This paper explores biogeography-based learning particle swarm optimization (BLPSO). Specifically, based on migration of biogeography-based optimization (BBO), a new biogeography-based learning strategy is proposed for particle swarm optimization (PSO), whereby each particle updates itself by using the combination of its own personal best position and personal best positions of all other particles through the BBO migration. The proposed BLPSO is thoroughly evaluated on 30 benchmark functions from CEC 2014. The results are very promising, as BLPSO outperforms five well-established PSO variants and several other representative evolutionary algorithms.
This paper proposes a novel scheme of nonuniform discretizetion-based control vector parameterization (ndCVP, for short) for dynamic optimization problems (DOPs) of industrial processes. In our ndCVP scheme, the time span is partitioned into a multitude of uneven intervals, and incremental time parameters are encoded, along with the control parameters, into the individual to be optimized. Our coding method can avoid handling complex ordinal constraints. It is proved that ndCVP is a natural generalization of uniform discretization-based control vector parameterization (udCVP). By integrating ndCVP into hybrid gradient particle swarm optimization (HGPSO), a new optimization method, named ndCVP-HGPSO for short, is formed. By application in four classic DOPs, simulation results show that ndCVP-HGPSO is able to achieve similar or even better performances with a small number of control intervals; while the computational overheads are acceptable. Furthermore, ndCVP and udCVP are compared in terms of two situations: given the same number of control intervals and given the same number of optimization variables. The results show that ndCVP can achieve better performance in most cases.Note to Practitioners-This paper was motivated by the problems of operational optimization in industrial processes. Most problems in industrial processes are dynamic optimization problems, and the aim is to optimize control variables like feeding rates, heating/cooling for reaction over a time span. Most of existing optimization methods use uniform discretization-based control vector parameterization (udCVP for short), in which the time span for optimization is partitioned into a multitude of even intervals and thus merely the control parameters need to be optimized. In this paper, we propose a novel scheme of nonuniform-based discretization control vector parameterization (ndCVP), in which the time span is unevenly partitioned, and the incremental time parameters are encoded into the individual to be optimized as well. It is proved that ndCVP is a natural generalization of udCVP. Moreover, by integrating ndCVP into hybrid gradient particle swarm optimization (HGPSO), a new Manuscript optimization method, named ndCVP-HGPSO for short, is formed for DOPs. Numeric simulations show that ndCVP-HGPSO is a highly competitive method for DOPs, because ndCVP can adjust the time intervals to generate high precision results in compliance with the shape of control trajectories.Index Terms-Dynamic optimization, hybrid gradient particle swarm optimization, nonuniform discretizetion-based control vector parameterization.
Resource over provisioning in cloud computing consumes energy excessively. Energy-aware dynamic virtual machine consolidation (DVMC) reduces energy consumption without compromising service level agreement. In this paper, we put forward a new framework of DVMC for green cloud computing. In particular, we propose a new virtual machine (VM) placement policy, namely, space aware best fit decreasing (SABFD) and a new migration VM selection policy, namely, high CPU utilization-based migration VM selection (called HS). Thorough simulations are carried out to evaluate the performances of different energy-aware DVMC plans based on real-world workload traces, with DVMC plans as various combinations of host overload detection, migration VM selection, and VM placement policies. The simulation results show that DVMC plans with SABFD policy or with HS policy outperforms alternative DVMC plans. What is more, a DVMC plan with both SABFD and HS policies makes the best performance. INDEX TERMS Cloud computing, green cloud computing, dynamic virtual machine consolidation, virtual machine placement, cloud datacenter.
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