Metaheuristic algorithms for building Covering Arrays: A review

Timaná-Peña, Jimena Adriana; Cobos, Carlos; Torres-Jiménez, José

doi:10.19053/01211129.v25.n43.2016.5295

Cited by 6 publications

(1 citation statement)

References 60 publications

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“…This technic includes tabu search, genetic algorithms, evolution strategies, simulated annealing, particle swarm optimizer, ant colony optimization and harmony search. [66], [68], [69] at specific case,they chose simulated annealing and evolution strategies as the most powerful techniques and characterized harmony search and simple genetic algorithm methods as slowest convergence rates algorithm [69]. Similarly, Leticia Fleck Fadel Miguel evaluated Harmony Search (HS) and Firefly Algorithm (FA) to solve truss shape and sizing optimization with multiple natural frequency constraints.…”

Section: Metaheuristic Optimization Algorithmmentioning

confidence: 99%

A review of applied methods of optimization in steel structures

Kazem¹,

Matinrad²

2021

ce papers

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Recent dazzling progress in computer science and stringent requirements of the design of “more optimized” buildings put forwards the study and research of the various optimization methods in the building sector. This study presents a comprehensive review of the state‐of‐the‐art and the most applicable algorithms applying computational optimization to Steel buildings and different steel design problems. Four different algorithms including Neural Network, Bee and Bat‐Inspired algorithm and Metaheuristic Optimization algorithm have been studied with emphasized on the application of these methods in steel structures. Algorithm Key points are reviewed with more details including configuration of the algorithms and control of building systems, besides particular attention on accuracy and capability of them to be converged as an optimized solution. From different convergence rate, number of unknown parameters and boundaries of solutions It can be concluded that in near future researches should be oriented towards improving the efficiency of the most effective search techniques and realistic approximation methods especially in large‐scale building optimization problems; and reducing time with increasing accuracy for such activities. Further effort should be required to quantify the robustness in optimal answers so as to improve building performance.

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Section: Metaheuristic Optimization Algorithmmentioning

confidence: 99%

A review of applied methods of optimization in steel structures

Kazem¹,

Matinrad²

2021

ce papers

View full text Add to dashboard Cite

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Adapting the elitism on greedy algorithm for variable strength combinatorial test cases generation

et al. 2019

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A combinatorial testing (CT) is an important technique usually employed in the generation of test cases. The generation of an optimal sized test case is a non-deterministic polynomial hard problem. In recent times, many researchers had developed various strategies based on the search-based approach to address the CT issues. This study presented the most recent variable interaction strength (VS) CT strategy using an enhanced variant in the greedy algorithm. Hence, they are referred to as variable strength modified greedy strategy (VS-MGS). Moreover, the modified strategy supports a VS together with interaction strength up to six. The proposed variant-greedy algorithm employed the elitism mechanism alongside the iteration in order to improve its efficiency. This algorithm is invariably called the modified greedy algorithm (MGA). Furthermore, the efficiency and performance of the VS-MGS using MGA were assessed first by comparing its results with the original greedy algorithm results and thereafter benchmarked with the results of the existing VS CT strategies. The VS-MGS's results ultimately revealed that the adaptation of elitism mechanism with iteration in greedy algorithm resulted in an improved efficiency in the process of generating a near-optimal test case set size.

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