Ant Colony Optimization Technique Research Articles

Ant colony optimization technique for integrating renewable DG in distribution system with techno-economic objectives

Ant colony optimization technique for integrating renewable DG in distribution system with techno-economic objectives

Optimal Algorithms for Load Balancing in Optical Burst Switching Networks

Data packet drop can happen in Optical Burst-Switched (OBS) when two data bursts are competing on the same wavelength. Recently, many techniques have been developed to solve this problem but they do not consider the congestion. Also, it is necessary to balance the load system in the OBS network. The Ant Colony Optimization (ACO) technique can be applied to determine the straight and the safest route. However, the ACO technique raises both power utilization as well as the execution time. In this study, Cuckoo Search (CS) and ACO methods based approach is proposed to avoid the congestion and load balancing in the OBS network. This strategy evaluates the intensity of hotspot data and then launches the congestion rate optimization process that depends on their load situations. The congestion rate optimization represents the available bandwidth, data distribution rate, queue size, and access rate, and also these factors are optimized through the CS technique. The fitness utility in the CS technique adjusts the distribution rate in the OBS network, and the proposed ACO technique solves the energy utilization problem. The simulation results proved that the presented strategy evades both the end-to-end delay as well as the possibility of packet drop.

Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real‐time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology‐enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES‐ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC‐PSO, and PSO‐COGENT algorithms, the created HES‐ACO algorithm was simulated at CloudSim and found to optimize all parameters.

Load balancing using dynamic algorithms for cloud environment: A survey

Numerous researchers have worked on different load balancing algorithms in cloud Environment. But, no efficient algorithm for load balancing in cloud environment is developed yet. For optimizing the solution and fully utilizing the resources authors moved to the nature inspired algorithms. This paper presents a survey of different dynamic and nature inspired algorithms with their advantages and disadvantages. The objective of this study is to bring out the conclusion that disadvantages of various nature inspired algorithms can be overcome if different algorithms can be used as a hybrid to suit the usage of application and new optimization technique can be formulated by the combination of two or more algorithm. Researchers are also proposing a new hybrid technic which is more effective and robust and will try to balance the load without being complex. Proposed algorithm will use Ant Colony and Bird swarm Optimization techniques through which the degree of imbalance will be reduced. By using the proper fitness function makespan can be minimized and resource utilization can be maximized. Good part of the Ant colony optimization and Bird Swarm optimization is that by using these algorithms best optimal solution can be find out and both can be suited for static and dynamic environment and adaptive as well.

Prosumer Energy Management for Optimal Utilization of Bid Fulfillment With EV Uncertainty Modeling

This article emphasizes the operation of a microgrid for optimal utilization of its resources. A microgrid system with photovoltaic (PV) generators, electric vehicles (EVs), electrical loads, and energy storage system is considered for the present study. Bidirectional power flow from the prosumer to the grid is proposed to provide flexibility for excess energy generated/stored at the prosumer. To address the EV uncertainty, the Markov chain model is used to predict EV availability based on historical travel data of the user. EV prediction uncertainty and the PV power uncertainty are considered in the modeling of the algorithm. The aggregator moderates on behalf of prosumers as a single entity between prosumers and the grid. The aggregator takes part in energy trading by submitting bids of specific energy volume to supply or consume in the day-ahead market. The bids are made to purchase energy at minimum prices and sell energy at maximum price. If the committed bid volumes are violated, the grid utility penalizes the aggregator and restricts their participation in energy transactions. The proposed strategy considers the energy bids made by the aggregator to regulate the rate of charging/discharging of EV. The charging/discharging schedule to fulfill the energy commitments while minimizing the operational cost for optimal microgrid operation has been optimized using a novel hybrid ant colony optimization and cuckoo search technique. A comparative analysis of the proposed technique with other popular meta-heuristic techniques to validate the efficacy of the proposed technique.

