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Journal of Optimization publishes research on both theoretical and applied aspects of mathematical programming and optimization methodologies in science and engineering.
Journal of Optimization maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
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Investigation of Bus Special Lane Performance Using Statistical Analysis and Optimization of the Signalized Intersection Delay by Machine Learning Methods
Nowadays, the performance analysis and evaluation of public transportation systems have great importance in traffic engineering science. So far, the bus system has not been very effective in some cities in Iran, and many management approaches such as the allocation of special lanes and regular bus scheduling, which are needed to increase the efficiency of this system, have not been sufficiently considered. The purpose of the present study is to optimize the delay of the signalized intersection of bus lane and investigate the factors affecting the urban bus usage by citizens in public transportation of Rasht city and especially their satisfaction. Therefore, the intersection delay was optimized by gathering the traffic volume data in peak hour time of a signalized intersection along the bus lane and using machine learning methods. In addition, by collecting two different questionnaires, taking 84 samples (first questionnaire) and 374 samples (second questionnaire), the satisfaction of citizens and business people on the boundary of the bus lane was considered. The results indicated that about 95% of the businesses around this route believe that the construction of the bus lane led to a decrease in the income of more than 110 dollars per month. Further to this, despite the lack of facilities, poorly designed routes, and lack of the bus system fleet, the bus lane of Imam Khomeini had a high degree of satisfaction among the citizens. The result of various models showed that the adaptive network-based fuzzy inference system (ANFIS) had the highest R2 and the lowest amount of root mean square error (RMSE). In fact, this model had a better performance to predict and optimize the delay of signalized intersection than the fuzzy model. The optimum amount of intersection delay was determined as 56 seconds. With this value, the delay of bus movements in the bus lane had a higher possibility of being reduced.
Optimization for the Redundancy Allocation Problem of Reliability Using an Improved Particle Swarm Optimization Algorithm
This paper presents an enhanced and improved particle swarm optimization (PSO) approach to overcome reliability-redundancy allocation problems in series, series-parallel, and complex systems. The problems mentioned above can be solved by increasing the overall system reliability and minimizing the system cost, weight, and volume. To achieve this with these nonlinear constraints, an approach is developed based on PSO. In particular, the inertia and acceleration coefficients of the classical particle swarm algorithm are improved by considering a normal distribution for the coefficients. The new expressions can enhance the global search ability in the initial stage, restrain premature convergence, and enable the algorithm to focus on the local fine search in the later stage, and this can enhance the perfection of the optimization process. Illustrative examples are provided as proof of the efficiency and effectiveness of the proposed approach. Results show that the overall system reliability is far better when compared with that of some approaches developed in previous studies for all three tested cases.
Optimum Design and Performance Analyses of Convective-Radiative Cooling Fin under the Influence of Magnetic Field Using Finite Element Method
In this study, the optimum design dimensions and performance analyses of convective-radiative cooling fin subjected to magnetic field are presented using finite element method. The numerical solutions are verified by the exact analytical solution for the linearized models using Laplace transform. The optimum dimensions for the optimum performance of the convection-radiative fin with variable thermal conductivity are investigated and presented graphically. Also, the effects of convective, radiative, and magnetic parameters as well as Biot number on the thermal performance of the cooling fin are analyzed using the numerical solutions. From the results, it is established that the optimum length of the fin and the thermogeometric parameter increases as the nonlinear thermal conductivity term increases. Further analyses also reveal that as the Biot number, convective, radiative, and magnetic parameters, increases, the rate of heat transfer from the fin increases and consequently improves the efficiency of the fin. Additionally, effects of the thermal stability values for the various multiboiling heat transfer modes are established. It is established that, in order to ensure stability and avoid numerical diffusion of the solution by the Galerkin finite element method, the thermogeometric parameter must not exceed some certain values for the different multiboiling heat transfer modes. It is hope that the present study will enhance the understanding of thermal response of solid fin under various factors and fin design considerations.
Theoretical Analysis of an Imprecise Prey-Predator Model with Harvesting and Optimal Control
In our present paper, we formulate and study a prey-predator system with imprecise values for the parameters. We also consider harvesting for both the prey and predator species. Then we describe the complex dynamics of the proposed model system including positivity and uniform boundedness of the system, and existence and stability criteria of various equilibrium points. Also the existence of bionomic equilibrium and optimal harvesting policy are thoroughly investigated. Some numerical simulations have been presented in support of theoretical works. Further the requirement of considering imprecise values for the set of model parameters is also highlighted.
Extended GRASP-Capacitated -Means Clustering Algorithm to Establish Humanitarian Support Centers in Large Regions at Risk in Mexico
Mexico is located within the so-called Fire Belt which makes it susceptible to earthquakes. In fact, two-thirds of the Mexican territory have a significant seismic risk. On the other hand, the country’s location in the tropical zone makes it susceptible to hurricanes which are generated in both the Pacific and Atlantic Oceans. Due to these situations, each year many communities are affected by diverse natural disasters in Mexico and efficient logistic systems are required to provide prompt support. This work is aimed at providing an efficient metaheuristic to determine the most appropriate location for support centers in the State of Veracruz, which is one of the most affected regions in Mexico. The metaheuristic is based on the -Means Clustering (KMC) algorithm which is extended to integrate (a) the associated capacity restrictions of the support centers, (b) a micro Genetic Algorithm GA to estimate a search interval for the most suitable number of support centers, (c) variable number of assigned elements to centers in order to add flexibility to the assignation task, and (d) random-based decision model to further improve the final assignments. These extensions on the KMC algorithm led to the GRASP-Capacitated -Means Clustering (GRASP-CKMC) algorithm which was able to provide very suitable solutions for the establishment of 260 support centers for 3837 communities at risk in Veracruz, Mexico. Validation of the GRASP-CKMC algorithm was performed with well-known test instances and metaheuristics. The validation supported its suitability as alternative to standard metaheuristics such as Capacitated -Means (CKM), Genetic Algorithms (GA), and Variable Neighborhood Search (VNS).
Topology-Aware Strategy for MPI-IO Operations in Clusters
This paper presents the topology-aware two-phase I/O (TATP), which optimizes the most popular collective MPI-IO implementation of ROMIO. In order to improve the hop-bytes metric during the file access, topology-aware two-phase I/O employs the Linear Assignment Problem (LAP) for finding an optimal assignment of file domain to aggregators, an aspect which is not considered in most two-phase I/O implementations. The distribution is based on the local data stored by each process, and its main purpose is to reduce the total hop-bytes of the I/O collective operation. Therefore, the global execution time can be improved. In most of the considered scenarios, topology-aware two-phase I/O obtains important improvements when compared with the original two-phase I/O implementations.
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