A review on simulation-based optimization methods applied to building performance analysis

Tree Growth Algorithm (TGA)

Metaheuristic algorithms have provided efficient tools to engineering designers by which it became possible to determine the optimum solutions of engineering design optimization problems encountered in every day practice. Generally metaheuristics are based on metaphors that are taken from nature or some other processes. Because of their success of providing solutions to complex engineering design optimization problems the recent literature has flourished with a large number of new metaheuristics based on a variety of metaphors. Despite the fact that most of these techniques have numerically proven themselves as reliable and strong tools for solutions of design optimization problems in many different disciplines, some argue against these methods on account of not having mathematical background and making use of irrelevant and odd metaphors. However, so long as these efforts bring about computationally efficient and robust optimum structural tools for designers what type of metaphors they are based on becomes insignificant. After a brief historical review of structural optimization this article opens this issue up for discussion of the readers and attempts to answer some of the criticisms asserted in some recent publications related with the novelty of metaheuristics.

Metaheuristics in structural optimization and discussions on harmony search algorithm

Integrating daylight and energy performance with optimization into the design process has always been a challenge for designers. Most of the building environmental performance simulation tools require a considerable amount of time and iterations for achieving accurate results. Moreover the combination of daylight and energy performances has always been an issue, as different software packages are needed to perform detailed calculations. A simplified method to overcome both issues using recent advances in software integration is explored here. As a case study; the optimization of external shadings in a typical office space in Australia is presented. Results are compared against common solutions adopted as industry standard practices. Visual comfort and energy efficiency are analysed in an integrated approach. The DIVA (Design, Iterate, Validate and Adapt) plug-in for Rhinoceros/Grasshopper software is used as the main tool, given its ability to effectively calculate daylight metrics (using the Radiance/Daysim engine) and energy consumption (using the EnergyPlus engine). The optimization process is carried out parametrically controlling the shadings’ geometries. Genetic Algorithms (GA) embedded in the evolutionary solver Galapagos are adopted in order to achieve close to optimum results by controlling iteration parameters. The optimized result, in comparison with conventional design techniques, reveals significant enhancement of comfort levels and energy efficiency. Benefits and drawbacks of the proposed strategy are then discussed.

/pdf/daylight-design-of-office-buildings-optimisation-of-external-3r3dv6cetx.pdf

Daylight Design of Office Buildings: Optimisation of External Solar Shadings by Using Combined Simulation Methods

Performance based discrete topology optimization of steel braced frames by a new metaheuristic

Optimization may be used in many engineering disciplines, and structural engineering is one among them. Structural optimization deals with topology geometry and size optimization of different kinds of structures such as frames, trusses, plates, and shells to achieve minimum cost, weight, or other specific goals. A basic understanding of optimization problem specifications and the capabilities and incapabilities of solution techniques is vital for researchers in this field. There are many different kinds of structural optimization problems and solution techniques. Structural optimization problems are mostly nonlinear because of their objective(s) and constraint(s). They usually have many local minimums, which make them complex and difficult to solve using classical methods. In this chapter, a classification of optimization problems and techniques is presented. Some of the major advances during the history of structural optimization are presented here. Metaheuristic techniques have proven to be versatile and robust techniques. Some of the most popular metaheuristic techniques utilize genetic algorithms, simulated annealing, tabu search, ant colony optimization, particle swarm optimization, harmony search, Big Bang–Big Crunch, firefly algorithm, cuckoo search, and other algorithms, and their applications in structural optimization have been be investigated.

Mohammed Ghasem Sahab

Papers

A Review on Traditional and Modern Structural Optimization: Problems and Techniques