Fuzzy Ant Colony Optimization Research Articles

Intrusion detection system and fuzzy ant colony optimization based secured routing in wireless sensor networks

Intrusion detection system and fuzzy ant colony optimization based secured routing in wireless sensor networks

Optimizing Enterprise Marketing Project Portfolios Using Fuzzy Ant Colony Optimization

Enterprise marketing project portfolio optimization is important for business competitiveness, but involves multiple uncertain factors. An integrated model using fuzzy rules was proposed in this paper to enhance ant colony optimization. The fuzzy ant colony algorithm effectively handled ambiguity in project costs, returns, and risks when selecting an optimal portfolio of marketing initiatives. Experiments demonstrated the algorithm efficiency in converging towards high-quality solutions. Case studies indicated the model helped boost customer loyalty and profits through tailored marketing strategies, outperforming conventional approaches. The fuzzy optimization provides an effective decision-making framework for enterprises to maximize marketing effectiveness.

Improved fuzzy ant colony optimization to recommend cultivation in Tamil Nadu, India

Improved fuzzy ant colony optimization to recommend cultivation in Tamil Nadu, India

Image retrieval based on swarm intelligence

To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.

Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

SummaryA sensor network is a situation‐based critical network defined under certain restrictions and constraints. The clustered architecture is defined over these networks to utilize the criticality vectors and to improve network communication. In this paper, a degree load‐balanced and fuzzy‐ACO (DLB‐fACO) protocol is proposed for optimizing the clustered architecture. In this architecture, the load analysis is performed while forming the clusters. The cluster head selection is performed based on energy, cluster density, and probability vector. The node degree‐ and energy‐balanced analysis is performed for identifying the cluster members. Once the clusters are formed, the ACO approach is applied to perform the cluster‐based hierarchical routing. Communication is performed at two levels. In the first level, node‐to‐cluster head communication is performed by considering energy and degree consideration. In the second level, the fuzzy‐integrated ACO method is applied for inter‐cluster route formation. The route optimization is here performed under fuzzy‐based energy, degree, and distance parameters. These fuzzy parameters are evaluated within the ACO algorithm for generating the optimized route. The proposed load‐balanced protocol is analyzed against the LEACH, LEACH‐C, LEACH‐CC, M‐LEACH, Fuzzy‐PSO, Fuzzy‐ACO, Fuzzy‐Cuckoo, and FMCB‐ER protocols. The experimentation results confirm the significant gain in network lifetime and packet communication. The cluster count, clustering switching, and communication failure are also reduced in comparison with existing conventional and optimized protocols.

Solving the Shortest Path Problem by Fuzzy Ant Colony Optimization Algorithm

Solving the Shortest Path Problem by Fuzzy Ant Colony Optimization Algorithm

Image Edge Detection with Fuzzy Ant Colony Optimization Algorithm

Searching and optimizing by using collective intelligence are known as highly efficient methods that can be used to solve complex engineering problems. Ant colony optimization algorithm (ACO) is based on collective intelligence inspired by ants' behavior in finding the best path in search of food. In this paper, the ACO algorithm is used for image edge detection. A fuzzy-based system is proposed to increase the dynamics and speed of the proposed method. This system controls the amount of pheromone and distance. Thus, instead of considering constant values for the parameters of the algorithm, variable values are used to make the search space more accurate and reasonable. The fuzzy ant colony optimization algorithm is applied on several images to illustrate the performance of the proposed algorithm. The obtained results show better quality in extracting edge pixels by the proposed method compared to several image edge detection methods. The improvement of the proposed method is shown quantitatively by the investigation of the time and entropy of conventional methods and previous works. Also, the robustness of the proposed method is demonstrated against additive noise.