Metaheuristic Optimization Algorithm of MPPT Controller for PV system application

The Maximum Power Point Tracking controller (MPPT) is a key element in Photovoltaic systems (PV) since it allows maintaining the PV operating point at its maximum under different temperatures and sunlight irradiations. Metaheuristic algorithms such as the ant colony optimization (ACO) are adopted and have shown their superiority to many other techniques. The perturb and observe (P&O) algorithm is a simple and efficient technique, and is one of the most commonly employed MPPT schemes for PV power generation systems. Which executed by manipulating direct duty ratio of the boost converter. P&O method miserably fails to recognize various MPPT controllers. This paper proposes ACO technique to solve real-life problems.

Comparison of Metaheuristic Techniques for Parcel Delivery Problem: Malaysian Case Study

Most people preferred e-commerce ensuing the Coronavirus Disease-2019 (COVID-19) outbreak, resulting in delivery companies receiving large quantities of parcels to be delivered to clients. Hurdle emerges when delivery person needs to convey items to a large number of households in a single journey as they never face this situation before. As a result, they seek the quickest way during the trip to reduce delivery costs and time. Since the delivery challenge has been classified as an NP-hard (non-deterministic polynomial-time hard)) problem, this study aims to search the shortest distance, including the runtime for the real case study located in Melaka, Malaysia. Hence, two metaheuristic approaches are compared in this study namely, Ant-Colony Optimization (ACO) and Genetic Algorithm (GA). The results show that the GA strategy outperforms the ACO technique in terms of distance, price, and runtime for moderate data sizes that is less than 90 locations.

Information Technology for Designing Rule bases of Fuzzy Systems using Ant Colony Optimization

This paper proposes the universal information technology for designing the rule bases (RB) with the formation of optimal consequents for fuzzy systems (FS) of different types on the basis of ant colony optimization (ACO) techniques. The developed ACO-based information technology allows effectively synthesizing rule bases of various dimensions both for the MISO and MIMO fuzzy systems taking into account the particular features of the RB consequents formation in the conditions of insufficient initial information. In order to study and validate the efficiency of the presented information technology the design of the RB for the adaptive fuzzy control system of the ship steering device is carried out in this work. The computer simulations results show that adaptive control system with developed RB provides achievement of high enough quality indicators of rudder angle control. Thus, application of the proposed ACO-based information technology allows designing effective RB with optimal consequents by means of minor computational costs that, in turn, confirms its high efficiency.

Assessment of jeans sewing thread consumption by applying metaheuristic optimization methods

Assessment of jeans sewing thread consumption by applying metaheuristic optimization methods

Enhancing Learning Retention with Document Summarization Using NMF and ACO

Retaining learning is a desirable value for the learned. The traditional approach to learning retention is to read, understand and summarise. This explains the reason why vital points in voluminous book are highlighted for the purpose of retention. This study developed an unsupervised extractive summarization algorithm using Non-negative Matrix Factorisation and Ant-Colony Optimization Techniques for electronic text summarization. The developed system is an improvement over the existing NMF and LSA in the sense that LSA and NMF do not adequately address the noise issue that features in semantic document representation thereby leading to poor selection of meaningful sentence that represent the document summary. While NMF was used for factorising the initial Document-Term matrix generated from the document, ACO was used to remove the remnant of noise from the DTM. The improved algorithm is applied on a literature text material which reduces the voluminous material into handy summary by extraction. The algorithm was evaluated using compression ratio, retention ratio and was found to be adequate. Keywords: Text Summarization, Non-Negative Matrix Factorisation, Ant-Colony Optimization

Towards a Distributed SMA-based Solution for the Interoperability of Hospital Information Systems for Better Routing of Emergency Ambulances

