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.
A multi-agent simulated annealing algorithm with parallel adaptive multiple sampling was proposed in @cite_32 . A parallel elitist sampling strategy was used to overcome the inherent serialization of the original simulated annealing algorithm. This strategy additionally provides benefit information, that is helpful for the convergence. An adaptive neighborhood search and a parallel multiple move mechanism were also used inside the algorithm to improve the algorithm's efficiency. In this work, the following methods were analyzed for generating candidate solutions: Simulated annealing, a mutation from the differential evolution algorithm, and the velocity and position update from the particle swarm optimization.
@cite_32
2762084119 2785967771 2157326321 2540076116
Abstract Protein structure prediction (PSP) with ab initio model keeps a challenge in bioinformatics on account of high computational complexity. To solve the problem within a limited time and resource, the parallel capacity and search efficiency are of significance for a successful algorithm. Traditional simulated annealing (SA) algorithm is extremely slow in convergence, and the implementation and efficiency of parallel SA algorithms are typically problem-dependent. To overcome such intrinsic limitation, in this paper a multi-agent simulated annealing (MASA) algorithm with parallel adaptive multiple sampling (MASA-PAMS) that features better search ability is proposed. The MASA-PAMS contains two main issues. First, a parallel elitist sampling strategy overcomes the inherent serialization of the original SA, provides benefit information for the iteration which is helpful for the convergence. Then an adaptive neighborhood search and a parallel multiple move mechanism displace the random sampling scheme which balance the intensification and diversification iteratively. Conducted experiments with 2D and 3D AB off-lattice models indicate that the MASA-PAMS performs better than, or at least comparable to other MASAs with different sampling schemes and several state-of-the-art algorithms for PSP. We consider a team of reinforcement learning agents that concurrently learn to operate in a common environment. We identify three properties - adaptivity, commitment, and diversity - which are necessary for efficient coordinated exploration and demonstrate that straightforward extensions to single-agent optimistic and posterior sampling approaches fail to satisfy them. As an alternative, we propose seed sampling, which extends posterior sampling in a manner that meets these requirements. Simulation results investigate how per-agent regret decreases as the number of agents grows, establishing substantial advantages of seed sampling over alternative exploration schemes. S U M M A R Y Non-linear inverse problems in the geosciences often involve probabilistic sampling of multimodal density functions or global optimization and sometimes both. Efficient algorithmic tools for carrying out sampling or optimization in challenging cases are of major interest. Here results are presented of some numerical experiments with a technique, known as Parallel Tempering, which originated in the field of computational statistics but is finding increasing numbers of applications in fields ranging from Chemical Physics to Astronomy. To date, experience in use of Parallel Tempering within earth sciences problems is very limited. In this paper, we describe Parallel Tempering and compare it to related methods of Simulated Annealing and Simulated Tempering for optimization and sampling, respectively. A key feature of Parallel Tempering is that it satisfies the detailed balance condition required for convergence of Markov chain Monte Carlo (McMC) algorithms while improving the efficiency of probabilistic sampling. Numerical results are presented on use of Parallel Tempering for trans-dimensional inversion of synthetic seismic receiver functions and also the simultaneous fitting of multiple receiver functions using global optimization. These suggest that its use can significantly accelerate sampling algorithms and improve exploration of parameter space in optimization. Parallel Tempering is a meta-algorithm which may be used together with many existing McMC sampling and direct search optimization techniques. It’s generality and demonstrated performance suggests that there is significant potential for applications to both sampling and optimization problems in the geosciences. Consider a target search problem on a unit interval where at any given time an agent can choose a region to probe into for the presence of the target in that region. The measurement noise is assumed to be increasing with the size of the search region the agent chooses. In this paper, a single-phase sequential and adaptive search algorithm is proposed and shown to achieve the best possible targeting rate and error exponent among all adaptive search algorithms. The proposed algorithm simply adopts a low complexity sorting operation on the posterior of the target and then pick up locations with larger posterior until the probability that the search region contains the target is closest to half.