Multitask Scheduling in Consideration of Fuzzy Uncertainty of Multiple Criteria in Service-Oriented Manufacturing

Tasks in the field of service-oriented manufacturing (SOM) such as cloud manufacturing have the characteristics of complexity, heterogeneity, uncertainty, and geographically distribution, which make scheduling them nontrivial and challenging, especially in the fuzzy environment. A fuzzy multicriteria modeling is of importance for the problem of fuzzy scheduling in SOM. In this article, four comprehensive models are proposed, which are different in the uncertain degree of considered performance criteria and/or defuzzification timepoints of fuzzy values. For each model, all weighted criteria are aggregated by using an exponential benefit function. For solving the models, three scheduling algorithms, namely one-level fuzzy ant colony optimization (OFACO), two-level single optimization fuzzy ACO (TSFACO), and two-level double optimization fuzzy ACO (TDFACO), are proposed. OFACO takes the view of the whole set of tasks on the SOM platform only whereas TSFACO and TDFACO consider both the view of the whole set of tasks and the view of individual task. The performance and effectiveness of the proposed fuzzy models and scheduling schemes are compared, respectively, using test datasets with varying sizes. The test results show that the first model with fuzzy objective is better for type-1 fuzzy uncertainty whereas the second model with defuzzified objective is better for type-2 fuzzy uncertainty and TDFACO outperforms the other two scheduling schemes in terms of the proposed integrated fuzzy multicriteria performance both from the individual task perspective and the whole task perspective.

FACO: a hybrid fuzzy ant colony optimization algorithm for virtual machine scheduling in high-performance cloud computing

High-performance cloud computing has recently become the focus of much interest. Extensive research has shown that scheduling and load balancing are among the key aspects of performance optimization. The allocation of a set of requests into a set of computing resources, which is considered as an NP-hard problem, aims to distribute efficiently the load within the cloud architecture. To resolve this problem, the last decade has seen a growing trend towards using hybrid approaches to combine the advantages of different algorithms. In this paper, we propose a hybrid fuzzy ant colony optimization algorithm (FACO) for virtual machine scheduling to guarantee high-efficiency in a cloud environment. The proposed fuzzy module evaluates historical information to calculate the pheromone value and select a suitable server while keeping an optimal computing time. The experimental work presented in this study provides one of the first investigations into how to choose the optimal parameters of ant colony optimization algorithms using the Taguchi experimental design. We have simulated the proposed algorithm through the Cloud Analyst and CloudSim simulators by applying different cloud configurations to evaluate the performance of the proposed algorithm. Our findings highlight how response time and processing time are improved compared to the Round Robin algorithm, Throttled algorithm and Equally Spread Current Execution Load algorithm, especially in the case of a high number of nodes. FACO algorithm could be applied to define efficient cloud architecture adapted to high-performance applications.

Ant colony optimization algorithm for scheduling jobs with fuzzy processing time on parallel batch machines with different capacities

We study the problem of scheduling on parallel batch processing machines with different capacities under a fuzzy environment to minimize the makespan. The jobs have non-identical sizes and fuzzy processing times. After constructing a mathematical model of the problem, we propose a fuzzy ant colony optimization (FACO) algorithm. Based on the machine capacity constraint, two candidate job lists are adopted to select the jobs for building the batches. Moreover, based on the unoccupied space of the solution, heuristic information is designed for each candidate list to guide the ants. In addition, a fuzzy local optimization algorithm is incorporated to improve the solution quality. Finally, the proposed algorithm is compared with several state-of-the-art algorithms through extensive simulated experiments and statistical tests. The comparative results indicate that the proposed algorithm can find better solutions within reasonable time than all the other compared algorithms.

Fuzzy Bio-Inspired Hybrid Techniques for Server Consolidation and Virtual Machine Placement in Cloud Environment

Abstract Cloud computing technology has transformed the information and communication technology industry by authorizing on-demand resource delivery to the cloud users. Datacenters are the major resource storage places from where the resources are disseminated to the requesters. When several requests are received by datacenters, the available resources are to be handled in an optimized way; otherwise the datacenters suffer from resource wastage. Virtualization is the technology that helps the cloud providers to handle several requests in an optimized way. In this regard, virtual machine placement, i.e., the process of mapping virtual machines to physical machines is considered to be the major research issue. In this paper, we propose to apply fuzzy hybrid bio-inspired meta-heuristic techniques for solving the virtual machine placement problem. The cuckoo search technique is hybridized with the fuzzy ant colony optimization and fuzzy firefly colony optimization technique. The experimental results obtained show competing performance of the proposed algorithms.

