All mathematical models and differential equations we have discussed so far are deterministic systems in the sense that, for given initial and boundary conditions, the solutions of the system can be determined. There is no intrinsic randomness in differential equations. In reality, randomness occurs everywhere, and not all models are deterministic. In fact, it is necessary to use stochastic models and sometimes the only sensible models are stochastic descriptions. In these cases, we have to deal with probability and statistics.
All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization methods on the convergence and accuracy of five optimizers: differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), artificial bee colony (ABC) and genetic algorithm (GA). We have used 19 different test functions with different properties and modalities to compare the possible effects of initialization, population sizes and the numbers of iterations. Rigorous statistical ranking tests indicate that 43.37% of the functions using the DE algorithm show significant differences for different initialization methods, while 73.68% of the functions using both PSO and CS algorithms are significantly affected by different initialization methods. The simulations show that DE is less sensitive to initialization, while both PSO and CS are more sensitive to initialization. In addition, under the condition of the same maximum number of fitness evaluations (FEs), the population size can also have a strong effect. Particle swarm optimization usually requires a larger population, while the cuckoo search needs only a small population size. Differential evolution depends more heavily on the number of iterations, a relatively small population with more iterations can lead to better results. Furthermore, ABC is more sensitive to initialization, while such initialization has little effect on GA. Some probability distributions such as the beta distribution, exponential distribution and Rayleigh distribution can usually lead to better performance. The implications of this study and further research topics are also discussed in detail.
No abstract is provided for this article.
No abstract is provided for this article.
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.
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.
Computational optimization is becoming increasingly important in engineering design and industrial applications. Products and services are often concerned with the maximization of profits and reduction of cost, but also aim at being more energy-efficient, environment-friendly and safety-ensured; at the same time they are limited by resources, time and money. This second workshop on Computational Optimization, Modelling and Simulation (COMS 2011) at ICCS 2011 will further summarize the latest developments of optimization and modelling and their applications in science, engineering and industry.
Software testing is an important but complex part of software development life cycle. The optimization of the software testing process is a major challenge, and the generation of the independent test paths remains unsatisfactory. In this paper, we present an approach based on metaheuristic firefly algorithm to generate optimal test paths. In order to optimize the test case paths, we use a modified firefly algorithm by defining appropriate objective function and introducing guidance matrix in traversing the graph. Our simulations and comparison show that the test paths generated are critical and optimal paths.
Cunninghamia lanceolate (Lambert.) Hooker is one of the main fast-growing timber forest species in southern China which has a long history of cultivation and spreads across 28 provinces, cities, and regions. Recently, a variant of fir was discovered in the Xiaoxi National Nature Reserve in Hunan Province. The heartwood is hard as iron and its ratio is more than 80%, with the especial character of anti-corruption. It is a natural germplasm resource, called Iron-heart Cunninghamia lanceolate. Study on it is still in the stage of data accumulation. In this paper, we studied it from three points as follows: (1) Plus tree selection and construction of germplasm resources nursery. (2) Study on cone and seed quality. (3) Genetic structure analysis of natural population. The research of Iron-heart Cunninghamia lanceolate lays a theoretical foundation for the protection, development, and utilization of the black-heart wood germplasm resources of Iron-heart Cunninghamia lanceolate in the future.
The interaction of a shock wave with an existing fast flame (convection–reaction driven) is considered whereby the driving piston is not at a constant speed. Between the shock and the flame, there is an induction zone which governs the acoustic coupling between the flame and the shock. The asymptotic matching of this zone to the regions near the flame is presented. The case of a variable piston speed is allowed so that pulsed transient inputs to fast deflagrations can be simulated.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
Engineering optimisation is typically multi-objective and multidisciplinary with complex constraints, and the solution of such complex problems requires efficient optimisation algorithms. Recently, Xin-She Yang proposed a bat-inspired algorithm for solving non-linear, global optimisation problems. In this paper, we extend this algorithm to solve multi-objective optimisation problems. The proposed multi-objective bat algorithm (MOBA) is first validated against a subset of test functions, and then applied to solve multi-objective design problems such as welded beam design. Simulation results suggest that the proposed algorithm works efficiently.
