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This paper presents a hybrid optimization method based on the fusion of the clonal selection algorithm (CSA) and harmony search (HS) technique. The CSA is employed to improve the members of the harmony memory in the HS method. The hybrid optimization algorithm is further used to optimize a fuzzy classification system for the Fisher Iris data classification. Computer simulations results demonstrate...
The harmony search (HS) method is an emerging meta-heuristic optimization algorithm. In this paper, we propose two modified HS methods to deal with the uni-modal and multi-modal optimization problems. The first modified HS method is based on the fusion of the HS and differential evolution (DE) technique, namely, HS-DE. The DE is employed here to optimize the members of the HS memory. The second modified...
The performance of evolutionary algorithms in optimization is tightly coupled to the computational effort required by the evaluation of the objective function. If the objective function is too expensive to evaluate, then, the elaboration of the procedures of the search algorithm alone may not result in the required improvement in algorithm's performance. However, if there is a way to speed up or decrease...
This paper proposes a hybrid optimization method based on the ant colony and clonal selection algorithms, in which the cloning and mutation operations are embedded in the ant colony to enhance its search capability. The novel algorithm is employed to deal with a few benchmark optimization problems under both static and dynamic environments. Simulation results demonstrate the remarkable advantages...
In this paper, we employ the clonal optimization method to optimize the detectors in the negative selection algorithm (NSA). Taking advantage of the clonal optimization strategy, the NSA detectors can be optimized for anomaly detection. A new motor fault detection scheme using our NSA is also discussed. We demonstrate the efficiency of the proposed approach with an example of bearings fault detection.
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