Fuzzy hierarchical ant colony optimization routing for weighted cluster in MANET

Wireless mobile network likely will be a set of self-directed systems. In which they are not only based on the unfixed infrastructure-less wireless network. This paper gives a major scope for routing ad-hoc network using fuzzy hierarchical ant colony optimization routing by maintaining the sustainability of the cluster head inside a cluster is very difficult in the adhoc network. The selected cluster head among the nodes is based on the quality of service parameter of the nodes. The weight assigned to the cluster head will determine how long the cluster head will hold. The fuzzy hierarchical ant colony optimization routing is used to select the optimized cluster head by way of in a cluster and the optimal cluster head will increase the routing. Ant colony optimization, Fuzzy Rules for Cluster Head Selection Process and Cluster’s Gateway are the three processes involved in the proposed FHACO protocol. The parameters taken for our proposed algorithm are the buffer size, energy consumption, routing overhead, packet delivery ratio and end to end delay throughput of wireless system. The system is implemented in network simulator-2 and it is assessed to gather weighted clustering technique. The experimental analysis shows that fuzzy hierarchical ant colony optimization produces a better result in maintaining the persistence of the cluster head.

A study of fuzzy control with ant colony algorithm used in mobile robot for shortest path planning and obstacle avoidance

The fuzzy ant colony optimization (FACO) method proposed in this paper minimizes the iterative learning error of the ant colony optimization (ACO) algorithm using fuzzy control. This algorithm finds the shortest path, detects any obstructions in front of the mobile robot using ultrasonic transducers, and adjusts the turning angle of the mobile robot so as to avoid obstacles. To verify the FACO algorithm, simulations using a mobile robot in two environments were carried out. The first environment was shortest-path planning without obstacles. The second environment was shortest-path planning for a single destination with obstacles. This paper also compares tests carried out in a simple Z-shaped environment map and in a complicated environment map. By comparing FACO with a pattern search (PS) algorithm, a genetic algorithm (GA), particle swarm optimization (PSO), and traditional ACO for the cases in the simple Z-shaped environment map, we found that the path distance obtained using FACO was 2.60, 4.40, 2.04, and 6.53% shorter than that using PS, GA, PSO, and ACO, respectively. In the complex environment map, as compared to the self-adaptive ant colony optimization, FACO had a path distance that was 1.38% shorter. Therefore, the results showed that the FACO algorithm can find the shortest path and avoid obstacles for both simple and complex topographies.

A Multi-Objective Fuzzy Ant Colony Optimization Algorithm for Virtual Machine Placement

In cloud computing, the most important challenge is to enforce proper utilization of physical resources. To accomplish the mentioned challenge, the cloud providers need to take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a multi-objective virtual machine placement problem (VMP) and propose to apply multi-objective fuzzy ant colony optimization (F-ACO) technique for optimal placing of virtual machines in the physical servers. VMP-F-ACO is a combination of fuzzy logic and ACO, where we use fuzzy transition probability rule to simulate the behaviour of the ants and the authors apply the same for virtual machine placement problem. The results of fuzzy ACO techniques are compared with five variants of classical ACO, three bin packing heuristics and two evolutionary algorithms. The results show that the fuzzy ACO techniques are better than the other optimization and heuristic techniques considered.

Ant Colony Optimization Based Orthogonal Directional Proactive–Reactive Routing Protocol for Wireless Sensor Networks

Routing protocols for wireless sensor networks are important in addressing the various quality-of-service (QoS) issues pertaining to different applications. The most important QoS issues while designing routing protocols for WSN are energy awareness, scalability and network lifetime. However to deal with these issues the solutions provided in related literature have certain inherent disadvantages like high control overhead, low packet delivery ratio and requirement of global location information. In order to resolve these issues, we propose an orthogonal transmission based scalable, lightweight and energy aware routing protocol named as OD-PPRP which does not require global location information and has low control overhead. The proposed protocol OD-PRRP has the characteristics of both reactive and proactive routing protocols and utilizes fuzzy logic and Ant Colony Optimization to identify energy efficient and optimal paths. The simulation results show in both static and dynamic environment, OD-PRRP has better network lifetime, low end to end transmission delay, less overhead and high packet delivery ratio than other state of art QoS aware routing protocol viz. EARQ, EAODV and EEABR.

