752 publications from this institution
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. Numerical experiments 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.
Metaheuristic algorithms have become powerful tools for modeling and optimization. This chapter provides an overview of nature-inspired metaheuristic algorithms, especially those developed in the last two decades, and their applications. We will briefly introduce algorithms such as genetic algorithms, differential evolution, genetic programming, fuzzy logic, and most importantly, swarm-intelligence-based algorithms such as ant and bee algorithms, particle swarm optimization, cuckoo search, firefly algorithm, bat algorithm, and krill herd algorithm. We also briefly describe the main characteristics of these algorithms and outline some recent applications of these algorithms.KeywordsNature-inspired algorithm; metaheuristic algorithms; modeling; optimization.
This technical note reports the results of a set of tests of software toolboxes for optimisation and uncertainty evaluation using finite element models.
Simulated annealing (SA) is a trajectory-based, random search technique for global optimization. It mimics the annealing process in materials processing when a metal cools and freezes into a crystalline state with minimum energy and larger crystal sizes so as to reduce the defects in metallic structures. The annealing process involves the careful control of temperature and its cooling schedule. SA has been successfully applied in many areas.
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The increasing demand of high-speed and secure wireless broadband networks has generated significant interests in the optimisation and the analysis of energy-efficiency in orthogonal frequency division multiple access (OFDMA) system. In this study, the authors present an in-depth mathematical analysis of the maximisation of energy-efficiency by taking into consideration the quality of service (QoS). By using optimality conditions, they have shown that the optimal solutions can be obtained analytically. Furthermore, it has been proved in this study that this optimisation problem is strictly concave with the existence of a global maximum. Case studies with multiple subchannels validate the consistency of numerical results with the results obtained from derivative-free method like genetic algorithm. Graphical illustrations also validate and confirm the numerical values obtained from the mathematical analysis. Therefore the solution is optimal with respect to the OFDMA model adopted in this study. The authors proposed approach can be used for practical applications because of its simplicity and efficacy with QoS guaranteed for efficient energy consumption.
A new metaheuristic optimisation algorithm, called Cuckoo Search (CS), was developed recently by Yang and Deb (2009).This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions.We then apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures.The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser.We will discuss the unique search features used in CS and the implications for further research.
Analysis of optimization algorithms can be carried out from different perspectives. This chapter first highlights the essence of optimization algorithms, followed by the brief introduction of some commonly used nature-inspired algorithms. Then, parameter tuning and parameter control are discussed in the context of algorithm analysis and performance.
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