Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).

An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm Optimization

This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)algorithm.Image steganography,a technique of embedding hidden information in digital photographs,should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect.The contemporary methods of steganography are at best a compromise between these two.In this paper,we present our approach,entitled Ant Colony Optimization(ACO)-Least Significant Bit(LSB),which attempts to optimize the capacity in steganographic embedding.The approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte,both for integrity verification and the file checksumof the secret data.This approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale images.The ACO algorithm uses adaptive exploration to select some pixels,maximizing the capacity of data embedding whileminimizing the degradation of visual quality.Pheromone evaporation is introduced through iterations to avoid stagnation in solution refinement.The levels of pheromone are modified to reinforce successful pixel choices.Experimental results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30%in the embedding capacity compared with traditional approaches;the average Peak Signal to Noise Ratio(PSNR)is 40.5 dB with a Structural Index Similarity(SSIM)of 0.98.The approach also demonstrates very high resistance to detection,cutting down the rate by 20%.Implemented in MATLAB R2023a,the model was tested against one thousand publicly available grayscale images,thus providing robust evidence of its effectiveness.

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

Optimal Location and Sizing ofMulti-Resource Distributed Generator Based onMulti-Objective Artificial Bee Colony Algorithm

Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in the stability of DN operation.It is urgent to find a method that can effectively connect multi-energy DG to DN.photovoltaic(PV),wind power generation(WPG),fuel cell(FC),and micro gas turbine(MGT)are considered in this paper.A multi-objective optimization model was established based on the life cycle cost(LCC)of DG,voltage quality,voltage fluctuation,system network loss,power deviation of the tie-line,DG pollution emission index,and meteorological index weight of DN.Multi-objective artificial bee colony algorithm(MOABC)was used to determine the optimal location and capacity of the four kinds of DG access DN,and compared with the other three heuristic algorithms.Simulation tests based on IEEE 33 test node and IEEE 69 test node show that in IEEE 33 test node,the total voltage deviation,voltage fluctuation,and system network loss of DN decreased by 49.67%,7.47%and 48.12%,respectively,compared with that without DG configuration.In the IEEE 69 test node,the total voltage deviation,voltage fluctuation and system network loss of DN in the MOABC configuration scheme decreased by 54.98%,35.93%and 75.17%,respectively,compared with that without DG configuration,indicating that MOABC can reasonably plan the capacity and location of DG.Achieve the maximum trade-off between DG economy and DN operation stability.

A Distributed Ant Colony Optimization Applied in Edge Detection

With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, learning algorithms must maintain high speed and accuracy through economical means. Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization technique in a distributed framework provided by the Hadoop/Map-Reduce infrastructure is proposed to improve the edge detection capabilities. Moreover, a filtering technique is applied to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection. Close examinations of the implementation of the proposed algorithm are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.

An Effective Optimization Algorithm for Ant Colony Vehicular Congestion Management

Adaptability and dynamicity are special properties of social insects derived from the decentralized behavior of the insects. Authors have come up with designs for software solution that can regulate traffic congestion in a network transportation environment. The effectiveness of various researches on traffic management has been verified through appropriate metrics. Most of the traffic management systems are centered on using sensors, visual monitoring and neural networks to check for available parking space with the aim of informing drivers beforehand to prevent traffic congestion. There has been limited research on solving ongoing traffic congestion in congestion prone areas like car park with any of the common methods mentioned. This study focus however is on a motor park, as a highly congested area when it comes to traffic. The car park has two entrance gate and three exit gates which is divided into three Isle of parking lot where cars can park. An ant colony optimization algorithm (ACO) was developed as an effective management system for controlling navigation and vehicular traffic congestion problems when cars exit a motor park. The ACO based on the nature and movement of the natural ants, simulates the movement of cars out of the car park through their nearest choice exit. A car park simulation was also used for the mathematical computation of the pheromone. The system was implemented using SIMD because of its dual parallelization ability. The result showed about 95% increase on the number of vehicles that left the motor park in one second. A clear indication that pheromones are large determinants of the shortest route to take as cars followed the closest exit to them. Future researchers may consider monitoring a centralized tally system for cars coming into the park through a censored gate being.

The improved artificial bee colony algorithm for mixed additive and multiplicative random error model and the bootstrap method for its precision estimation

To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model(MAM error model),we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error model.The improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine transformation.The experimental results show that based on the weighted least squares criterion,the algorithm can get the results consistent with the weighted least squares method without multiple formula derivation.The parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods,which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.

Improved Ant Colony Algorithm for Vehicle Scheduling Problem in Airport Ground Service Support

Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.

A Drone-Based Blood Donation Approach Using an Ant Colony Optimization Algorithm

This article presents an optimized approach of mathematical techniques in themedical domain by manoeuvring the phenomenon of ant colony optimization algorithm(also known as ACO).A complete graph of blood banks and a path that covers all the blood banks without repeating any link is required by applying the Travelling Salesman Problem(often TSP).The wide use promises to accelerate and offers the opportunity to cultivate health care,particularly in remote or unmerited environments by shrinking lab testing reversal times,empowering just-in-time lifesaving medical supply.

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.