Multi-agent systems MASs have been widely used to interoperate hospital information systems (HISs). The use of MASs for HISs interoperability has become a central solution, especially in the field of emergency medicine. In emergencies, the notion of delay is relative, because responders only have a few minutes to react. This emergency response time has an important role in the event that an accident occurs on the road. Existing procedures for the emergency ambulance (EA) dispatch strategy are based on manual dispatch. In this work, we are introducing a distributed emergency ambulance (DEA) routing system to control emergency latency time, which includes driving route planning to guide emergency vehicles and the allocation of distributed emergency resources (emergency ambulances and hospitals) to reduce the EA response time caused by traffic or the wrong human decision to transport ambulance to the accident site. The allocation of resources (hospitals) is ensured through a recommendation system based on the interoperability of several interconnected HISs using a multi-agent system. The proposed solution takes into consideration dynamic traffic flow information during the day to build dynamic paths for EA. The improved method is based on a distributed architecture to calculate and find the optimal pathway for a set of emergency vehicles based on ACO ant colony optimization techniques. The results of the simulation show that the proposed method can decrease the total travel time of the ambulance to reach the accident position compared to conventional methods that use lights and sirens to warn other vehicles to free up the road for the ambulance or use a traditional approach based on the vision/reflection of the driver to choose in a random way the paths to take. Based on such a solution, ambulance staff will be able to save lives by optimizing the total journey with the minimum travel.

Path Outlining of a KUKA LBR Iiwa 14 R820 using Hybrid Optimization

In this paper, the inverse kinematics of more advanced degree of freedom robot is computed with the support of MATLAB. This paper deals with the study of control modeling of the KUKA robot. The path planning of robot end effector has been a research area for years. In this paper the new hybrid optimization techniques are used to solve the forward and inverse kinematics solution of KUKA LBR iiwa 14 R820. For Forward Kinematics SHO (selfish herd optimization), KHO (krill herd optimization)hybrid optimization technique is used and for Inverse Kinematics, hybrid ALO (ant lion optimization), ACO (ant colony optimization) technique is used. This proposed work also incorporates the itinerary of the robot in a workspace area with multiple obstacles by using a Hybrid GWO (Grey wolf optimization), GOA (grasshopper optimization). The optimized route discovered by applying the hybrid optimizer is energy efficient. Graphic User Interface is used to strut the optimized path into 3-Dimensional form. Forged consequences are administered to exemplify the effeteness and virtue of the propounding method. While using the different optimizing techniques the optimized path is found, with obstacle and without obstacle with less execution time and in less number of iterations. The Graphic user Interface is used to represent the different paths. In this work, the multi-objective functions such as collision free motion, jerk, travelling time and acceleration are considered for the planning of trajectory of the robot manipulator. The fitness function is reducing the multi-objective function.

A novel quantum algorithm for ant colony optimisation

Ant colony optimisation (ACO) is a commonly used meta-heuristic to solve complex combinatorial optimisation problems like the travelling salesman problem (TSP), vehicle routing problem (VRP) etc. However, classical ACO algorithms provide better optimal solutions but do not reduce computation time overhead to a significant extent. Algorithmic speed-up can be achieved by using parallelism offered by quantum computing. Existing quantum algorithms to solve ACO are either quantum-inspired classical algorithms or hybrid quantum-classical algorithms. Since all these algorithms need the intervention of classical computing, leveraging the true potential of quantum computing on real quantum hardware remains a challenge. This study's main contribution is to propose a fully quantum algorithm to solve ACO, enhancing the quantum information processing toolbox in the fault-tolerant quantum computing (FTQC) era. We have solved the single source single destination (SSSD) shortest-path problem using our proposed adaptive quantum circuit for representing the dynamic pheromone-updating strategy in real IBMQ devices. Our quantum ACO technique can be further used as a quantum ORACLE to solve complex optimisation problems in a fully quantum setup with significant speed up upon the availability of more qubits.

Improve the QoS using multi-path routing protocol for Wireless Multimedia Sensor Network

