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This paper proposes a novel population based optimization algorithm called Andean Condor Algorithm (ACA) for solving cell formation problems. The ACA metaheuristic is inspired by the movement pattern of the Andean Condor when it searches for food. This pattern of movement corresponds to the flight distance traveled by the Andean Condor from its nest to the place where food is found. This distance varies depending on the seasons of the year. The ACA metaheuristic presents a balance of its population through a performance indicator based on the average quality of the population's fitness. This balance determines the number of Andean Condors that will perform an exploration or intensification movements. ACA metaheuristics have a flexible design. It allows to easily integrate specific heuristics according to the optimization problem to be solved. Two types of computational experiments have been performed. According to the results obtained it has been possible to determine that ACA is an algorithm with an outstanding RPD% in relation to the algorithms BAT, MBO and PSO, robust and with a convergence which tends not to be trapped in the local optimums. Besides, according to the non-parametric multiple comparison, results have been obtained in which the ACA metaheuristic has significant differences in relation to the BAT, MBO and PSO algorithms. [ABSTRACT FROM AUTHOR]

The conserved regions between genomic sequences extracted from the different species give important information about the complex regulatory mechanisms of the transcription and translation processes. However, identification of these regulatory regions that are short DNA segments and called motifs is a major challenge in bioinformatics. With the avalanche of the newly sequenced genomic data and our evolving understanding of the characteristics of the regulatory mechanisms, there is still a need for developing fast and accurate motif discovery techniques or considerably refinement on the existing models. In this paper, we presented two different Artificial Bee Colony algorithm based motif discovery techniques and investigated their serial and parallelized implementations. Experimental studies on the three real data sets showed that the proposed methods outperformed other metaheuristics in terms of similarity values of the predicted motifs. [ABSTRACT FROM AUTHOR]