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The paper presents a study on the efficiency of Ant Colony Communities (ACC) used to solve the Travelling Salesman Problem. The ACC is an approach to parallelize the Ant Colony Optimization algorithm (ACO). An ACC is made up of a Community Server that coordinates the work of a set ant colony clients. Each client implements a classical ACO algorithm. The individual colonies process cargos of data obtained from the server and send them back the as partial results. The paper presents a general description of the ACC concept and describes in details two ways of implementing it. The first one uses an inhomogeneous environment of traditional computers working in an asynchronous mode. The second one uses the homogenous Hadoop environment and the processing is done in a synchronized mode. The performance of the Communities is estimated by low level measures: their power and scalability. The high level measure deals with the length of obtained routes. The paper presents also the taxonomy of parallel implementations of the Ant Colony Optimization. [ABSTRACT FROM AUTHOR]

Consolidation of services is one of the key problems in cloud data centers. It consists of two separate but related issues: Virtual machine (VM) placement and VM migration problems. In this paper, a VMconsolidation scheme is proposed that turns the virtual machine consolidation (VMC) problem into a vector packing optimization problem based on deadlockfree migration (DFM) to minimize the energy consumptions. To solve this NP-hard and computationally infeasible for large data centers problem, a novel algorithm named Chicken Swarm Optimization based on deadlock-free migration (DFM-CSO) algorithm is proposed. The DFM-CSO algorithm is characterized by the 'one-step look-ahead with n-VMs migration in parallel (OSLA-NVMIP)' method, which carries out the VM migration validation and the rearrangement of target physical host, as well as records the migration order for each solution placement, so that VM transfer can be completed according to the migration sequence. The experimental results, for both real and synthetic datasets, show that the proposed algorithm with higher convergence rate is favourable in comparison with the other deadlock-free migration algorithms. [ABSTRACT FROM AUTHOR]

The particle swarm optimization algorithm (PSO) is not good at dealing with discrete optimization problems, and for the krill herd algorithm (KH), the ability of local search is relatively poor. In this paper, we optimized PSO by quantum behavior and optimized KH by simulated annealing, so a new hybrid algorithm, named the annealing krill quantum particle swarm optimization (AKQPSO) algorithm, is proposed, and is based on the annealing krill herd algorithm (AKH) and quantum particle swarm optimization algorithm (QPSO). QPSO has better performance in exploitation and AKH has better performance in exploration, so AKQPSO proposed on this basis increases the diversity of population individuals, and shows better performance in both exploitation and exploration. In addition, the quantum behavior increased the diversity of the population, and the simulated annealing strategy made the algorithm avoid falling into the local optimal value, which made the algorithm obtain better performance. The test set used in this paper is a classic 100-Digit Challenge problem, which was proposed at 2019 IEEE Congress on Evolutionary Computation (CEC 2019), and AKQPSO has achieved better performance on benchmark problems. [ABSTRACT FROM AUTHOR]