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In this paper, we present a novel hybrid metaheuristic for the Knapsack Problem with Forfeits (KPF). KPF is a recently introduced generalization of the Knapsack Problem. In this variant, a penalty cost incurs whenever both items composing a so-called forfeit pair belong to the solution. Our proposed algorithm, called GA–CG Forfeits, combines the strengths of the Genetic and Carousel Greedy paradigms. In this work, we define the algorithm and compare it with two previously proposed heuristics on a set of benchmark instances. In these tests, GA–CG Forfeits provided significantly better solutions than the other two algorithms on all instances. [ABSTRACT FROM AUTHOR]

Today, there are a lot of useful algorithms for covering array (CA) generation, one of the branches of combinatorial testing. The major CA challenge is the generation of an array with the minimum number of test cases (efficiency) in an appropriate run-time (performance), for large systems. CA generation strategies are classified into several categories: computational and meta-heuristic, to name the most important ones. Generally, computational strategies have high performance and yield poor results in terms of efficiency, in contrast, meta-heuristic strategies have good efficiency and lower performance. Among the strategies available, some are efficient strategies but suffer from low performance; conversely, some others have good performance, but is not such efficient. In general, there is not a strategy that enjoys both above-mentioned metrics. In this paper, it is tried to combine the genetic algorithm and the Augmented Lagrangian Particle Swarm Optimization with Fractional Order Velocity to produce the appropriate test suite in terms of efficiency and performance. Also, a simple and effective minimizing function is employed to increase efficiency. The evaluation results show that the proposed strategy outperforms the existing approaches in terms of both efficiency and performance. [ABSTRACT FROM AUTHOR]

Cloud computing delivers practical solutions for long-term image archiving systems. Cloud data centers consume enormous amounts of electrical energy that increases their operational costs. This shows the importance of investing on energy consumption techniques. Dynamic placement of virtual machines to appropriate physical nodes using metaheuristic algorithms is among the methods of reducing energy consumption. In metaheuristic algorithms, there should be a balance between both exploration and exploitation aspects so that they can find better solutions in a search space. Exploration means looking for a solution in a wider area, while exploitation is producing new solutions from existence ones. Artificial bee colony optimization, which is a biological metaheuristic algorithm, is a sign-oriented approach. It has a strong exploration ability, but a relatively weaker exploitation power. On the other hand, tabu search is a popular algorithm that shows better exploitation in comparison with ABC. In this study, cloud computing environments are detailed with an allocation protocol for efficient energy and resource management. The technique of energy-aware allocation splits data centers (DCs) resources among client applications end routes to enhance energy efficacy of DCs and also achieves anticipated quality of service (QoS) for everyone. Heuristic protocols are exercised for optimizing the distribution of resources to upgrade the efficiency of DC. In the current paper, energy-aware resources allotment technique is employed and optimized in clouds via a new approach called Tabu Job Master (JM). Tabu JM claims the benefits of some variables and also rapid convergence speeds. Results are duly achieved for energy consumption—the count of virtual machines (VMs) migration and also makespan. The results shown by Tabu JM are benchmarked by using genetic algorithm (GA), artificial bee colony (ABC), ABC with crossover and technique of mutation, the basic tabu search techniques, and Tabu Job Master. [ABSTRACT FROM AUTHOR]

In this paper, multiplier-less nearly perfect reconstruction tree structured non-uniform filter banks (NUFB) are proposed. When sharp transition width filter banks are to be implemented, the order of the filters and hence the complexity will become very high. The filter banks employ an iterative algorithm which adjusts the cut off frequencies of the prototype filter, to reduce the amplitude distortion. It is found that the proposed design method, in which the prototype filter is designed by the frequency response masking method, gives better results when compared to the earlier reported results, in terms of the number of multipliers when sharp transition width filter banks are needed. To reduce the complexity and power consumption for hardware realization, a design method which makes the NUFB totally multiplier-less is also proposed in this paper. The NUFB is made multiplier-less by converting the continuous filter bank coefficients to finite precision coefficients in the signed power of two space. The filter bank with finite precision coefficients may lead to performance degradation. This calls for the use of suitable optimization techniques. The classical gradient based optimization techniques cannot be deployed here, because the search space consists of only integers. In this context, meta-heuristic algorithm is a good choice as it can be tailor made to suit the problem under consideration. Thus, this design method results in near perfect NUFBs which are simple and multiplier-less and have linear phase and sharp transition width with very low aliasing. Also, different non-uniform bands can be obtained from the tree structured filter bank by rearranging the branches. [ABSTRACT FROM AUTHOR]