Concrete Physics Method for Solving NP hard Problem

With a NP hard problem given, we may find a equivalent physical world. The rule of the changing of the physical states is simply the algorithm for solving the original NP hard problem .It is the most natural algorithm for solving NP hard problems. In this paper we deal with a famous example , the well known NP hard problem——Circles Packing. It shows that our algorithm is dramatically very efficient. We are inspired that, the concrete physics algorithm will always be very efficient for NP hard problem.

Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

Software-defined networking(SDN)algorithms are gaining increas-ing interest and are making networks flexible and agile.The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components,enabling flexible and dynamic network management.A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers.The deployment of the controller—that is,the controller placement problem(CPP)—becomes a vital model challenge.Through the advancements of blockchain technology,data integrity between nodes can be enhanced with no requirement for a trusted third party.Using the lat-est developments in blockchain technology,this article designs a novel sea turtle foraging optimization algorithm for the controller placement problem(STFOA-CPP)with blockchain-based intrusion detection in an SDN environ-ment.The major intention of the STFOA-CPP technique is the maximization of lifetime,network connectivity,and load balancing with the minimization of latency.In addition,the STFOA-CPP technique is based on the sea turtles’food-searching characteristics of tracking the odour path of dimethyl sulphide(DMS)released from food sources.Moreover,the presented STFOA-CPP technique can adapt with the controller’s count mandated and the shift to controller mapping to variable network traffic.Finally,the blockchain can inspect the data integrity,determine significantly malicious input,and improve the robust nature of developing a trust relationship between sev-eral nodes in the SDN.To demonstrate the improved performance of the STFOA-CPP algorithm,a wide-ranging experimental analysis was carried out.The extensive comparison study highlighted the improved outcomes of the STFOA-CPP technique over other recent approaches.

Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC

A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional （3D） stacked system-on-chip （SoC） by hybridizing grey wolf optimization with differential evo- lution （HGWO）. Because basic grey wolf optimization （GWO） is easy to fall into stagnation when it carries out the operation of at- tacking prey, and differential evolution （DE） is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE＇s strong searching ability. The proposed algorithm can accele- rate the convergence speed of GWO and improve its performance. Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.

Intelligent Deer Hunting Optimization Based Grid Scheduling Scheme

The grid environment is a dynamic,heterogeneous,and changeable computing system that distributes various services amongst different clients.To attain the benefits of collaborative resource sharing in Grid computing,a novel and proficient grid resource management system(RMS)is essential.Therefore,detection of an appropriate resource for the presented task is a difficult task.Several scientists have presented algorithms for mapping tasks to the resource.Few of them focus on fault tolerance,user fulfillment,and load balancing.With this motivation,this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm(DHOA),called IGSS-DHOA which schedules in such a way that the makespan gets minimized in the grid platform.The IGSS-DHOA technique is mainly based on the hunting nature of humans toward deer.It also derives an objective function with candidate solution(schedule)as input and the outcome is the makespan value denoting the quality of the candidate solution.The simulation results highlighted the supremacy of the IGSS-DHOA technique over the recent state of art techniques with the minimal average processing cost of 31717.9.