Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm further to introduce chaos-enhanced firefly algorithm with automatic parameter tuning, which results in two more variants of FA. The author first compares the performance of these algorithms, and then uses them to solve a benchmark design problem in engineering. Results obtained by other methods will be compared and analyzed.
Metaheuristic optimization algorithms are usually based on swarm intelligence,and these algorithms are often referred to as smart algorithms.We review some of the widely used algorithms for optimization,including ant and bee algorithms,bat algorithm,cuckoo search, firefly algorithm and particle swarm optimization.We also discuss the challenging issues concerning parameter tuning and parameter control in metaheuristic algorithms.
Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration. Though not all of them are efficient, a few algorithms have proved to be very effi cient and thus have become popular tools for solving real-world problems. Some algorithms are insuffici ently studied. The purpose of this review is to present a relatively comprehensive list of all the algorithms in the literature, so as to inspire further research.
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Purpose This viewpoint paper aims to introduce a novel conceptual framework for understanding knowledge sharing in the AI-HI era, arguing that conventional metaphors such as the “knowledge commons” obscure the real structural tensions in knowledge governance. It reframes the debate through the lens of the tragedy of the anticommons (TOAC) and proposes the underexplored concept of negative rivalry in use (negative-RIU) as a defining property of knowledge. Design/methodology/approach The paper adopts a critical and integrative approach, combining insights from knowledge management, transaction cost economics, intellectual property theory and Eastern dialectical thinking. It challenges the traditional application of material asset protection logic to knowledge and develops a systemic understanding of knowledge co-creation and value generation. Findings Knowledge, unlike physical goods, usually improves through use. Overprotection via fragmented intellectual property rights leads to TOAC, inhibiting innovation and collaboration. A cross-case analysis reveals that the conflict between knowledge’s negative-RIU and high excludability underpins this tragedy, while a portfolio of governance mechanisms – from strategic openness to community self-organization – can facilitate sharing. Practical implications This perspective supports a more balanced and adaptive approach to knowledge management. It provides a KM design checklist to help organizations and policymakers align legal, organizational and technological systems with the negative-RIU nature of knowledge in AI-driven environments, thereby mitigating the risks of the anticommons. Originality/value By introducing and applying the concept of negative-RIU to knowledge management, the paper offers a timely and impactful theoretical reorientation. It provides a foundational framework for revising KM practices and policies through mechanisms and principles that better harness the collaborative potential of the AI-HI integration era.
No abstract is provided for this article.
Test functions are important to validate and compare the performance of various optimization algorithms. In previous years, there have been many test or benchmark functions reported in the literature. However, there is no standard list or set of benchmark functions with diverse properties that algorithms may be tested upon. On the other hand, any new optimization algorithm should be tested by a diverse range of test or benchmark functions so as to see if it can solve certain types of problems or not. For this purpose, we compile here 140 benchmark functions for unconstrained optimization problems.
Percussive drilling is gaining interest for both shallow and deep applications due to its potential for higher drilling rates in hard rocks. Therefore, for efficient rock breaking, the development of advanced percussive drilling simulation tools has the potential to be transformative. Such tools must accurately capture the rock’s response to enable an effective analysis of the fragmentation process. Traditional continuum numerical methods, such as the finite element method (FEM), do not simulate discrete cracks or the contact interaction between rock fragments. The finite-discrete element method (FDEM) is a three-dimensional hybrid method that combines FEM with the discrete element method (DEM) that addresses these limitations. New FDEM simulation results of impacts on Kuru Grey granite show good agreement with published experimental data. The interpretation focuses on two significant processes in percussive drilling: crack propagation and chipping generation. FDEM successfully simulates the evolution of cracks, including radial, side, and inclined cracks, as well as crushed and cracked zones. The simulation also reproduces the coalescence of adjacent craters to generate more chippings. Additionally, the stress state, velocity field and discrete fractures simulated by FDEM provide detailed insights into the different fracture patterns for Kuru Grey granite, enhancing understanding of the fundamental underlying mechanisms.
This chapter introduces some of the most widely used techniques for data mining, including nearest-neighbor algorithm, k-mean algorithm, decision trees, random forests, Bayesian classifier, and others. Special techniques such as CURE and BFR for mining big data are also briefly introduced.
The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in algorithm analysis and performance evaluations. No-free-lunch theorems are of both theoretical and practical importance, while many important studies on convergence analysis of various metaheuristic algorithms have proven to be fruitful. This paper discusses the recent results on no-free-lunch theorems and algorithm convergence, as well as their important implications for algorithm development in practice. Free lunches may exist for certain types of problem. In addition, we will highlight some open problems for further research.
Thyroid cancer (TC) is the most common malignancy of the endocrine system and its incidence is gradually rising. Research has demonstrated a close link between autophagy and thyroid cancer. We constructed a prognostic model of autophagy-related long noncoding RNA (lncRNA) in thyroid cancer and explored its prognostic value. A total of 14,142 lncRNAs and 212 autophagy-related genes (ATGs) were obtained from the Cancer Genome Atlas (TCGA) database and the Human Autophagy Database (HADb), respectively. We performed lncRNA-ATGs correlation analysis and finally obtained 1166 autophagy-associated lncRNAs. Subsequently we conducted univariate Cox regression analysis and multivariate Cox regression analysis, a nine-autophagy-related lncRNAs (AC092279.1, AC096677.1, DOCK9-DT, LINC02454, AL136366.1, AC008063.1, AC004918.3, LINC02471, AL162231.2) significantly associated with prognosis was identified. Based on these autophagy-related lncRNAs, a risk model was constructed. The area under the curve (AUC) of the risk score was 0.905, proving that the accuracy of risk signature was superior. In addition, multiple regression analysis showed that risk score was a significant independent prognostic risk factor for thyroid cancer. In this study, a nine autophagy-related lncRNAs in thyroid cancer were established to predict the prognosis of thyroid cancer patients.
The aim of this article is to demonstrate that a firm may owe its continued existence to its attempts to conceal information from its competitors about the unknown characteristics of a certain factor, not just to its savings on market transaction costs, its team-working, risk-sharing or the encouragement of ex ante specific investment. This is because the existence of a firm contract severs the relationship between the factor market and the product market, thereby making it difficult for outsiders to observe the marginal contribution of the intermediate factor and make statistical inferences about the factor’s unknown characteristics. Furthermore, an optimal contract is determined by a trade-off not only between traditional risk-sharing and incentive, but also between the incentive and information concealing. Finally, we show that this latter kind of trade-off also affects the position of the optimal boundary of the firm.
When calculating the connection between pipelines, aiming at the problems of large value of loss constraint, a small value of pressure, and inaccurate water hammer effect, the hydraulic calculation, and water hammer analysis process of large drop and long-distance municipal water transmission and distribution system are designed. The triangular curve grid processing pipeline is set as a grid structure from sparse to dense, and the normal imaging numerical relationship is used to control the pipeline constraint conditions Then, a long-distance water transmission and distribution system is set as a grid structure, and the large drop condition is defined. After the elevation value of the pipe centerline is calibrated by using the large drop hydraulic calculation algorithm, the numerical relationship of the water hammer effect is constructed to complete the process analysis. The test results show that the hydraulic calculation error is controlled at about 0.07%, the water level error is controlled at 0.1cm ~ 2cm, and the difference between the pressure value and the measured standard value is less than 2N.
