Bee Colony Optimization Algorithm for Routing and Wavelength Assignment Based on Directional Guidance in Satellite Optical Networks

With the development of satellite communication,in order to solve the problems of shortage of on-board resources and refinement of delay requirements to improve the communication performance of satellite optical networks,this paper proposes a bee colony optimization algorithm for routing and wavelength assignment based on directional guidance(DBCO-RWA)in satellite optical networks.In D-BCORWA,directional guidance based on relative position and link load is defined,and then the link cost function in the path search stage is established based on the directional guidance factor.Finally,feasible solutions are expanded in the global optimization stage.The wavelength utilization,communication success probability,blocking rate,communication hops and convergence characteristic are simulated.The results show that the performance of the proposed algorithm is improved compared with existing algorithms.

Membrane-inspired quantum bee colony optimization and its applications for decision engine

In order to effectively solve combinatorial optimization problems,a membrane-inspired quantum bee colony optimization(MQBCO)is proposed for scientific computing and engineering applications.The proposed MQBCO algorithm applies the membrane computing theory to quantum bee colony optimization(QBCO),which is an effective discrete optimization algorithm.The global convergence performance of MQBCO is proved by Markov theory,and the validity of MQBCO is verified by testing the classical benchmark functions.Then the proposed MQBCO algorithm is used to solve decision engine problems of cognitive radio system.By hybridizing the QBCO and membrane computing theory,the quantum state and observation state of the quantum bees can be well evolved within the membrane structure.Simulation results for cognitive radio system show that the proposed decision engine method is superior to the traditional intelligent decision engine algorithms in terms of convergence,precision and stability.Simulation experiments under different communication scenarios illustrate that the balance between three objective functions and the adapted parameter configuration is consistent with the weights of three normalized objective functions.

A Novel Improved Artificial Bee Colony and Blockchain-Based Secure Clustering Routing Scheme for FANET

Flying Ad hoc Network(FANET)has drawn significant consideration due to its rapid advancements and extensive use in civil applications.However,the characteristics of FANET including high mobility,limited resources,and distributed nature,have posed a new challenge to develop a secure and ef-ficient routing scheme for FANET.To overcome these challenges,this paper proposes a novel cluster based secure routing scheme,which aims to solve the routing and data security problem of FANET.In this scheme,the optimal cluster head selection is based on residual energy,online time,reputation,blockchain transactions,mobility,and connectivity by using Improved Artificial Bee Colony Optimization(IABC).The proposed IABC utilizes two different search equations for employee bee and onlooker bee to enhance convergence rate and exploitation abilities.Further,a lightweight blockchain consensus algorithm,AI-Proof of Witness Consensus Algorithm(AI-PoWCA)is proposed,which utilizes the optimal cluster head for mining.In AI-PoWCA,the concept of the witness for block verification is also involved to make the proposed scheme resource efficient and highly resilient against 51%attack.Simulation results demonstrate that the proposed scheme outperforms its counterparts and achieves up to 90%packet delivery ratio,lowest end-to-end delay,highest throughput,resilience against security attacks,and superior in block processing time.

Threshold Selection Method Based on Reciprocal Gray Entropy and Artificial Bee Colony Optimization

Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.

Modelling Dry Port Systems in the Framework of Inland Waterway Container Terminals

Overcoming the global sustainability challenges of logistics requires applying solutions that minimize the negative effects of logistics activities.The most efficient way of doing so is through intermodal transportation(IT).Current IT systems rely mostly on road,rail,and sea transport,not inland waterway transport.Developing dry port(DP)terminals has been proven as a sustainable means of promoting and utilizing IT in the hinterland of seaport container terminals.Conventional DP systems consolidate container flows from/to seaports and integrate road and rail transportation modes in the hinterland which improves the sustainability of the whole logistics system.In this article,to extend literature on the sustainable development of different categories of IT terminals,especially DPs,and their varying roles,we examine the possibility of developing DP terminals within the framework of inland waterway container terminals(IWCTs).Establishing combined road–rail–inland waterway transport for observed container flows is expected to make the IT systems sustainable.As such,this article is the first to address the modelling of such DP systems.After mathematically formulating the problem of modelling DP systems,which entailed determining the number and location of DP terminals for IWCTs,their capacity,and their allocation of container flows,we solved the problem with a hybrid metaheuristic model based on the Bee Colony Optimisation(BCO)algorithmand themeasurement of alternatives and ranking according to compromise solution(i.e.,MARCOS)multi-criteria decision-making method.The results from our case study of the Danube region suggest that planning and developingDP terminals in the framework of IWCTs can indeed be sustainable,as well as contribute to the development of logistics networks,the regionalisation of river ports,and the geographic expansion of their hinterlands.Thus,the main contributions of this article are in proposing a novel DP concept variant,mathematically formulating the problems of its modelling,and developing an encompassing hybrid metaheuristic approach for treating the complex nature of the problem adequately.

Mobility Aware Zone-Based Routing in Vehicle Ad hoc Networks Using Hybrid Metaheuristic Algorithm

Vehicle Ad hoc Networks(VANETs)have high mobility and a rando-mized connection structure,resulting in extremely dynamic behavior.Several challenges,such as frequent connection failures,sustainability,multi-hop data transfer,and data loss,affect the effectiveness of Transmission Control Protocols(TCP)on such wireless ad hoc networks.To avoid the problem,in this paper,mobility-aware zone-based routing in VANET is proposed.To achieve this con-cept,in this paper hybrid optimization algorithm is presented.The hybrid algo-rithm is a combination of Ant colony optimization(ACO)and artificial bee colony optimization(ABC).The proposed hybrid algorithm is designed for the routing process which is transmitting the information from one place to another.The optimal routing process is used to avoid traffic and link failure.Thefitness function is designed based on Link stability and Residual energy.The validation of the proposed algorithm takes solution encoding,fitness calculation,and updat-ing functions.To perform simulation experiments,NS2 simulator software is used.The performance of the proposed approach is analyzed based on different metrics namely,delivery ratio,delay time,throughput,and overhead.The effec-tiveness of the proposed method compared with different algorithms.Compared to other existing VANET algorithms,the hybrid algorithm has proven to be very efficient in terms of packet delivery ratio and delay.

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.

Applications of artificial intelligence in power system operation, control and planning: a review

As different artificial intelligence(AI)techniques continue to evolve,power systems are undergoing significant technological changes with the primary goal of reducing computational time,decreasing utility and consumer costs and ensuring the reliable operation of an electrical power system.AI techniques compute large amounts of data at a faster speed than numerical optimization methods with higher processing speeds.With these features,AI techniques can further automate and increase the performance of power sys-tems.This paper presents a comprehensive overview of diverse AI techniques that can be applied in power system operation,control and planning,aiming to facilitate their various applications.We explained how AI can be used to resolve system frequency changes,maintain the voltage profile to minimize transmission losses,reduce the fault rate and minimize reactive current in distributed sys-tems to increase the power factor and improve the voltage profile.