A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm

Securing digital image data is a key concern in today’s information-driven society.Effective encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption systems.This study introduces an innovative approach to creating S-boxes for encryption algorithms.The proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption scheme.The nonlinearity measure of the proposed S-boxes is 112.These qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data security.Furthermore,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption system.To validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been performed.In addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are calculated.The results indicate a 99.71%NPCR and 33.51%UACI.These results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.

An Air Defense Weapon Target Assignment Method Based on Multi-Objective Artificial Bee Colony Algorithm

With the advancement of combat equipment technology and combat concepts,new requirements have been put forward for air defense operations during a group target attack.To achieve high-efficiency and lowloss defensive operations,a reasonable air defense weapon assignment strategy is a key step.In this paper,a multi-objective and multi-constraints weapon target assignment(WTA)model is established that aims to minimize the defensive resource loss,minimize total weapon consumption,and minimize the target residual effectiveness.An optimization framework of air defense weapon mission scheduling based on the multiobjective artificial bee colony(MOABC)algorithm is proposed.The solution for point-to-point saturated attack targets at different operational scales is achieved by encoding the nectar with real numbers.Simulations are performed for an imagined air defense scenario,where air defense weapons are saturated.The non-dominated solution sets are obtained by the MOABC algorithm to meet the operational demand.In the case where there are more weapons than targets,more diverse assignment schemes can be selected.According to the inverse generation distance(IGD)index,the convergence and diversity for the solutions of the non-dominated sorting genetic algorithm III(NSGA-III)algorithm and the MOABC algorithm are compared and analyzed.The results prove that the MOABC algorithm has better convergence and the solutions are more evenly distributed among the solution space.

Security Test Case Prioritization through Ant Colony Optimization Algorithm

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems.One of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test suites.By effectively applying test case prioritization,both the time and cost required for developing secure software can be reduced.This paper proposes a test case prioritization technique based on the Ant Colony Optimization(ACO)algorithm,a metaheuristic approach.The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional techniques.It has been applied to a Mobile Payment Wallet application to validate the proposed approach.The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric.The ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering technique.These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.

A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype classification task from cancer gene expression data,it is challenging to obtain those significant genes due to the high dimensionality and high noise of data.Moreover,the existing methods always suffer from some issues such as premature convergence.Methods:To address those problems,we propose a new ant colony optimization(ACO)algorithm called DACO to classify the cancer gene expression datasets,identifying the essential genes of different diseases.In DACO,first,we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed;then,a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution;finally,the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally.To demonstrate the performance of the proposed algorithm in classification,different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets.Results:The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets.It can be used to address the classification problem effectively.Moreover,a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses.Conclusion:The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.

A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies quickly lead to the growth of an intelligent environment.Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks.The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things(IoT)network is widely recognized.Sensor nodes are low-power devices with low power devices,storage,and quantitative processing capabilities.The existing system uses the Artificial Immune System-Particle Swarm Optimization method to mini-mize the energy and improve the network’s lifespan.In the proposed system,a hybrid Energy Efficient and Reliable Ant Colony Optimization(ACO)based on the Routing protocol(E-RARP)and game theory-based energy-efficient clus-tering algorithm(GEC)were used.E-RARP is a new Energy Efficient,and Reli-able ACO-based Routing Protocol for Wireless Sensor Networks.The suggested protocol provides communications dependability and high-quality channels of communication to improve energy.For wireless sensor networks,a game theo-ry-based energy-efficient clustering technique(GEC)is used,in which each sen-sor node is treated as a player on the team.The sensor node can choose beneficial methods for itself,determined by the length of idle playback time in the active phase,and then decide whether or not to rest.The proposed E-RARP-GEC improves the network’s lifetime and data transmission;it also takes a minimum amount of energy compared with the existing algorithms.

Selective Harmonics Elimination Technique for Artificial Bee Colony Implementation

In this research,an Artificial Bee Colony(ABC)algorithm based Selective Harmonics Elimination(SHE)technique is used as a pulse generator in a reduced switch fifteen level inverter that receives input from a PV system.Pulse width modulation based on Selective Harmonics Elimination is mostly used to suppress lower-order harmonics.A high gain DC-DC-SEPIC converter keeps the photovoltaic(PV)panel’s output voltage constant.The Grey Wolf Optimization(GWO)filter removes far more Photovoltaic panel energy from the sunlight frame.To eliminate voltage harmonics,this unique inverter architecture employs a multi-carrier duty cycle,a high-frequency modulation approach.The proposed ABC harmonics elimination approach is compared to SHE strategies based on Particle Swarm Optimization(PSO)and Flower Pollination Algorithm(FPA).The suggested system’s performance is simulated and measured using the MATLAB simulation tool.The proposed ABC approach has a THD level of 4.86%,which is better than the PSO and FPA methods.

Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these needs.The WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services(QoS)constraints.The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints.In this paper,we introduce an extension of our previous works on the Artificial Bee Colony(ABC)and Bat Algorithm(BA).A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search.The bat agent is used to improve the solution of exhausted bees after a threshold(limits),and also an Elitist Strategy(ES)is added to BA to increase the convergence rate.The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria(average fitness values,best fitness values,and execution time)that were measured for 30 different runs.These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets.The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors.TheWilcoxon signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100%of datasets.

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.