In this paper, an artificial glowworm swarma* optimization algorithm for solving 0-1 knapsack problem is proposed, and the detailed realization of the algorithm is illustrated. According to intelligent algorithm for kna...
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ISBN:
(纸本)9780878492237
In this paper, an artificial glowworm swarma* optimization algorithm for solving 0-1 knapsack problem is proposed, and the detailed realization of the algorithm is illustrated. According to intelligent algorithm for knapsack problem, the question of sensitive parameter's choice is avoided under the greed idea. Simulation results show that the artificial glowworm swarma* optimization algorithm for solving 0-1 knapsack problems is feasible and effective.
Nature inspireda* optimization algorithms, namely artificial bee colony (ABC) optimization and firefly algorithm (FA), have been applied to synthesize beam patterns of a hexagonal planar array of isotropic elements. Two...
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Nature inspireda* optimization algorithms, namely artificial bee colony (ABC) optimization and firefly algorithm (FA), have been applied to synthesize beam patterns of a hexagonal planar array of isotropic elements. Two different cases, comprising two different beam patterns of a pencil beam and a square footprint pattern over a bounded region with lower peak sidelobe levels are presented. The pencil beam is generated by thinning the uniformly excited array and the square footprint pattern is generated by imposing optimum amplitudes, phases, and their corresponding states ("on"/"off") to the array elements. The optimum values of the parameters for both the cases are computed using ABC and FA individually, and the superiority of FA over ABC for the proposed problem in terms of computing solutions for both the cases is established.
Fault detection and identification of gas turbines is a crucial process for providing engine safe operation and decreasing the maintenance costs. In studies conducted in the field of global optimization-based gas turb...
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Fault detection and identification of gas turbines is a crucial process for providing engine safe operation and decreasing the maintenance costs. In studies conducted in the field of global optimization-based gas turbine fault diagnosis, the genetic algorithm as the most well-known evolutionarya* optimization algorithm is usually employed to identify the engine health parameters. However, because of the evolutionary and stochastic nature of this algorithm, the genetic-algorithm-based diagnosis usually suffers from computational burden and reliability. To mitigate this problem, in the present work, a comparative study has been performed on the global optimization-based gas turbine fault diagnosis, and it is shown that an innovative hybrida* optimization algorithm as a fault detection and identification system can significantly enhance the performance of the conventional optimization-based diagnosis systems, even in the presence of measurement noise. The results obtained indicate that the fault detection and identification system based on the hybrid invasive weed optimization/particle swarma* optimization algorithm outperforms all the examined diagnosis systems (i.e., the genetic-algorithm-based, particle-swarm-optimization-based, and invasive weed-optimization-based fault detection and identification system) in terms of accuracy, reliability, and especially computational cost. The results demonstrate that the genetic-algorithm-based fault detection and identification system showed the weakest performance among all the examined diagnosis systems.
Node localization algorithm of NLOS (Non-line-of-sight) environment based on PSO (particle swarm optimization) is proposed aiming at NLOS range error. PSO algorithm is quoted in wireless sensor network localization. F...
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Node localization algorithm of NLOS (Non-line-of-sight) environment based on PSO (particle swarm optimization) is proposed aiming at NLOS range error. PSO algorithm is quoted in wireless sensor network localization. First of all, the parameter of PSO algorithm is improved and nonlinear adjustment to inertia weight is made to boost convergence rate of algorithm, at the same time, target value is in rank ordering to decrease calculated quantity. Simulation results demonstrate that proposed algorithm reduced error influence of NLOS and improved location accuracy.
The objective of this paper is to demonstrate the opportunities of topology optimization applied to additive technology which will permit to design ultra-light structures practically without regard to the technologica...
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The objective of this paper is to demonstrate the opportunities of topology optimization applied to additive technology which will permit to design ultra-light structures practically without regard to the technological limits. The article gives a brief historical overview of the mutual influence of structures, materials and manufacturing technologies. The additive technology seems to have the broadest opportunities for producing existing structures using conventional materials without design changes. A hypothetical variable density material provides the means to solve an auxiliary problem of optimal material distribution considering stress or stiffness constraints. The speciala* optimization algorithm allows the optimal topology layout to be found which will have a minimum value of the integral characteristic, called “load-carrying factor” (LCF). The LCF is a powerful tool for estimating the perfection limit of any structural and technological solution. Along with the optimal structure, such solutions are difficult for manufacturing using conventional materials and technologies. A creation of real material with the characteristics of hypothetical material is considered as one of the nearest areas of additive technology's development for finding optimal structures.
