China
Africa
Explore chapters and articles related to this topic
Harmony Search
Published in Nazmul Siddique, Hojjat Adeli, Nature-Inspired Computing, 2017
The aim of any musical performance is to discover some kind of pleasing harmony, which is considered as a perfect state of combination of different musical instruments determined by an aesthetic quality. It is the same process that an optimization looks for a perfect combination of values of different decision variables determined by an optimality criterion leading to a global optimal solution. The estimation of aesthetic quality of a musical performance is very subjective and leads to the difficulty of being exactly defined by a set of mathematical equations or any kind of quantitative description. Practically, the aesthetic quality of a musical instrument is essentially determined by its pitch (or frequency), timbre (or sound quality), and amplitude (or loudness). Timbre is largely determined by the harmonic content that is in turn determined by the waveforms or modulations of the sound signal. However, the harmonics that it can generate will largely depend on the pitch or frequency range of the particular instrument (Lee and Geem, 2005).
Systems Thinking
Published in David G. Carmichael, Project Management Framework, 2005
Input is equivalent to a project management decision. The term ‘control’ is favoured here in place of both input, decision and design variable. The project manager exerts control on the project in order to bring about a desired project behaviour. The most obvious controls are resource (people, materials, equipment, …) selections. Resources may be freely selected by the project manager, subject to any constraints being present. Where an objective/optimality criterion exists (or objectives/criteria exist), they are selected to extremise this objective.
Introduction
Published in Franklin Y. Cheng, Kevin Z. Truman, Structural Optimization, 2017
Franklin Y. Cheng, Kevin Z. Truman
Chapter 10: This chapter covers topological design, pile foundations, damage detection and structural identification. In this chapter an amalgamation of examples where the optimality-criteria techniques presented in Chapter 8 have been modified to solve several non-traditional structural problems are presented. The unique quality of using optimality-criteria techniques is that they can be modified to accommodate the types of, and relationship between, the design variables, the analysis methods, the constraints, and the objective for the optimization.
A multi-objective design optimisation of eco-friendly aircraft: the impact of noise fees on airplanes sustainable development
Published in International Journal of Sustainable Engineering, 2018
Umberto Iemma, Fabio Pisi Vitagliano, Francesco Centracchio
Solving multi-objective optimisation problems implies to identify the variables vector x in the n-dimensional design space D corresponding to a minimum (or a maximum) of the objective functions Jk(x), with all the inequality constraints g(x) and equality constraints h(x) satisfied. In case of conflicting objectives, the optimality criterion lies on the existence of a set of solutions such that it is possible to further minimise one objective solely at the expense of at least another one: such solutions are called non-dominated and constitute the Pareto front. The selection of a single optimal solution among the Pareto solutions may involve the choice of a further objective, in the present work referred to as the merit factor Ψ(x), aimed at the ranking of the optimal solutions.
Design of near-optimal irregular fractional plans satisfying multi-optimality criteria
Published in Journal of Industrial and Production Engineering, 2018
The main advantage of this approach is that we can find out an optimal design which also performs better with respect to other optimality criteria. However, computational complexity is quite high. There is no proper termination criterion for NSGA-II as it runs until number of iterations equals to a predefined number. So, it is difficult to find out the number of iterations required to reach the optimality. We may get it locally, but not globally optimal designs, so we need to replicate the process several times and select the best result for final design.