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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]

Currently, population aging has aroused much concern in many countries since the elderly occupy a lot of social public resources in hospitals and nursing homes. Home health care (HHC) is treated as an alternative solution to serve the elderly community. In recent years, managing and organizing the operation of HHCs receive a great deal of attention. The HHC routing and scheduling problems attract huge amounts of interest from modeling and optimization areas. Nevertheless, there are rarely studies focusing on the cooperation of multiple HHC centers in such problems. This work addresses a multi-center, multi-objective and stochastic HHC routing and scheduling problem for minimizing the total operation cost and penalty cost incurred by earliness and delay service, where the caregivers' working time, customers' requirements and resource constraints are considered. Firstly, a multi-objective chance-constrained programming model is developed to formulate the studied problem. Secondly, a multi-objective cooperation evolutionary algorithm by using stochastic simulation is specially developed, in which two populations respectively perform global and local searches, and the cooperation of two populations is designed. The stochastic simulation approach is employed to evaluate the quality and feasibility of the obtained solutions. Finally, extensive experiments are performed on a set of test instances and three multi-objective optimization algorithms are compared to verify the performance of the proposed algorithm. The comparisons and discussions validate the competitiveness of the designed approach. [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]

Abstract: Despite dental implantation being a great success, one of the key issues facing it is a mismatch of mechanical properties between engineered and native biomaterials, which makes osseointegration and bone remodeling problematical. Functionally graded material (FGM) has been proposed as a potential upgrade to some conventional implant materials such as titanium for selection in prosthetic dentistry. The idea of an FGM dental implant is that the property would vary in a certain pattern to match the biomechanical characteristics required at different regions in the hosting bone. However, matching the properties does not necessarily guarantee the best osseointegration and bone remodeling. Little existing research has been reported on developing an optimal design of an FGM dental implant for promoting long-term success. Based upon remodeling results, metaheuristic algorithms such as the genetic algorithms (GAs) and simulated annealing (SA) have been adopted to develop a multi-objective optimal design for FGM implantation design. The results are compared with those in literature. [Copyright &y& Elsevier]

Abstract: This paper presents the application of ant colony optimization (ACO) for the multi-objective optimization of hybrid laminates for obtaining minimum weight and cost. The investigated laminate is made of glass–epoxy and graphite–epoxy plies to combine the lightness and economical attributes of the first with the high-stiffness property of the second using a modified variation of ACO so called the elitist ant system (EAS) in order to make the tradeoff between the cost and weight as the objective functions. First natural frequency was considered as a constraint. The obtained results using the EAS method including the Pareto set, optimum stacking sequences, and the number of plies made of either glass or graphite fibers were compared with those using the genetic algorithm (GA) and any colony system (ACS) reported in literature. The comparisons confirm the advantage of hybridization and showed that the EAS algorithm outperformed the GA and ACS in terms of function’s value and constraint accuracy. [Copyright &y& Elsevier]

Minimum cost of energy is the main goal of a wind farm layout optimization. This is achieved by maximizing the total energy while minimizing the total costs of the farm. In this study, two sizes of commercial turbines were considered to investigate the effect of a non-homogenous farm on the layout optimization process. A cost model consisting of turbines, cable, transformers, foundation, and service vehicle routes was developed. Using Genetic Algorithm and Artificial Neural Network, first the superiority of the new algorithm in turbines and cable layout was verified versus previous studies. Next, two cases were investigated, i.e. (1) a farm populated with identical turbines and (2) a farm with a random mixture of both sizes of turbines. The layouts of both cases were optimized by both single and multi-objective optimizations. In the single objective optimization, only the larger turbines remained in the optimal layout of the second case and reduced the Levelized Cost of Energy (LCOE) into half of the first case. Multi-objective optimization clarified the reason for selecting larger size turbines in the layouts when the goal of the optimization was to minimize the cost of energy. As reported in literature, non-homogenous farms produce higher output. However, they impose a higher LCOE which makes them less appealing to developers. • Single objective (cost) and multi-objective (cost & power output) optimization result in different layout. • Non-homogenous farms produce higher power output. • Non-homogenous farms impose a higher LCOE which makes them less appealing to developers. [ABSTRACT FROM AUTHOR]