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Nowadays, the attraction of the optimization techniques based on artificial intelligence has increased the due to obtaining the its successful results on the difficult optimization problems in various areas. The artificial bee colony (ABC) algorithm based on swarm intelligence and the differential evolution (DE) algorithm improved by inspiring the natural biological evolution mechanism is the most...
This paper presents an opposition-based hybrid Artificial Bee Colony (ABC) with Differential Evolution (DE) algorithm for solving continuous problems. The proposed algorithm, called OABCDE, employs an efficient mutation operation of DE and a crossover-like mechanism to enhance the convergence of ABC without adding parameters. The opposition-based learning routine is periodically executed to prevent...
Clustering is an unsupervised classification method where objects in the unlabeled data set are classified on the basis of some similarity measure. The conventional partitional clustering algorithms, e.g., K-Means, K-Medoids have several disadvantages such as the final solution is dependent on initial solution, they easily stuck into local optima. The nature inspired population based global search...
The paper is introducing the principles of a new global optimization strategy, Imperialistic Strategy (IS), applied to the Continuous Global Optimization Problem (CGOP). Inspired from existing multi-population strategies, like the Island Model (IM) approaches to parallel Evolutionary Algorithms (EA) and the Imperialistic Competitive Algorithm (ICA), the proposed IS method is considered an optimization...
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