This paper aims to provide an optimal design of geometric parameters of a special architecture of the delta parallel mechanism, in order to improve positioning accuracy, workspace size, and kinematic and dynamic perfo...
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This paper aims to provide an optimal design of geometric parameters of a special architecture of the delta parallel mechanism, in order to improve positioning accuracy, workspace size, and kinematic and dynamic performance characteristics. In the studied 3[P2(US)] robot, the radius of both fixed and moving platforms, length of the connecting rods, and installation angle of the actuators of the manipulator are chosen as the decision variables. These parameters are optimized to maximize the weighted objective function, comprising workspace volume, global dexterity, global mass, global error, and global error sensitivity indices. Optimizations are performed employing two distinct algorithms, Genetic and harmonysearch whose results confirm each other. The optimal design of the robot leads to maximum workspace size, high dexterity, and dynamic performance, with a minimum error of the end-effector position in its reachable workspace.
One of the advances made in the management of human resources for the effective implementation of service delivery is the creation of personnel schedules. In this context, especially in terms of the majority of health...
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One of the advances made in the management of human resources for the effective implementation of service delivery is the creation of personnel schedules. In this context, especially in terms of the majority of health-care systems, creating nurse schedules comes to the fore. Nurse scheduling problem (NSP) is a complex optimization problem that allows for the preparation of an appropriate schedule for nurses and, in doing so, considers the system constraints such as legal regulations, nurses' preferences, and hospital policies and requirements. There are many studies in the literature that use exact solution algorithms, heuristics, and meta-heuristics approaches. Especially in large-scale problems, for which deterministic methods may require too much time and cost to reach a solution, heuristics and meta-heuristic approaches come to the fore instead of exact methods. In the first phase of the study, harmony search algorithm (HSA), which has shown progress recently and can be adapted to many problems is applied for a dataset in the literature, and the algorithm's performance is evaluated by comparing the results with other heuristics which is applied to the same dataset. As a result of the evaluation, the performance of the classical HSA is inadequate when compared to other heuristics. In the second phase of our study, by considering new approaches proposed by the literature for HSA, the effects on the algorithm's performance of these approaches are investigated and we tried to improve the performance of the algorithm. With the results, it has been determined that the improved algorithm, which is called opposition-based parallel HSA, can be used effectively for NSPs.
Linear Quadratic Regulator (LQR) is an important control technique with excellent properties associated to robust stability. This technique has been the subject of constant study until the present day, since the probl...
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Linear Quadratic Regulator (LQR) is an important control technique with excellent properties associated to robust stability. This technique has been the subject of constant study until the present day, since the problems weighting matrices Q and R are still open, as well as the situations in which the dynamics is unknown. This work proposes the application of a novel harmonysearch (HS) algorithm to find the Q and R matrices. This algorithm ensures the automatic adjustment of parameters. By adopting the CH-47 helicopter and an inverted pendulum system, several tests involving search and simulations have been performed with two HS algorithms i.e. standard HS and Statistical Dispersion harmonysearch (SDHS). The latter one can be seen as the main contribution of this work, which also compares the results obtained from the algorithms.
This paper presents an enhanced adaptive global-best harmonysearch (EAGHS) to solve global continuous optimization problems. The global-best HS (GHS) is one of the strongest versions of the classical HS algorithm tha...
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This paper presents an enhanced adaptive global-best harmonysearch (EAGHS) to solve global continuous optimization problems. The global-best HS (GHS) is one of the strongest versions of the classical HS algorithm that hybridizes the concepts of swarm intelligence and conventional HS. However, randomized selection of harmony in the permissible interval diverts the GHS algorithm from the global optimum. To address this issue, the proposed EAGHS method introduces a dynamic coefficient into the GHS algorithm to increase the search power in early iterations. Various complex and extensively-applied benchmark functions are used to validate the developed EAGHS algorithm. The results indicate that the EAGHS algorithm offers faster convergence and better accuracy than the standard HS, GHS and other similar algorithms. Further analysis is performed to evaluate the sensitivity of the proposed method to the changes of parameters such as harmony memory consideration rate, harmonysearch memory, and larger dimensions.
This paper proposes a modified global harmonysearch (MGHS) algorithm with random crossover algorithm to solve continuous high dimensional optimization problems. For the problem of premature convergence in harmony sea...