A fuzzy ant colony optimization to solve an open shop scheduling problem with multi-skills resource constraints

An open shop scheduling problem based on a mechanical workshop is described here. The main objective is to find the sequence of jobs which minimizes the total flow time. For that reason, we first formulate the problem as a mixed integer linear programming model which considers different resource constraints related to the personnel assignment. Resource skills and their availability are required to process tasks. A mathematical model is described and solved optimally. Besides that, a fuzzy ant colony optimization method is proposed due to the difficulty to fix the different parameters of an ACO and improve the quality of the solution. Finally, some computational experiments are defined using the references of the literature to get efficiency of ant colony optimisation. A first kind of tests are related to the small-sized instances allowing to evaluate the general performance of the model and the algorithm while a second one involves the large-size instances showing a further evaluation of the algorithm.

Energy Efficient Routing in Wireless Sensor Networks Based on Fuzzy Ant Colony Optimization

A wireless sensor network (WSN) is a collection of sensor nodes that dynamically self-organize themselves into a wireless network without the utilization of any preexisting infrastructure. One of the major problems in WSNs is the energy consumption, whereby the network lifetime is dependent on this factor. In this paper, we propose an optimal routing protocol for WSN inspired by the foraging behavior of ants. The ants try to find existing paths between the source and base station. Furthermore, we have combined this behavior of ants with fuzzy logic in order for the ants to make the best decision. In other words, the fuzzy logic is applied to make the use of these paths optimal. Our algorithm uses the principles of the fuzzy ant colony optimization routing (FACOR) to develop a suitable problem solution. The performance of our routing algorithm is evaluated by Network Simulator 2 (NS2). The simulation results show that our algorithm optimizes the energy consumption amount, decreases the number of routing request packets, and increases the network lifetime in comparison with the original AODV.

The multi-objective hybridization of particle swarm optimization and fuzzy ant colony optimization

In this paper, we illustrate a novel optimization approach based on Multi-objective Particle Swarm Optimization (MOPSO) and Fuzzy Ant Colony Optimization (FACO). The basic idea is to combine these two techniques using the best particle of the Fuzzy Ant algorithm and integrate it as the best local Particle Swarm Optimization (PSO), to formulate a new approach called hybrid MOPSO with FACO (H-MOPSO-FACO). This hybridization solves the multi-objective problem, which relies on both time performance criteria and the shortest path. Experimental results illustrate that the proposed method is efficient.

Weaponry Equipment Risk Management Based on Adaptive Fuzzy Neural Network and Ant Colony Optimization

Weaponry Equipment Risk Management Based on Adaptive Fuzzy Neural Network and Ant Colony Optimization

Fuzzy sliding-mode control for ball and beam system with fuzzy ant colony optimization

This paper mainly addresses the balance control of a ball and beam system, where a pair of decoupled fuzzy sliding-mode controllers (DFSMCs) are proposed. The DFSMC has the advantage of reducing the design complexity, in which the coupling dynamics of the state-error dynamics are considered as disturbance terms. Stability analysis of the ball and beam system with DFSMCs is also discussed in detail. To further improve the control performance, an improved ant colony optimization (ACO) is proposed to optimize the controller parameters. The proposed ACO algorithm has the enhanced capability of fuzzy pheromone updating and adaptive parameter tuning. The proposed ACO-optimized scheme is utilized to tune the parameters of the fuzzy sliding-mode controllers for a real ball-and-beam system. Compared to some conventional ACO algorithms, simulation and experimental results all indicate that the proposed scheme can provide better performance in the aspect of convergence rate and accuracy.