Artificial Bee Colony (ABC) is a popular swarm intelligence based approach used to solve nonlinear and complex optimization problems. It is a simple to implement and swarm based probabilistic algorithm. As the case of...
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ISBN:
(纸本)9781479981656
Artificial Bee Colony (ABC) is a popular swarm intelligence based approach used to solve nonlinear and complex optimization problems. It is a simple to implement and swarm based probabilistic algorithm. As the case of other swarm based algorithms, ABC is also computationally expensive due to its slow nature of search process. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. Therefore, to balance the exploration and exploitation characteristics of the ABC, Shrinking Hyper-Sphere based localsearch approach is developed and hybridized with in the ABC solution search process. The proposed algorithm is named as Shrinking Hyper-Sphere based ABC (SHABC). The experiments over 14 well known benchmark functions of complex in nature, show that the SHABC algorithm perform better than the original version of ABC and its latest variant, namely Modified ABC (MABC) in most of the experiments.
Natural properties of stochastic searching strategies and operations in metaheuristic algorithms have important influence on convergence performance of various metaheuristic algorithms. Through similarity analysis to ...
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Natural properties of stochastic searching strategies and operations in metaheuristic algorithms have important influence on convergence performance of various metaheuristic algorithms. Through similarity analysis to two kinds of metaheuristic algorithms, exact heuristic algorithm based on branchand- cut is put forward according to change trend of similarity between two arbitrary high-quality solutions. In the meanwhile, two conditions were given in this paper because efficiency of branch-and-cut algorithm is closely allied to complexity of solved object. New heuristic algorithm can help metaheuristic algorithms finding superior solutions than other heuristic algorithms, and accelerate metaheuristic algorithms convergence. Simulation experiments show that new heuristic algorithm is efficacious.
The observation scheduling method for Space Situational Awareness (SSA) that prioritizes only Observation Effectiveness (OE) has a low completion rate for user requests. In this study, we present the results from our ...
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ISBN:
(纸本)9781509009374
The observation scheduling method for Space Situational Awareness (SSA) that prioritizes only Observation Effectiveness (OE) has a low completion rate for user requests. In this study, we present the results from our analysis of the method of scheduling Resident Space Objects (RSO: satellites and their debris orbiting the earth) in Low Earth Orbit (LEO) that prioritizes OE and Request Completion Rate (RCR). First, we analyzed the limitations of the existing method, and then, we designed a new method that increases the RCR of the requests having a high average value of OE. The proposed method results in an improvement of 16.1% in the completion rate of high-priority requests and a reduction of only 0.063 in the average OE when compared with the corresponding results from the method that prioritizes only OE. Further, we applied a local search algorithm to improve the schedule that we created.
Lattice reduction (LR) has been researched a lot in conjunction with ordered successive interference cancellation (OSIC), contributing large enhancement of the performance with relatively small complexity. However, th...
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ISBN:
(纸本)9781479980925
Lattice reduction (LR) has been researched a lot in conjunction with ordered successive interference cancellation (OSIC), contributing large enhancement of the performance with relatively small complexity. However, the performance gap between the LR-aided OSIC (LR-OSIC) and maximum likelihood (ML) still exists. This paper suggests LR-aided all-ordering successive interference cancellation (LR-AOSIC) as an optimum successive interference cancellation (SIC) detector showing near-ML performance almost unaffected by channel correlation and modulation order. In addition, LR-OSIC using modified local search algorithm (LR-MLS-OSIC) is also proposed as a sub-optimum SIC detector to reduce the complexity of LR-AOSIC affordably, nearly maintaining the performance.
In this study we present a combinatorial optimization method based on particle swarm optimization and local search algorithm on the multi-robot search system. Under this method, in order to create a balance between ex...
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