Hybrid and adaptive harmony search algorithm for optimizing energy efficiency in VMP problem in cloud environment

Data Center energy usage has risen dramatically because of the rapid growth and demand for cloud computing. This excessive energy usage is a challenge from an economic and environmental point. Virtual Machine Placement (VMP) along with virtualization technologies is widely used to manage power utilization in data centers. The assignment of virtual machines to physical machines affects energy consumption. VMP is a process of mapping VMs onto a set of PMs in a data center to minimize total power consumption and maximize resource utilization. The VMP is an NP-hard problem due to its constraints and huge combinations. In this paper, we formulated the problem as a single objective optimization problem in which the objective is to minimize the energy consumption in cloud data centers. The main contribution of this paper is hybrid and adaptive harmony search algorithm for optimal placements of VMs to PMs. HSA with adaptive PAR settings, simulated annealing and local search strategy aims at minimizing energy consumption in cloud data centers with satisfying given constraints. Experiments are conducted to validate the performance of these variations. Results show that these hybrid HSA variations produce better results than basic HSA and adaptive HSA. Hybrid HS with simulated annealing, and local search strategy gives better results than other variants for 80 percent datasets.

Improved Sparrow Search Algorithm Based on Iterative Local Search

This paper solves the shortcomings of sparrow search algorithm in poor utilization to the current individual and lack of effective search, improves its search performance, achieves good results on 23 basic benchmark functions and CEC 2017, and effectively improves the problem that the algorithm falls into local optimal solution and has low search accuracy. This paper proposes an improved sparrow search algorithm based on iterative local search (ISSA). In the global search phase of the followers, the variable helix factor is introduced, which makes full use of the individual’s opposite solution about the origin, reduces the number of individuals beyond the boundary, and ensures the algorithm has a detailed and flexible search ability. In the local search phase of the followers, an improved iterative local search strategy is adopted to increase the search accuracy and prevent the omission of the optimal solution. By adding the dimension by dimension lens learning strategy to scouters, the search range is more flexible and helps jump out of the local optimal solution by changing the focusing ability of the lens and the dynamic boundary of each dimension. Finally, the boundary control is improved to effectively utilize the individuals beyond the boundary while retaining the randomness of the individuals. The ISSA is compared with PSO, SCA, GWO, WOA, MWOA, SSA, BSSA, CSSA, and LSSA on 23 basic functions to verify the optimization performance of the algorithm. In addition, in order to further verify the optimization performance of the algorithm when the optimal solution is not 0, the above algorithms are compared in CEC 2017 test function. The simulation results show that the ISSA has good universality. Finally, this paper applies ISSA to PID parameter tuning and robot path planning, and the results show that the algorithm has good practicability and effect.

An Effective Memetic Algorithm for the Distributed Integrated Scheduling of Tree-Structured Products

Aiming at the distributed integrated scheduling of complex products with tree structure, a memetic algorithm-based distributed integrated scheduling algorithm is proposed. Based on the framework of the memetic algorithm, the algorithm uses a distributed estimation algorithm for global search and performs a local search strategy based on the critical operation set for the current optimal solution obtained in each evolutionary generation. A bi-chain-based individual representation method is presented and a simple greedy insertion-based decoding method is given; two position-based probability models are built, which are used to describe the distribution of the operation priority and factory assignment, respectively. Based on the designed probability models, two learning-based updating mechanisms and an improved sampling method are given, which ensures that the population evolves towards a promising region. In order to enhance the searchability for the superior solutions, nine disturbance operators based on the critical operation set are presented. The parameters are determined by the design-of-experiment (DOE) test, and the effectiveness of the proposed algorithm is verified by comparative experiments.

A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling

This paper studies the generalized normal distribution algorithm (GNDO) performance for tackling the permutation flow shop scheduling problem (PFSSP). Because PFSSP is a discrete problem and GNDO generates continuous values, the largest ranked value rule is used to convert those continuous values into discrete ones to make GNDO applicable for solving this discrete problem. Additionally, the discrete GNDO is effectively integrated with a local search strategy to improve the quality of the best-so-far solution in an abbreviated version of HGNDO. More than that, a new improvement using the swap mutation operator applied on the best-so-far solution to avoid being stuck into local optima by accelerating the convergence speed is effectively applied to HGNDO to propose a new version, namely a hybrid-improved GNDO (HIGNDO). Last but not least, the local search strategy is improved using the scramble mutation operator to utilize each trial as ideally as possible for reaching better outcomes. This improved local search strategy is integrated with IGNDO to produce a new strong algorithm abbreviated as IHGNDO. Those proposed algorithms are extensively compared with a number of well-established optimization algorithms using various statistical analyses to estimate the optimal makespan for 41 well-known instances in a reasonable time. The findings show the benefits and speedup of both IHGNDO and HIGNDO over all the compared algorithms, in addition to HGNDO.

A Novel BBO Algorithm Based on Local Search and Nonuniform Variation for Iris Classification

In order to improve the iris classification rate, a novel biogeography-based optimization algorithm (NBBO) based on local search and nonuniform variation was proposed in this paper. Firstly, the linear migration model was replaced by a hyperbolic cotangent model which was closer to the natural law. And, the local search strategy was added to traditional BBO algorithm migration operation to enhance the global search ability of the algorithm. Then, the nonuniform variation was introduced to enhance the algorithm in the later iteration. The algorithm could achieve a stronger iris classifier by lifting weaker similarity classifiers during the training stage. On this base, the convergence condition of NBBO was proposed by using the Markov chain strategy. Finally, simulation results were given to demonstrate the effectiveness and efficiency of the proposed iris classification method.

Pallet Scheduling Models Under Deterministic and Non-Deterministic Scenarios Using a Hybrid GA Method

This study examines the pallet scheduling problem considering random demands under the novel pallet operation mechanism by resources sharing among the pallet sharing system. Two nonlinear integer pallet scheduling models under deterministic and non-deterministic environment are formulated in terms of the pallet demand variable. To solve the pallet programming model, the hybrid genetic algorithm (HGA) integrating local search strategy is designed to derive the optimal pallet scheduling solution. Besides, the fixed sample size sampling strategy is employed to deal with the uncertain demand during the non-deterministic programming model, realized by the Monte Carlo simulation. The two models can assist decision makers arrange a scientific pallet scheduling solution under deterministic and non-deterministic atmosphere. Finally, the numerical case is implemented to testify the effectiveness of the two models and efficiency of the hybrid algorithms.