No abstract is provided for this article.
Biology-derived algorithms are an important part of computational sciences, which are essential to many scientific disciplines and engineering applications. Many computational methods are derived from or based on the analogy to natural evolution and biological activities, and these biologically inspired computations include genetic algorithms, neural networks, cellular automata, and other algorithms.
Simulated annealing is one of the earliest and yet widely used nature-inspired algorithms. This chapter introduces its main search mechanism, parameter setting, and implementation.
Almost all real-world problems have constraints. Constraint-handling is an important part of algorithm implementation in optimization. This chapter introduces the main techniques for handling constraints.
No abstract is provided for this article.
No abstract is provided for this article.
It is a great pleasure to welcome you to the 27th International Conference on CADCAM, Robotics and Factories of the Future, sponsored by the International Society for Productivity Enhancement, Middlesex University, Festo Limited GB, National Instruments UK & Ireland, the Sector Skills Council for Science, Engineering and Manufacturing Technologies and our proceedings publisher Institute of Physics Publications.
and science.In numerous fields, including mechanical engineering, civil engineering, electrical engineering, structural and aerospace engineering, automotive industry, oil industry, chemical engineering, ocean science and climate research to name just a few, simulation plays a critical role not only for verification purposes, but, more importantly in the design process itself.The complexity of structures and systems makes it analytically intractable, and it is thus extremely time-consuming and challenging to carry out any realistic design tasks, and in many cases, it is almost impossible to achieve any sensible design solutions under stringent constraints.These challenging tasks can be to optimally adjust the geometry and/or material parameters so that the system meets given performance requirements, or to calibrate the model parameters to make it fit given measurements, or to generate the optimal paths/routes for scheduling and planning tasks.In most cases, the interactions can be highly complex and multifold, and it is not easy or possible to isolate the processes of interest in the simplest, solvable form.For example, in the design of an electronic device, it is not just the isolated device to be designed that needs to be considered but also its -sometimes complex -interactions with the environment that affect the device's performance.On the other hand, using accurate, realistic simulations allows the engineers to avoid costly prototyping and to realize the design closure with numerical models rather than through physical system measurements and prototype re-building.Furthermore, accurate simulations make it possible to analyze phenomena that could not be captured using simplistic theoretical models or too expensive or too time-consuming to be investigated through physical measurements.While high-fidelity numerical models can be very accurate, they tend to be computationally expensive.Simulation times of several hours, days, or weeks are not uncommon.In many cases, it may be a highly challenging task to just set up the model that takes into account all main, relevant system components and their interactions.One of the consequences is that a direct use of high-fidelity simulations in the optimization process may be prohibitive.The presence of massive computing resources is not always translated into computational speedup in practice, which is due to a growing demand for simulation
Modern metaheuristic algorithms are in general suited for global optimization. This paper combines the recently developed eagle strategy algorithm with differential evolution. The new algorithm, denoted as the ES–DE, is implemented by interfacing SAP2000 structural analysis code and MATLAB mathematical software. The performance of the ES–DE is evaluated by solving four benchmark problems where the objective is to minimize the weight of steel frames. The optimized designs obtained by the proposed algorithm are better than those found by the standard differential evolution algorithm and also very competitive with literature. The overall convergence behavior is significantly enhanced by the hybrid optimization strategy.
Fourier series and fast Fourier transforms have important applications in signal and image processing. This chapter first introduces Fourier series and then Fourier transforms.
No abstract is provided for this article.
No abstract is provided for this article.
Particle swarm optimization (PSO) was developed by Kennedy and Eberhart in 1995 based on the swarm behavior, such as fish and bird schooling in nature, which has generated much interest in the ever-expanding area of swarm intelligence. There are over two dozen PSO variants, and hybridization with other algorithms has also been investigated. This chapter reviews the basic ideas of particle swarm optimization, some common variants, and convergence properties.
Test functions are important to validate and compare the performance of optimization algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and tt can be expected this complete set of functions can be used for validation of new optimization in the future.
Injectable Hydrogels In article number 2206554, Lin Yu and co-workers develop an injectable and thermosensitive hydrogel with inherent long-acting NO-releasing capacity. The hydrogel system loaded with β-lapachone and CuCys nanoparticles can synchronously boost intracellular reactive oxygen species and reactive nitrogen species levels, thereby synergistically enhancing the anti-tumor efficacy. This hydrogel is suitable as a platform for extending NO-associated medical applications.
In the mathematical modelling of sediment compaction and porous media flow, the rheological behaviour of sediments is typically modelled in terms of a nonlinear relationship between effective pressure $p_e$ and porosity $\phi$, that is $p_e=p_e(\phi)$. The compaction law is essentially a poroelastic one. However, viscous compaction due to pressure solution becomes important at larger depths and causes this relationship to become more akin to a viscous rheology. A generalised viscoelastic compaction model of Maxwell type is formulated, and different styles of nonlinear behaviour are asymptotically analysed and compared in this paper.
Most global optimization problems are nonlinear and thus difficult to solve, and they become even more challenging when uncertainties are present in objective functions and constraints. This paper provides a new two-stage hybrid search method, called Eagle Strategy, for stochastic optimization. This strategy intends to combine the random search using L\'evy walk with the firefly algorithm in an iterative manner. Numerical studies and results suggest that the proposed Eagle Strategy is very efficient for stochastic optimization. Finally practical implications and potential topics for further research will be discussed.
