Theses and dissertations (Engineering and Built Environment)
Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/10
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Item Optimum design of steel structures using evolutionary algorithms(2019-01) Dolwana, Zolisa; Tabakov, Pavel Y.; Moyo, SibusisoThe subject of this thesis is optimization of steel structures using evolutionary algorithms. Heuristic algorithms are used and compared for the best possible results both in two dimen-sional and three dimensional structures. The topology, shape and sizing of the optimization problem has been formulated based on practical real life problems. The design has to produce best results without violating the stress and displacement constraints. The design constraints satisfy the demands of steel material properties and the selected profiles. Structural steel is discussed in detail on how they can be designed, and manufactured in both two dimensions (2-D) and three dimensions (3-D) to carry required loads and provide adequate rigidity. These types of structures are commonly found in the construction of build-ings, bridges, transmission line towers, industrial sheds, automotive vehicles and ships etc. Steel exhibits desirable physical properties that make it one of the most versatile structural materials in use. Its great strength, uniformity, light weight, ease of use, and many other de-sirable properties makes it the material of choice for numerous structures such as steel bridges, high rise buildings, towers, and other structures. Steel structures are formed with a specific shape following certain standards of chemical composition and strength. During the course of construction steel can be joined by welding or bolting methods. The structural steel problem is solved using population based methods, namely, the genetic algorithm (GA), particle swarm optimization (PSO) and big bang - big crunch (BB-BC). The quality of results produced using these heuristic methods has been studied in several problems. The present study demonstrates how progress in modern evolutionary algorithms has revolu-tionized design optimization of engineering structures. The performance of an evolutionary algorithm called the big bang - big crunch algorithm is shown by example of the steel trusses where the minimum possible weight was determined subjected to stress and displacement constraints.Item Two and three-dimensional bin packing problems : an efficient implementation of evolutionary algorithms(2018) Ntanjana, Andile; Tabakov, Pavel Y.; Moyo, SibusisoThe present research work deals with the implementation of heuristics and genetic algo- rithms to solve various bin packing problems (BPP). Bin packing problems are a class of optimization problems that have numerous applications in the industrial world, ranging from efficient cutting of material to packing various items in a larger container. Bin packing problems are known to be non-deterministic polynomial-time hard (NP-hard), and hence it is impossible to solve them exactly in polynomial time. Thus heuristics are very important to design practical algorithms for such problems. In this research we avoid the use of linear programming because we consider it to be a very cumbersome approach for analysing these types of problems and instead we proposed a simple and very efficient algorithm which is a combination of the fi fi heuristic algorithm in combination with the genetic algorithm, to solve the two and three – dimensional bin packing problems. The packing was carried out in two phases, wherein the fi phase the bins are packed by means of the fi fi heuristic algorithm with the help of other auxiliary techniques, and in the second phase the genetic algorithm is implemented. The purpose of the second phase is to improve the initial arrangements by performing combinatorial optimization for either a limited number of bins or the whole set at one time without destroying the original pattern (elitist strategy). The programming code developed can be used to write high-speed and capable software, which can be used in real-time applications. To conclude, the developed optimization ap- proach signifi tly helps to handle the bin packing problem. Numerical results obtained by optimizing existing industrial problems demonstrated that in many cases it was possible to achieve the optimum solution within only a few seconds, whereas for large-scale complex problems the result was near optimum efficiency over 90% within the same period of time.