752 publications from this institution
All design problems in telecommunications can be formulated as optimization problems, and thus may be tackled by some optimization techniques. However, these problems can be extremely challenging due to the stringent time requirements, complex constraints, and a high number of design parameters. Solution methods tend to use conventional methods such as Lagrangian duality and fractional programming in combination with numerical solvers, while new trends tend to use evolutionary algorithms and swarm intelligence. This chapter provides a summary review of the bio-inspired optimization algorithms and their applications in telecommunications. We also discuss key issues in optimization and some active topics for further research.
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Complex numbers can be useful in solving many engineering problems such as linear circuits, mechanical vibrations, signal processing and image processing. This chapter introduces the fundamentals of complex numbers and complex functions.
A multispecies artificial ecosystem is formulated using cellular automata with species interactions and food chain hierarchy. The constructed finite state automaton can simulate the complexity and self-organized characteristics of the evolving multispecies living ecosystems. Simulations show that a small perturbation or extinction event may affect many other species in the ecosystem in an avalanche manner. Both the avalanches and the extinction arising from these changes follow a power law, reflecting that the multispecies living ecosytems have the characteristics of self-organized criticality.
Catalytic reactions with inhibition and cooperativity are common in chemical engineering, processing of engineering materials and biological systems. A theoretical model for competitive and cooperative activity in enzyme reactions is formulated in terms of a pair of coupled nonlinear reaction–diffusion equations. A new stochastic cellular automaton is then constructed to simulate this nonlinear system. Numerical simulations show stable two- and three-dimensional Turing pattern formation, and complex patterns have the interesting feature of self-organized criticality.
The popularization of bridge employment is conducive to the realization of active aging, which requires not only the subjective initiative of retirees but also the active cooperation of local governments and relevant enterprises. This study combined evolutionary game theory with system dynamics to model and simulate the behavior of local government and enterprises on bridge employment, aiming to highlight the importance of government behavior and enterprise actions in the process of active aging and to analyze the effectiveness of different incentive and punishment mechanisms in promoting bridge employment. Results show that the system with dynamic incentive and punishment is easier to keep stable than the system with static incentive and punishment; specifically, the dynamic mechanism with low incentive intensity and high punishment intensity is better than other mechanisms. Finally, this study emphasized the importance of government policy to bridge employment and put forward relevant management implications.
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A recently developed metaheuristic optimization algorithm, firefly algorithm (FA), mimics the social behavior of fireflies based on the flashing and attraction characteristics of fireflies. In the present study, we will introduce chaos into FA so as to increase its global search mobility for robust global optimization. Detailed studies are carried out on benchmark problems with different chaotic maps. Here, 12 different chaotic maps are utilized to tune the attractive movement of the fireflies in the algorithm. The results show that some chaotic FAs can clearly outperform the standard FA.
By analyzing the similarity of a self-organizing system and an optimization process, we highlight that optimization can be considered as self-organization. We analyze the characteristics of some popular met heuristic algorithms such as firefly algorithm and cuckoo search for applications in self-organizing systems.
The effectiveness of enterprise human resource management is an important topic of human resource management in academic studies, and it is the key for enterprises to achieve long-term sustainable development. Up to now, fruitful research achievements have been achieved, but there is still a lack of a systematic literature review. Based on 218 research results collected in the CNKI and SSCI databases of the WOS from 1975 to 2021, this paper systematically reviews the enterprise human resource management effectiveness from the aspects of evolution, topic overview, and content sorting by using bibliometrics with Citespace software and a systematic literature review. This paper expounds the concept of enterprise human resource management effectiveness, analyzes the existing research results, and finds out where the research gap is. Based on this, this paper puts forward the prospect of future research topics from the aspects of measurement methods, influencing factors, and results, which not only helps to systematically reveal the overview of the existing research in the field of enterprise human resource management effectiveness, but also provides direction for further research.
The tragedy of the commons refers to the overuse of resources which are rival in consumption but lack excludability and it also refers to rent dissipation. While the tragedy of the anticommons is a tragedy closely connected with underuse of resources that are rival in consumption and with too strong excludability. The prior studies proved that the tragedy of the commons and the tragedy of the anticommons are symmetric from the perspective of pure mathematics, especially the game theory, which was later refuted by behavioral economics experiments. According to them, the tragedy of the anticommons is severer than the tragedy of the commons. The asymmetry of the tragedy of the commons and the tragedy of the anticommons is a paradox by these different research methods. This paradox shows that there are imperfections in the completely rational economic man hypothesis set up by neoclassical economics. As a fundamental theory, the tragedy of the commons is quite influential in many disciplines, such as microeconomics, public sector economics, ecological economics, environmental economics, management, sociology, property law, and political science. And the tragedy of the anticommons theory has also opened its door of both theoretical research and practical implications since its acceptance by Nobel laureate Buchanan, the main founder of public choice school. Only when theoretical issues are thoroughly discussed and made clear enough, can people avoid misunderstanding or misusing the commons theory. Thus, it is necessary to elucidate the paradox between them. Based on Simon's bounded rationality, Kahneman and Tversky's prospect theory, value function, Thaler's mental accounting, endowment effect, and other cognitive psychological tools, this study clearly shows that agents' decision-making process is not just based on the long-believed marginal benefit and marginal cost analysis advocated by traditional neoclassical economists. Agents' decision-making is a process in which agents selectively absorb, code the objective marginal revenue and marginal cost, and feed relevant information to their brain. Therefore, what plays a directly decisive role is not the objective marginal revenue and marginal cost per se, but the mentally perceived subjective utility of marginal revenue and marginal cost by the human brain. Followed by this research clue, the paradox between the tragedy of the commons and the tragedy of the anticommons is elucidated from the perspective of cognitive psychology.
Many problems in science and engineering can be formulated as optimization problems, subject to complex, nonlinear constraints. The solutions of optimization problems often require sophisticated optimization techniques. Traditional algorithms may struggle to deal with such highly nonlinear problems. Nature-inspired algorithms can be good alternatives, and they are flexible and efficient for solving problems in optimization, data mining and machine learning. This chapter introduces the fundamentals of algorithms, classification of optimization problems and algorithms as well as a brief history of metaheuristics.
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In this paper, a Markov model of finite neuronal assembly activity is presented, Poisson approximation is considered, generating function of probability distribution is introduced, so that the Poisson approximation error is studied.
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In this article, we present nature-inspired techniques for automatic image registration of multi-temporal satellite images. Multi-temporal satellite image registration is becoming increasingly important to aid in flood damage assessment. We consider two images in the registration process: one before-flood image and another during-flood image. The objective is to maximise the similarity metric (of these two images) using information theoretic measures such as mutual information (MI). The maximum MI would imply that the images are better registered. The function of these metrics for transformation parameters is generally non-convex and irregular and, therefore, makes it difficult to use standard optimisation methods for the global solution. In this study, nature-inspired techniques – genetic algorithm (GA), particle swarm optimisation (PSO) and firefly algorithm (FA) are used to search for the maximum MI. The multi-temporal images – Linear Imaging Self-Scanning Sensor III (LISS III) image (before flood) and...