The diversity of quality-of-service (QoS) necessities of Internet applications prompts several QoS routing techniques which receive different QoS metrics into deliberation. Conversely, merely adjusting existent multi-path routing techniques to maintain several QoS metrics, cannot assurance correctness. To solve this problem, we essentially analyze the Improve the Qos Using Multipath Routing Protocol (IQMRP) for Wireless Multimedia Sensor Network (WMSN) is proposed. Generally, wireless transactions are prone to link losers owing to signal intrusion that result in communication failures as well as packet drop. Thus, concurrent routing beside multi-path will considerably enhance the capability to handle these failures, significantly gaining wireless multimedia application. The multi-path communication assists to offer a great bandwidth necessary through the multimedia transaction among two nodes. Even while a single route would be capable of offering connectivity, hence, the multi-path transmission also serves to balance traffic load that can extend the network lifespan. In this approach, Ant Colony Optimization (ACO) technique to chooses the multi-path using route preference possibility. This route preference possibility is measured based on node residual energy, bandwidth as well as next-hop accessibility. Next route load is equated through an indicator to allocate the data traffic evenly on the connections from sender to recipient. Simulation results illustrate that diminishes both the average delay as well as route load function in the network. • We essentially analyze the Improve the Qos Using Multipath Routing Protocol (IQMRP) for Wireless Multimedia Sensor Network (WMSN) is proposed. • Even while a single route would be capable of offering connectivity, hence, the multi-path transmission also serves to balance traffic load that can extend the network lifespan. In this approach, Ant Colony Optimization (ACO) technique to chooses the multi-path using route preference possibility. • The load on the paths can be equated through determining an indicator via every node to disseminate the data load uniformly from sender to recipient.

Modified ant colony optimization based PID controller design for coupled tank system

Most of the optimization algorithm suffers from slow convergence and poor solution accuracy due to nonlinearity characteristics of the problem. This paper proposes a meta-heuristic method, namely the modified Ant Colony Optimization (m-ACO) technique to address these issues. The conventional ant colony optimization algorithm is improved by using multiple random initializations to increase the probability of getting a better start population and hence increase the possibility to achieve near-global optimum. The efficacy of the proposed algorithm is tested on classical benchmark functions and on the PID controllers. The performance of controllers depends on the selection of tuning parameters that are optimized to obtain an optimum solution. The m-ACO technique is used to optimize parameters of PID controller for Coupled Tank System. It is one of the benchmark control problems due to its nonlinear characteristics. The obtained results of the proposed algorithm are compared with the Zeigler Nichols tuned PID controller, and PSO tuned PID controller in terms of transient performances including rise-time, peak overshoot and performance parameters such as Integral Square Error (ISE) and Integral Absolute Error (IAE). The proposed method is also tested for showing effectiveness in set-point tracking and disturbance rejection.

Mining Fuzzy Temporal Gradual Emerging Patterns

Gradual emerging patterns (GEPs) are gradual item sets that occur less frequently in one data set and more frequently in another. For instance, let ‘fan speed’ and ‘temperature’ be attributes of two numerical data sets. A gradual item set “the higher the speed, the lower the temperature” (which correlates a data set’s attributes) becomes a GEP if it is less frequent (in terms of support as in frequent pattern mining) in one data set and more frequent in another. However, such patterns do not indicate how time gap impacts the emergence. Many correlations appear over time, for instance when phenomena appear after some meteorological situation due to latency. Previous works have not taken this temporal aspect into account. In this paper, we introduce temporal gradual emerging patterns (TGEPs) which are temporal gradual patterns (TGPs) whose frequency supports increase significantly between transformed data sets. For instance, a TGP “the higher the speed, the lower the temperature, almost 3 minutes later” becomes a TGEP if it occurs more frequently in one transformed data set than in another. Furthermore, we extend border manipulation to the case of mining TGEPs. In addition, we propose a more efficient ant colony optimization technique that exploits a heuristic approach to construct TGEPs.

Novel load balancing approach based on ant colony optimization technique in cloud computing

The cloud is the framework in which communication is connected with virtual machines, data centers, hosts, and brokers. The broker searches for a highly reliable cloudlet virtual machine for execution. Vulnerability can occur in the network because of which framework gets overburden. A research strategy is introduced in this article to expand the fault tolerance of the framework. The proposed approach improvement depends on the algorithm of ant colony optimization (ACO) that can choose the better virtual machine on which is to migrate the cloudlet to reduce the execution time and energy consumption. The efficiency of the proposed approach simulated in terms of execution time, energy consumption and examined with CloudSim. The introduction is provided in this article with a detailed description of cloud computing and, in addition, green cloud computing with its models. This article also discussed the virtual machine (VM) in more depth in the introduction section, which allows cloud service providers to supervise cloud resources competently while dispensing with the need for human oversight. Then the article submitted and explained the related works with their discussion and then it explained the novel proposed load balancing based on ACO technique and concluded that the execution time and energy consumption of the proposed technique is better than the three-threshold energy saving algorithm (TESA) technique that is commonly used in cloud load balancing.