The plug and play nature of distributed generation (DG) sources present in existing power grids changes the fault current levels and paths seen by the relays, which challenges the traditional network protection scheme...
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ISBN:
(纸本)9781538642924
The plug and play nature of distributed generation (DG) sources present in existing power grids changes the fault current levels and paths seen by the relays, which challenges the traditional network protection schemes. Conventional directional overcurrent relays (DOCRs) commonly used for the protection of meshed and ring type distribution systems may not be adequate to address the impacts caused by the huge penetration of various types of DGs and their fluctuating nature. Hence in this paper, a numerical protection strategy is proposed with adaptive protection settings considering the stochastic nature of DGs. The optimal relay settings to minimize the overall operating time of the numerical relays are obtained through a novel hybrid method. In this method, an adaptive fuzzy inference module (AFIM) equipped with ana* optimization algorithm is used for determining the appropriate current settings and a heuristic algorithm is used for calculating the time settings of the numerical relays. The proposed protection algorithm is tested on a modified IEEE 14-bus system equipped with different types of DGs. The case study results conducted with various DG scenarios and different operation modes verify the ability of the proposed approach to deduce optimal protection settings of the numerical protection algorithm with minimal relay operating time.
With the ability in addressing different types of information under uncertainty, the belief rule base (BRB) has been an efficient tool in modeling the nonlinearity of practical systems, as well as taking the experts k...
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ISBN:
(纸本)9781509007684
With the ability in addressing different types of information under uncertainty, the belief rule base (BRB) has been an efficient tool in modeling the nonlinearity of practical systems, as well as taking the experts knowledge and experience into the modeling process. However, the modeling accuracy has been the sole objective for BRB training and learning in the parameter optimization process, which does not take the modeling complexity into consideration. The exclusion of the modeling complexity can directly cause the infeasibility for constructing and further optimizing the BRB system, especially with human's involvement. In this study, the Akaike Information Criterion (AIC) is used the replace the conventional mean square error (MSE) as the modeling objective. Through a thorough deduction process, the AIC-based objective can represent both modeling accuracy and complexity. Furthermore, the BRB parameter model and the corresponding algorithm are proposed as well. The proposed BRB optimization with AIC-based objective is validated by a numeric case study.
Recently optimal design for engineering structures is being a research hotspot, and the core technology is structural optimization technology, which is being used in more and more fields. In this paper, its research p...
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ISBN:
(纸本)9781467388290
Recently optimal design for engineering structures is being a research hotspot, and the core technology is structural optimization technology, which is being used in more and more fields. In this paper, its research progress was reviewed which was consists of research condition and prospect in future. Firstly, the fundamental concept of structural optimization was introduced, and then structural optimization levels such as size optimization, shape optimization, and topology optimization and its application in aerospace field were presented. Afterwards,a* optimization algorithms like optimality criteria methods, mathematical programming methods, and genetic algorithms were reviewed, and finally, probable research directions of structural optimization technology in the future were prospected.
The adaptive niche quantum-inspired immune clonal algorithm (ANQICA) is proposed by combining the quantum coding, immune clone and niche mechanism together to solve the multi-modal function optimization more effective...
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The adaptive niche quantum-inspired immune clonal algorithm (ANQICA) is proposed by combining the quantum coding, immune clone and niche mechanism together to solve the multi-modal function optimization more effectively and make the function converge to as many as possible extreme value points. The quantum coding can better explore the solution space, the niche mechanism ensures the algorithm to converge to multi-extremum and the adaptive mechanism is introduced according to the characteristics of each procedure of the algorithm to improve the effect of the algorithm. Example analysis shows that the ANQICA is better in exploration and convergence. Therefore, the ANQICA can be used to solve the problem of multi-modal function optimization effectively.
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