Abstract
In the optimum design of reinforced concrete (RC) structural members, the robustness of the employed method is important as well as solving the optimization problem. In some cases where the algorithm parameters are defined as non-effective values, local-optimum solutions may prevail over the existing global optimum results. Any metaheuristic algorithm can be effective to solve the optimization problem but must give the same results for several runs. Due to the randomization nature of these algorithms, the performance may vary with respect to time. The essential and novel work done in this study is the comparative investigation of 10 different metaheuristic algorithms and two modifications of harmony search (HS) algorithm on the optimum cost design of RC retaining walls constrained with geotechnical and structural state limits. The employed algorithms include classical ones (genetic algorithm (GA), differential evaluation (DE), and particle swarm optimization (PSO)), proved ones on structural engineering applications (harmony search, artificial bee colony, firefly algorithm), and recent algorithms (teaching–learning-based optimization (TLBO), flower pollination algorithm (FPA), grey wolf optimization, Jaya algorithm (JA)). The modifications of HS include adaptive HS (AHS) concerning the automatic change of algorithm parameters and hybridization of AHS with JA that is developed for the investigated problem. According to the numerical investigations, recent algorithms such as TLBO, FPA, and JA are generally the best at finding the optimum values with less deviation than the others. The adaptive-hybrid HS proposed in this study is also competitive with these algorithms, while it can reach the best solution by using a lower population number which can lead to timesaving in the optimization process. By the minimization of material used in construction via best optimization, sustainable structures that support multiple types of constraints are provided.
Highlights
harmony search (HS) is combined with Jaya algorithm (JA) by considering the general optimization equation belonging to the JA method shown in Equation (32) instead of the global search phase of HS (expressed via Equation (33)), besides the usage of modified versions of HMCR and FW parameters
Case 1 In Case 1, optimum design variables and minimum cost were obtained for a specific design through performing 30 cycles
The best result as minimum cost value can be provided via differential evolution (DE), particle swarm optimization (PSO), Flower Pollination Algorithm (FPA), Teaching–Learning-Based Optimization (TLBO), and JA
Summary
Metaheuristic algorithms that formalize a process, happening, natural phenomena, or theory in several phases of numerical iterations, are generally used in the design of structures These algorithms are especially effective on the optimum cost design of reinforced concrete (RC) members since they involve two different types of material with a complimentary design to eliminate disadvantages of concrete and steel. That is because the optimum design of retaining walls is one of the major applications in structural optimization, and the studies were performed during the 1980s [11]. The research cases include 30 multiple cycles of the optimization methodology for evaluation of algorithms based on minimum cost, average cost, and standard deviation
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