Identifying the top-K frequent items is one of the most common and important operations in large data processing systems. As a result, several solutions have been proposed to solve this problem approximately. In this paper, we identify that in modern distributed settings with both multi-node as well as multi-core parallelism, existing algorithms, although theoretically sound, are suboptimal from the performance perspective. In particular, for identifying top-K frequent items, Count-Min Sketch (CMS) has fantastic update time but lack the important property of reducibility which is needed for exploiting available massive data parallelism. On the other end, popular Frequent algorithm (FA) leads to reducible summaries but the update costs are significant. In this paper, we present Topkapi, a fast and parallel algorithm for finding top-K frequent items, which gives the best of both worlds, i.e., it is reducible as well as efficient update time similar to CMS. Topkapi possesses strong theoretical guarantees and leads to significant performance gains due to increased parallelism, relative to past work.

This article proposes a multipopulation-based adaptive differential evolution (DE) algorithm to solve dynamic optimization problems (DOPs) in an efficient way. The algorithm uses Brownian and adaptive quantum individuals in conjunction with the DE individuals to maintain the diversity and exploration ability of the population. This algorithm, denoted as dynamic DE with Brownian and quantum individuals (DDEBQ), uses a neighborhood-driven double mutation strategy to control the perturbation and thereby prevents the algorithm from converging too quickly. In addition, an exclusion rule is used to spread the subpopulations over a larger portion of the search space as this enhances the optima tracking ability of the algorithm. Furthermore, an aging mechanism is incorporated to prevent the algorithm from stagnating at any local optimum. The performance of DDEBQ is compared with several state-of-the-art evolutionary algorithms using a suite of benchmarks from the generalized dynamic benchmark generator (GDBG) system used in the competition on evolutionary computation in dynamic and uncertain environments, held under the 2009 IEEE Congress on Evolutionary Computation (CEC). The simulation results indicate that DDEBQ outperforms other algorithms for most of the tested DOP instances in a statistically meaningful way.

Real world optimization problems are used to judge the performance of any Evolutionary Algorithm (EA) over real world applications. This is why the performance of any EA over the real world optimization problems is very important for judging its efficiency. In this work, we represent a multi population based memetic algorithm CDELS. It is hybridization of a competitive variant of Differential Evolution (DE) and a Local Search method. As the number of optima is large in this case, we have also incorporated a distant search method to hop from one optima to other optima. However, it is well known that DE has fast but less reliable convergence property. To overcome this limitation, a hybrid mutation strategy is developed to balance between exploration and thorough search. In addition, a proximity checking method is applied to distribute the subpopulations over a larger portion of the search space as this further enhances the searching ability of the algorithm. The performance of CDELS algorithm is evaluated on the test suite provided for the Competition on Testing Evolutionary Algorithms on Real-world Numerical Optimization Problems in the 2011 IEEE Congress on Evolutionary Computation and the simulation results are shown in this paper.

Work load optimization in power generation plants is of practical importance in carbon constrained power industry. The main objective of the coal-fired power generation workload optimization is to minimize fuel consumption while maintaining the desired output and to maintain NOxemission within the environmental license limit. In this article, we represent an efficient Memetic Algorithm (MA) with a constraint handling method for the power generation loading optimization. This MA is developed by combining a competitive variant of Deferential Evolution (DE) and Simplex method. The proposed approach incorporates the constraint handling method to modify the selection rule which guides the search process in better direction. The simulation results based on a coal-fired power plant clearly indicate that our proposed method is very effective and it shows great computational efficiency in power generation workload optimization.

In recent years, there has been a growing interest in real world application of heuristic methods. Memetic Algorithm (MA) is one of such effective heuristics. In this paper, we represent an efficient MA for determining optimal proportional-integral-derivative (PID) controller parameters of an AVR system. This MA is developed by combining a competitive variant of Deferential Evolution (DE) and a Local Search method. The proposed method has excellent features, such as easy implementation, stable convergence characteristic and good computational efficiency. Fast tuning of PID controller parameters results in far better performance of the controller. Performance of our proposed algorithm is compared with other famous heuristics and the simulation results clearly indicate that our proposed approach is indeed more efficient and robust in improving the step response of an AVR system.