Engineering, Genetic algorithms, Mathematical optimization


Optimization is considered an essential step in reinforcing the efficiency of performance and economic feasibility of construction projects. In the past few decades, evolutionary algorithms (EAs) have been widely utilized to solve various types of construction-related optimization problems due to their efficiency in finding good solutions in relatively short time periods. However, in many cases, these existing evolutionary algorithms failed to identify the optimal solution to several optimization problems. As such, it is deemed necessary to develop new approaches in order to help identify better-quality solutions. This doctoral research presents the development of a new evolutionary algorithm, named “Electimize,” that is based on the simulation of the flow of electric current in the branches of an electric circuit. The main motive in this research is to provide the construction industry with a robust optimization tool that overcomes some of the shortcomings of existing EAs. In solving optimization problems using Electimize, a number of wires (solution strings) composed of a number of segments are fabricated randomly. Each segment corresponds to a decision variable in the objective function. The wires are virtually connected in parallel to a source of an electricity to represent an electric circuit. The electric current passing through each wire is calculated by substituting the values of the segments in the objective function. The quality of the wire is based on its global resistance, which is calculated using Ohm’s law. iv he main objectives of this research are to 1) develop an optimization methodology that is capable of evaluating the quality of decision variable values in the solution string independently; 2) devise internal optimization mechanisms that would enable the algorithm to extensively search the solution space and avoid its convergence toward local optima; and 3) provide the construction industry with a reliable optimization tool that is capable of solving different classes of NP-hard optimization problems. First, internal processes are designed, modeled, and tested to enable the individual assessment of the quality of each decision variable value available in the solution space. The main principle in assessing the quality of each decision variable value individually is to use the segment resistance (local resistance) as an indicator of the quality. This is accomplished by conducting a sensitivity analysis to record the change in the resistance of a control wire, when a certain decision variable value is substituted into the corresponding segment of the control wire. The calculated local resistances of all segments of a wire are then normalized to ensure that their summation is equal to the global wire resistance and no violation is made of Kirchhoff’s rule. A benchmark NP-hard cash flow management problem from the literature is attempted to test and validate the performance of the developed approach. Not only was Electimize able to identify the optimal solution for the problem, but also it identified ten alternative optimal solutions, outperforming the existing algorithms. Second, the internal processes for the sensitivity analysis are designed to allow for extensive search of the solution space through the generation of new v wires. Every time a decision variable value is substituted in the control wire to assess its quality, a new wire that might have a better quality is generated. To further test the capabilities of Electimize in searching the solution space, Electimize was applied to a multimodal 9-city travelling salesman problem (TSP) that had been previously designed and solved mathematically. The problem has 27 alternative optimal solutions. Electimize succeeded to identify 21 of the 27 alternative optimal solutions in a limited time period. Moreover, Electimize was applied to a 16-city benchmark TSP (Ulysses16) and was able to identify the optimal tour and its alternative. Further, additional parameters are incorporated to 1) allow for the extensive search of the solution space, 2) prevent the convergence towards local optima, and 3) increase the rate of convergence towards the global optima. These parameters are classified into two categories: 1) resistance related parameters, and 2) solution exploration parameters. The resistance related parameters are: a) the conductor resistivity, b) its cross-sectional area, and c) the length of each segment. The main role of this set of parameters is to provide the algorithm with additional gauging parameters to help guide it towards the global optima. The solution exploration parameters included a) the heat factor, and b) the criterion of selecting the control wire. The main role of this set of parameters is to allow for an extensive search of the solution space in order to facilitate the identification all the available alternative optimal solutions; prevent the premature convergence towards local optima; and increase the rate of convergence towards the global optima. Two TSP instances (Bayg29 and ATT48) are attempted and vi the results obtained illustrate that Electimize outperforms other EAs with respect to the quality of solutions obtained. Third, to test the capabilities of Electimize as a reliable optimization tool in construction optimization problems, three benchmark NP-hard construction optimization problems are attempted. The first problem is the cash flow management problem, as mentioned earlier. The second problem is the time cost tradeoff problem (TCTP) and is used as an example of static optimization. The third problem is a site layout planning problem (SLPP), and represents dynamic optimization. When Electimize was applied to the TCTP, it succeeded to identify the optimal solution of the problem in a single iteration using thirty solution strings, compared to hundreds of iterations and solution strings that were used by EAs to solve the same problem. Electimize was also successful in solving the SLPP and outperformed the existing algorithm used to solve the problem by identifying a better optimal solution. The main contributions of this research are 1) developing a new approach and algorithm for optimization based on the simulation of the phenomenon of electrical conduction, 2) devising processes that enable assessing the quality of decision variable values independently, 3) formulating methodologies that allow for the extensive search of the solution space and identification of alternative optimal solutions, and 4) providing a robust optimization tool for decision makers and construction planners.


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Graduation Date





Khalafallah, Ahmed


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science


Civil, Environmental, and Construction Engineering








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Doctoral Dissertation (Open Access)


Dissertations, Academic -- Engineering and Computer Science, Engineering and Computer Science -- Dissertations, Academic

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