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Abstract Functionally graded beams (FGBs) have been studied extensively in literature to predict their static stability and dynamic behavior. In typical FGBs, each of material variation has significant influence on overall beams’ strength. This research utilized single and multi-objective optimization approaches to optimize the volume fraction distribution in the Timoshenko FGBs in order to maximize the fundamental frequency while minimizing mass and axial stresses. The FGBs with different ratios of material properties are considered. Several metaheuristic optimization techniques are utilized to address single and multi-objective problems. For validating purpose, computational optimization results are compared with those available in the literature. [ABSTRACT FROM AUTHOR]

Internationally, there are heightened demands for efficient public transportation systems due to high population growth rates in urban areas and their associated increased trip demands within and across city boundaries. An ideal and sustainable public transportation system should satisfy its passengers while minimizing operation costs that are often associated with energy consumptions. One such cost-effective approach is establishing an integrated public transit system. A transit system generally includes a set of bus routes and rail lines connected by transfer stations. The main objective of this research is to propose a sustainable and integrated transit establishment model to design an optimal bus transit system in combination with an existing railway system dealing with both fixed and variable demands while satisfying multiple objectives. Moreover, this paper finds an optimum set of transit routes that corresponds to chosen tradeoffs between user cost, operator cost and, notably, unsatisfied demand cost. Optimal transit networks have been achieved using single and multi-objective approaches via metaheuristic optimization algorithms including the genetic algorithm and the non-dominated sorting genetic algorithm II (NSGA-II). The study area is chosen as Daejeon City, South Korea for its strategic location. Compared with existing transit networks, the proposed approach shows significant improvements in terms of costs. In addition, the proposed approach can provide an efficient methodology for finding alternative alignments of existing transit systems for decision makers. [ABSTRACT FROM AUTHOR]

Public transportation can have an efficient role ingainingtraveler satisfaction while decreasing operation costs through establishing an integrated public transit system. The main objective of this research is to propose an integrated multimodal transit model to design the best combination of both railway and feeder bus mode transit systems, while minimizing total cost. In this paper, we have proposed a strategy for designing transit networks that provide multimodal services at each stop, and for consecutively assigning optimum demand to the different feeder modes. Optimum transit networks have been achieved using single and multi-objective approaches via metaheuristic optimization algorithms, such as simulated annealing, genetic algorithms, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The used input data and study area were based on the real transit network of Petaling Jaya, located in Kuala Lumpur, Malaysia. Numerical results of the presented model, containing the statistical results, the optimum demand ratio, optimal solution, convergence rate, and comparisons among best solutions have been discussed in detail. [ABSTRACT FROM AUTHOR]

In this paper, a metaheuristic optimizer, the multi-objective water cycle algorithm (MOWCA), is presented for solving constrained multi-objective problems. The MOWCA is based on emulation of the water cycle process in nature. In this study, a set of non-dominated solutions obtained by the proposed algorithm is kept in an archive to be used to display the exploratory capability of the MOWCA as compared to other efficient methods in the literature. Moreover, to make a comprehensive assessment about the robustness and efficiency of the proposed algorithm, the obtained optimization results are also compared with other widely used optimizers for constrained and engineering design problems. The comparisons are carried out using tabular, descriptive, and graphical presentations. [ABSTRACT FROM AUTHOR]