature selection (FS) strategy becomes a difficult goal. Many FS techniques, based on bioinspired metaheuristic methods, have been developed to tackle this problem. However, these methods still suffer from limitations; so, in this paper, we developed an alternative FS technique, based on integrating operators of the chameleon swarm algorithm (Cham) with the quantum-based optimization (QBO) technique. With the use of eighteen datasets from various real-world applications, we proposed that QCham is investigated and compared to well-known FS methods. The comparisons demonstrate the benefits of including a QBO operator in the Cham because the proposed QCham can efficiently and accurately detect the most crucial features. Whereas the QCham achieves nearly 92.6%, with CPU time(s) nearly 1.7 overall the tested datasets. This indicates the advantages of QCham among comparative algorithms and high efficiency of integrating the QBO with the operators of Cham algorithm that used to enhance the process of balancing between exploration and exploitation. [ABSTRACT FROM AUTHOR]

In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel population-based bio-inspired meta-heuristic optimization algorithm, called Blood Coagulation Algorithm (BCA). BCA derives inspiration from the process of blood coagulation in the human body. The underlying concepts and ideas behind the proposed algorithm are the cooperative behavior of thrombocytes and their intelligent strategy of clot formation. These behaviors are modeled and utilized to underscore intensification and diversification in a given search space. A comparison with various state-of-the-art meta-heuristic algorithms over a test suite of 23 renowned benchmark functions reflects the efficiency of BCA. An extensive investigation is conducted to analyze the performance, convergence behavior and computational complexity of BCA. The comparative study and statistical test analysis demonstrate that BCA offers very competitive and statistically significant results compared to other eminent meta-heuristic algorithms. Experimental results also show the consistent performance of BCA in high dimensional search spaces. Furthermore, we demonstrate the applicability of BCA on real-world applications by solving several real-life engineering problems. [ABSTRACT FROM AUTHOR]