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ISBN:
(纸本)9781728101057
This paper proposes a modified global harmonysearch (MGHS) algorithm with random crossover algorithm to solve continuous high dimensional optimization problems. For the problem of premature convergence in harmony search algorithm, in the improvisation stage of MGHS algorithm, the new harmony vector is generated dynamically by means of random crossover for the global optimization problems, i.e., the worst harmony learning from the best harmony and the random selected other harmony learning from the best harmony random crossover strategy. Finally. MGHS algorithm is applied in the simulation test of 8 benchmark functions, the simulation results demonstrate the MGHS algorithm has higher convergence precision and convergence rate.
Economic considerations are significantly important in designing a dam and its related hydraulic structures. Considering the methods used for economic design of hydraulic structures such as a spillway, they are also d...
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Nurse Rostering Problem (NRP) is a well-known NP-Hard combinatorial optimization problem. The fact is that coping real-world constraints in allocating the shift duties fairly among the available nurses is still a hard...
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ISBN:
(纸本)9783319990071;9783319990064
Nurse Rostering Problem (NRP) is a well-known NP-Hard combinatorial optimization problem. The fact is that coping real-world constraints in allocating the shift duties fairly among the available nurses is still a hard task to accomplish. The problem becomes more serious due to the shortage of nurses. Thus, this work aims to tackle this problem by hybridizing an Enhanced harmony search algorithm (EHSA) with the standard Hill climbing (HC). This hybridization may help to strike the balance between exploration and exploitation in the searching process. The proposed algorithm is called Climbing harmony search algorithm (CHSA) where it applied to solve a real-world NRP dataset, which arises at the Medical Center of Universiti Kebangsaan. The results show that CHSA performs much better than EHSA alone and Basic harmony search algorithm (BHSA) in all instances in terms of obtained penalty values (PVs), desirable patterns (DPs) and computational time as well.
A modified multi-objective harmony search algorithm called Niching Multi-objective harmony search algorithm (NMOHSA) is proposed to solve multimodal multi-objective optimization problems. It adopts the neighborhood in...
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ISBN:
(纸本)9781728121536
A modified multi-objective harmony search algorithm called Niching Multi-objective harmony search algorithm (NMOHSA) is proposed to solve multimodal multi-objective optimization problems. It adopts the neighborhood information to build dynamic harmony memory for maintaining the population diversity. A new memory consideration rule is also applied to prevent the algorithm be trapped into local optimal solution. Moreover, two key parameters, harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR), are dynamically adjusted. Empirical results show that the proposed algorithm performs much better than the other existing multimodal multi-objective algorithms in terms of the solution quality.
Recently the development of optimization algorithm is rapidly increased. Among several optimization algorithms, harmonysearch (HS) has been recently proposed for solving engineering optimization problems. The HS has ...
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ISBN:
(纸本)9781450372015
Recently the development of optimization algorithm is rapidly increased. Among several optimization algorithms, harmonysearch (HS) has been recently proposed for solving engineering optimization problems. The HS has some weaknesses such as parameters selection and falling in local optima. Many variants proposed to solve these problems. This paper presents successful hybrid algorithms with high performance to solve the pressure vessel design simulation. The hybrid algorithms consist of well-known variants of HS and an opposition-based learning technique. The hybrid algorithm improved the HS exploration and avoiding falling in local optima, which lead the algorithm to provide significant results.
Finding the optimal parameters that can reproduce experimental data (such as the velocity-density relation and the specific flow rate) is a very important component of the validation and calibration of microscopic cro...
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Finding the optimal parameters that can reproduce experimental data (such as the velocity-density relation and the specific flow rate) is a very important component of the validation and calibration of microscopic crowd dynamic models. Heavy computational demand during parameter search is a known limitation that exists in a previously developed model known as the harmonysearch-Based Social Force Model (HS-SFM). In this paper, a parallel-based mechanism is proposed to reduce the computational time and memory resource utilisation required to find these parameters. More specifically, two MATLAB-based multicore techniques (parfor and create independent jobs) using shared memory are developed by taking advantage of the multithreading capabilities of parallel computing, resulting in a new framework called the Parallel harmonysearch-Based Social Force Model (P-HS-SFM). The experimental results show that the parfor-based P-HS-SFM achieved a better computational time of about 26h, an efficiency improvement of approximate to 54% and a speedup factor of 2.196 times in comparison with the HS-SFM sequential processor. The performance of the P-HS-SFM using the create independent jobs approach is also comparable to parfor with a computational time of 26.8h, an efficiency improvement of about 30% and a speedup of 2.137 times.
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