Chaotic signal is nonlinear and non-Gaussian signal. Features of non-periodic and wide band spectrum make it quite difficult to separate mixture of chaotic signals blindly. To address this issue, a new blind source se...
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ISBN:
(纸本)9781479944194
Chaotic signal is nonlinear and non-Gaussian signal. Features of non-periodic and wide band spectrum make it quite difficult to separate mixture of chaotic signals blindly. To address this issue, a new blind source separation method based on artificialbeecolony optimizer is proposed. Additionally, a parameterized representation of orthogonal matrices through principal rotation is adopted to reduce the complexity of the optimization problem. Simulation results show that the proposed method can accurately separate the mixture of chaotic signals within a few tens of iterations, and its overall performance is better than traditional independent component analysis approaches.
artificialbeecolony (ABC) algorithm is a swarm-based optimization algorithm with advantages like simplicity and proper exploration ability. However, it suffers from improper exploitation in solving complicated probl...
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ISBN:
(纸本)9781479954865
artificialbeecolony (ABC) algorithm is a swarm-based optimization algorithm with advantages like simplicity and proper exploration ability. However, it suffers from improper exploitation in solving complicated problems. In order to overcome this disadvantage, modifications on all three bee types are proposed. By introducing a new procedure for the scout bees and modifying the search patterns of both employed and onlooker bees, the capabilities of all three bee types are utilized properly. These modifications lead to better exploitation and exploration abilities. Experiments are conducted on 12 different benchmark functions including standard, shifted, rotated, and shifted-rotated multimodal problems. The results confirm the superiority of the proposed algorithm compared with some other well-known algorithms in this field.
In this paper, we present a new artificial bee colony algorithm to solve the non-unicost Set Covering Problem. The artificial bee colony algorithm is a recent metaheuristic technique based on the intelligent foraging ...
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ISBN:
(纸本)9783319067407
In this paper, we present a new artificial bee colony algorithm to solve the non-unicost Set Covering Problem. The artificial bee colony algorithm is a recent metaheuristic technique based on the intelligent foraging behavior of honey bee swarm. Computational results show that artificial bee colony algorithm is competitive in terms of solution quality with other metaheuristic approaches for the Set Covering Problem problem.
Since the parameters of wireless power transmission system have strong coupling, the efficiency and receiving power can't be controlled easily. This paper proposes to use improved artificial bee colony algorithm t...
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ISBN:
(纸本)9783038352105
Since the parameters of wireless power transmission system have strong coupling, the efficiency and receiving power can't be controlled easily. This paper proposes to use improved artificial bee colony algorithm to finish the coils designing. Firstly, it deduces the load current and power expression of the receiver coils and analyses the factors that influence the efficiency and power;then it introduces the constraints of the system and establishes the nonlinear programming model. Secondly, by improving the search method of onlookers, it accelerates the convergence speed and increases the global search ability of artificial bee colony algorithm. Finally, it uses the improved algorithm to design the resonant coils of the system and fulfills the efficiency optimization under the condition of rated power. Then it sets up a simulation experiment platform. The results show the effectiveness of the modified algorithm.
Intrusion detection, as a dynamic security protection technology, is able to defense the internal and external network attacks. Using artificial bee colony algorithm to optimize the parameters of neural network is to ...
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ISBN:
(纸本)9781479948604
Intrusion detection, as a dynamic security protection technology, is able to defense the internal and external network attacks. Using artificial bee colony algorithm to optimize the parameters of neural network is to avoid the neural network falling into a local optimum, can solve the problem of slow convergence speed of the neural network algorithm. Also artificial bee colony algorithm can deal with the problem of finding the optimal solutions in a very short period of time. In this paper, An artificialbeecolony optimized neural network algorithm is applied to intrusion detection. And the experimental results shows that the optimized method has better detection accuracy and efficiency than the single BP neural network.
Trusted network is characterized by a large amount of data, abnormal dispersion, and high complexity. Traditional methods are easily affected by trusted network environment, resulting in unreliable mining results. The...