Evaluation of policy options supporting electric vehicles in city logistics: a case study

Due to the restriction of movement of heavy-duty vehicles (e.g.trucks), most cities have started to implement urban consolidation centers to reduce the negative impact of carbon emission. Accordingly, this study focuses on the city logistics problem with respect to policy decisions regarding the use of light commercial vehicles through urban consolidation schemes in a way to minimise carbon emissions and, thus, total transportation costs. In particular, this study has developed a mathematical model to minimise total transportation costs including routing costs, vehicle fixed costs, emission costs, and zone entry fees. The proposed model is solved using a modified ant colony optimisation (MACO) technique. The optimal results are obtained within CPU time, and results are validated using the ACO algorithm and the Cplex solver. Meanwhile, different supporting policies are introduced, and computational experiments are executed on a real-life case study. Results show that the purchase subsidy and zone entry fee are significant considerations when promoting electric vehicles (EVs) in city logistics.

Criterial Based Opinion Leader’s Selection for Decision-Making Using Ant Colony Optimization

The Internet has already become a medium for people to access information, express their views, and connect with one another through various media and tools as a result of its rapid global development. Web 2.0 has become increasingly important in people’s daily lives. With the passing of time, many online communities, such as blogs, forums, and websites, are becoming more interactive. Individuals can easily express their opinions publicly and see what others say about their opinions. In a social network, an opinion leader is a powerful individual who is an expert in a particular field and has a large number of people who follow his or her comments or ideas. Companies and governments may contact the most powerful people after discovering them in order to influence sales or guide public opinion, respectively. In heterogeneous communication networks, opinion leaders serve as an influencer for the acceptance and dissemination of new products and users in marketing. Others’ drives, values, perceptions, motivations, and actions can be influenced by opinion leaders. These influences have a major effect on other customers’ policymaking processes. Opinion leaders come in a variety of demographic shapes; in general, they are committed, ambitious, and dynamic individuals with excellent academic and social edifications who persuade customers through their expert knowledge. Opinions and values are the most important variables in human experiences in social phenomena. The method of assessing opinions is extremely difficult since people often change their minds when speaking with others. The study of the evolution and formation of opinions in a social group is known as opinion dynamics, and it is based on collective decision-making. The bounded confidence rule is one of the intrinsic interaction principles in the dynamics of human actions. Many models have been created to date to explore the dynamics of leaders’ decision-making. Due to the social network’s high complexity and the randomness and contingency of their behavior, identifying opinion leaders in the network is difficult. Organizations and entrepreneurs must choose which opinion leaders to partner with to gain a deeper understanding of how to communicate with them to promote various goods and services. For the opinion leader’s selection optimization, the proposed framework feature-based opinion leader selection is utilized using the ant colony optimization technique that selects the best opinion leader based on their unique features. The proposed method will address the opinion leader selection problem.

Ant colony optimization for mining gradual patterns

Gradual pattern extraction is a field in Knowledge Discovery in Databases that maps correlations between attributes of a data set as gradual dependencies. A gradual dependency may take the form: “the more Attribute\(_{K}\), the less Attribute\(_{L}\)”. Classical approa-ches for extracting gradual patterns extend either a breath-first search or a depth-first search strategy. However, these strategies can be computationally expensive and inefficient especially when dealing with large data sets. In this study, we investigate 3 population-based optimization techniques (i.e. ant colony optimization, genetic algorithm and particle swarm optimization) that may be employed improve the efficiency of mining gradual patterns. We show that ant colony optimization technique is better suited for gradual pattern mining task than the other 2 techniques. Through computational experiments on real-world data sets, we compared the computational performance of the proposed algorithms that implement the 3 population-based optimization techniques to classical algorithms for the task of gradual pattern mining and we show that the proposed algorithms outperform their classical counterparts.