multi objective algorithm

Nowadays, cloud computing industry has been transformed to a new supply chain between cloud service providers and service requesters. To this end, cloud service provisioning in datacenters is procured via virtualization platforms in which it can potentially increase the utilization of computing resources at infrastructure level. Inefficient virtual machine placement (VMP) schemes lead lower system utilization, increase of resource dissipation, and high rate of power consumption. Therefore, this paper formulates VMP problem on physical machines of cloud datacenters to a multi-objective optimization problem with minimization of both power consumption and resource dissipation perspectives which is computationally NP-Hard. Since the most meta-heuristic algorithms are designed for continuous optimization problems and are also susceptible to get stuck in local optimum, to figure out this combinatorial problem an optimization algorithm based on simulated annealing algorithm commensurate with discrete search space of stated problem is extended, so that the possibility of getting stuck in local optimum is reduced. To validate the proposed approach, several scenarios are introduced and conducted. Reported results from simulation of different scenarios show that the proposed approach outperforms against other existing approaches in terms of reduction in power consumption, resource dissipation, and the number of active server in use.

One of the important issues in coordination of protective relays is minimization of the time interval between the operation of main and backup overcurrent (OC) relays. Overcurrent relays coordination problem due to the large number of variables and the nature of the objective functions can be introduced as a complex optimization problem that necessitates the need for an efficient optimization method with appropriate accuracy and speed. Given that for the realization of protective purposes including increased relays operation speed, selectivity, support, reliability and stability, different objective functions can be described, therefore, providing a multi-objective mathematical problem in optimizing protective problems may be necessary. In this regard, this article due to the capabilities of the MOPSO, proposes a multi-objective optimization structure, in which the SQP by increasing the speed and not increasing the search space is added to MOPSO. In this paper, several objective functions are proposed based on protection goals and optimal adjustment points have been extracted using the proposed multi-objective MOPSO-SQP. The simulations have been implemented and carefully analyzed on several typical power systems. The simulation results confirm the efficiency of the proposed algorithm in ensuring the optimal coordination of protective overcurrent relays in power system.

In this paper, a hybrid multi-objective algorithm consisting of features of genetic and firefly algorithms is presented. The algorithm starts with a set of fireflies (particles) that are randomly distributed in the solution space; these particles converge to the optimal solution of the problem during the evolutionary stages. Then, a local search plan is presented and implemented for searching solution neighbors to improve the quality of global solutions. This part of the algorithm is used to search sparsely populated areas for finding the dominant solutions. To improve the algorithm, for each firefly some changes have been made on the criteria of determining the global optimal solution and doing local optimal solution; this leads to more uniformity of the Pareto curve and error reduction, as the experimental results show. The proposed algorithm is an extension of a basic algorithm.