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Trusted network is characterized by a large amount of data, abnormal dispersion, and high complexity. Traditional methods are easily affected by trusted network environment, resulting in unreliable mining results. Therefore, a new real-time mining method of trusted network difference data is proposed. Real-time collection of trusted network difference data through history system is performed on the basis of determining the principle of trusted network difference data mining and collecting and extracting the characteristics of difference data. The process of trusted network differential data mining is designed through the artificial bee colony algorithm. According to the process, differential data mining is carried out from three aspects: constructing a trusted network differential data transmission path, updating pheromone, and establishing a differential data transmission path set. The experimental results show that the proposed method can effectively realize the real-time mining of difference data, and the mining accuracy is more accurate.
In this study, we proposed a parallel implementation of the combinatorial type artificial bee colony algorithm which has an efficient neighbor production mechanism. Running time and performance tests of the proposed p...
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ISBN:
(纸本)9781479948741
In this study, we proposed a parallel implementation of the combinatorial type artificial bee colony algorithm which has an efficient neighbor production mechanism. Running time and performance tests of the proposed parallel model were carried on the traveling salesmen problem. Results show that parallel artificial bee colony algorithm decreases the running time due to exploiting the computational power of parallel computing systems. Beside, better quality solutions are obtained compared to the reproduction methods of the genetic algorithm.
Curriculum-Based university Course Time-Tabling, CB-CTT, a known scheduling problem. We adapted a new swarm intelligence approach, identified as MABC based on the artificialbeecolony (ABC) to solve the CB-CTT. The a...
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ISBN:
(纸本)9781479938575
Curriculum-Based university Course Time-Tabling, CB-CTT, a known scheduling problem. We adapted a new swarm intelligence approach, identified as MABC based on the artificialbeecolony (ABC) to solve the CB-CTT. The approach consists of two steps: first, a feasible solution of the problem is constructed, which satisfies only the hard constraints;and then, the soft constraints are attempted to be satisfied. MABC could satisfy the hard constraints of the problem for all datasets of the ITC-2007 track 3, a benchmark dataset for the CB-CTT. The penalty of the achieved solutions by MABC is comparable to the related work in the literature that used the ABC for solving the CB-CTT.
This paper investigates multiband cooperative spectrum sensing problem in cognitive radio system aiming at maximizing total opportunistic throughput when the interference to primary users is given. It can be formulate...
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ISBN:
(纸本)9781479962396
This paper investigates multiband cooperative spectrum sensing problem in cognitive radio system aiming at maximizing total opportunistic throughput when the interference to primary users is given. It can be formulated as a combinatorial optimization problem with two different kinds of parameters, weight coefficients and decision thresholds. Due to the non-convex nature of the formulated problem, we first propose an artificial bee colony algorithm (ABC) to solve it. To enhance the searching ability of the algorithm in terms of finding the optimal solution, we further introduce some modifications into the ABC and thus, the modified ABC (MABC) is devised. The simulation results indicate that MABC exhibits a promising performance in dealing with such problems when compared with other intelligence algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO).
artificialbeecolony (ABC) algorithm is a Nature Inspired algorithm (NIA). ABC is motivated by clever food foraging behavior of honey bees. Comparable to other population based stochastic algorithm ABC also lack bala...
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ISBN:
(纸本)9781479951734
artificialbeecolony (ABC) algorithm is a Nature Inspired algorithm (NIA). ABC is motivated by clever food foraging behavior of honey bees. Comparable to other population based stochastic algorithm ABC also lack balance in exploration of local search reason and exploitation of finest possible solutions in search space. The proposed algorithm is a hybrid of two memetic algorithms;it combines Levy Flight search in ABC (LFABC) and Memetic Search in ABC (MeABC) algorithm. The proposed algorithm named as Levy Flight Memetic Search in ABC (LFMABC) algorithm. The proposed LFMABC algorithm is tested over eleven benchmark functions in addition to two real world problems in order to establish its superiority over ABC and its recent variants. Results shows that proposed strategy LFMABC is able to find optimum in less time for considered problems.
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