This paper presents a two-level protein folding optimization on a three-dimensional AB off-lattice model. The first level is responsible for forming conformations with a good hydrophobic core or a set of compact hydrophobic amino acid positions. These conformations are forwarded to the second level, where an accurate search is performed with the aim of locating conformations with the best energy value. The optimization process switches between these two levels until the stopping condition is satisfied. An auxiliary fitness function was designed for the first level, while the original fitness function is used in the second level. The auxiliary fitness function includes expression about the quality of the hydrophobic core. This expression is crucial for leading the search process to the promising solutions that have a good hydrophobic core and, consequently, improves the efficiency of the whole optimization process. Our differential evolution algorithm was used for demonstrating the efficiency of the two-level optimization. It was analyzed on well-known amino acid sequences that are used frequently in the literature. The obtained experimental results show that the employed two-level optimization improves the efficiency of our algorithm significantly, and that the proposed algorithm is superior to other state-of-the-art algorithms.
To improve the Artificial Bee Colony (ABC) algorithm convergence performance, an internal feedback strategy based ABC is proposed in @cite_3 . In this strategy, internal states are used fully in each iteration, to guide the subsequent searching process. @cite_7 , a chaotic ABC algorithm was introduced. This algorithm combines the artificial bee colony and the chaotic search algorithm to avoid the premature convergence. If the algorithm was trapped into the local optimum, it uses a chaotic search algorithm to prevent stagnation. A balance-evolution artificial bee colony algorithm was presented in @cite_5 @cite_13 . During the optimization process, this algorithm uses convergence information to manipulate adaptively between the local and global searches.
@cite_5 @cite_13 @cite_7 @cite_3
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Prediction of the three-dimensional structure of a protein from its amino acid sequence can be considered as a global optimization problem. In this paper, the Chaotic Artificial Bee Colony (CABC) algorithm was introduced and applied to 3D protein structure prediction. Based on the 3D off-lattice AB model, the CABC algorithm combines global search and local search of the Artificial Bee Colony (ABC) algorithm with the chaotic search algorithm to avoid the problem of premature convergence and easily trapping the local optimum solution. The experiments carried out with the popular Fibonacci sequences demonstrate that the proposed algorithm provides an effective and high-performance method for protein structure prediction. Abstract Protein structure prediction is a fundamental issue in the field of computational molecular biology. In this paper, the AB off-lattice model is adopted to transform the original protein structure prediction scheme into a numerical optimization problem. We present a balance-evolution artificial bee colony (BE-ABC) algorithm to address the problem, with the aim of finding the structure for a given protein sequence with the minimal free-energy value. This is achieved through the use of convergence information during the optimization process to adaptively manipulate the search intensity. Besides that, an overall degradation procedure is introduced as part of the BE-ABC algorithm to prevent premature convergence. Comprehensive simulation experiments based on the well-known artificial Fibonacci sequence set and several real sequences from the database of Protein Data Bank have been carried out to compare the performance of BE-ABC against other algorithms. Our numerical results show that the BE-ABC algorithm is able to outperform many state-of-the-art approaches and can be effectively employed for protein structure optimization. An adaptive parallel ant colony algorithm (PACO) is presented. In the algorithm, we propose a strategy for information exchange between processors which make each processor choose its partner to communicate and update the pheromone adaptively. We also propose a method of adjusting the time interval of information exchange adaptively according to the diversity of the solutions so as to increase the ability of search and avoid early convergence. Experimental results show that our algorithm PACO has high convergence speed, high speedup and efficiency. This paper proposes evolutionary wall-following control of a mobile robot using an interval type-2 fuzzy controller (IT2FC) with species-differential-evolution-activated continuous ant colony optimization (SDE-CACO). Both the position and speed of a mobile robot are controlled by using two IT2FCs to improve noise resistance ability. A new cost function is defined to accurately evaluate the wall-following performance of an evolutionary IT2FC. A two-stage training approach is proposed that learns a position IT2FC followed by a speed IT2FC to optimize both the wall-following accuracy and the moving speed. The proposed learning approach avoids the time consuming task of the exhaustive collection of supervised input-output training pairs. All fuzzy rules are generated online using a clustering-based approach during the evolutionary learning process. All of the free parameters in an online-generated IT2FC are optimized using SDE-CACO, in which an SDE mutation operation is incorporated within a continuous ACO to improve its explorative ability. The proposed SDE-CACO is compared with various population-based optimization algorithms to demonstrate its efficiency and effectiveness in the wall-following control problem. This study also includes experiments that demonstrate wall-following control utilizing a real mobile robot.