By analyzing the living behaviors of eels, this paper proposes a new eel swarm intelligence algorithm. This paper first describes the behavior of migratory eels, extracts three important behaviors--density adaption, n...
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By analyzing the living behaviors of eels, this paper proposes a new eel swarm intelligence algorithm. This paper first describes the behavior of migratory eels, extracts three important behaviors--density adaption, neighboring learning and sex mutation, and establishes a model for the mathematical description of the three important behaviors. Based on rational organization of the three important behaviors, the eel algorithm is designed for continuous optimization problems. Finally, we test the performance of eel swarm intelligence algorithm via several selected benchmark problems. The results show that the algorithm, in terms of its excellent convergence speed and solving accuracy, is competitive tothe compared algorithms.
This letter highlights a fundamental inconsistency in the formulation of the Gravitational search algorithm (GSA) (Rashedi et al., Inf Sci 2232-48, 2009). GSA is said to be based on the law of gravity, that is, candid...
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This letter highlights a fundamental inconsistency in the formulation of the Gravitational search algorithm (GSA) (Rashedi et al., Inf Sci 2232-48, 2009). GSA is said to be based on the law of gravity, that is, candidate solutions attract each other in the search space based on their relative distances and 'masses' (qualities). We show that, contrary to what is claimed, GSA does not take the distances between solutions into account, and therefore cannot be considered to be based on the law of gravity.
Gravitational search algorithm is one of the new optimization algorithms that is based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interact...
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Gravitational search algorithm is one of the new optimization algorithms that is based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a binary version of the algorithm is introduced. To evaluate the performances of the proposed algorithm, several experiments are performed. The experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions.
In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. in this paper, a new optimization algorithm based on the law of gravity and...
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In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. in this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristicsearch methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions. (C) 2009 Elsevier Inc. All rights reserved.
This paper compares three heuristic search algorithms: genetic algorithm (GA), simulated annealing (SA) and tabu search (TS), for hardware-software partitioning. The algorithms operate on functional blocks for designs...
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This paper compares three heuristic search algorithms: genetic algorithm (GA), simulated annealing (SA) and tabu search (TS), for hardware-software partitioning. The algorithms operate on functional blocks for designs represented as directed acyclic graphs, with the objective of minimising processing time under various hardware area constraints. The comparison involves a model for calculating processing time based on a non-increasing first-fit algorithm to schedule tasks, given that shared resource conflicts do not occur. The results show that TS is superior to SA and GA in terms of both search time and quality of solutions. In addition, we have implemented an intensification strategy in TS called penalty reward, which can further improve the quality of results.
An off-line algorithm for semi-empirical modeling of non-linear dynamic systems is presented. The model representation is based on the interpolation of a number of simple local models, where the validity of each local...
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An off-line algorithm for semi-empirical modeling of non-linear dynamic systems is presented. The model representation is based on the interpolation of a number of simple local models, where the validity of each local model is restricted to an operating regime, but where the local models yield a complete global model when interpolated. The input to the algorithm is a sequence of empirical data and a set of candidate local model structures. The algorithm searches for an optimal decomposition into operating regimes, and local model structures. The method is illustrated using simulated and real data. The transparency of the resulting model and the flexibility with respect to incorporation of prior knowledge is discussed.
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