Showing papers on "Ant colony optimization algorithms published in 2023"

An Accurate UAV 3-D Path Planning Method for Disaster Emergency Response Based on an Improved Multiobjective Swarm Intelligence Algorithm

Abstract: Planning a practical three-dimensional (3-D) flight path for unmanned aerial vehicles (UAVs) is a key challenge for the follow-up management and decision making in disaster emergency response. The ideal flight path is expected to balance the total flight path length and the terrain threat, to shorten the flight time and reduce the possibility of collision. However, in the traditional methods, the tradeoff between these concerns is difficult to achieve, and practical constraints are lacking in the optimized objective functions, which leads to inaccurate modeling. In addition, the traditional methods based on gradient optimization lack an accurate optimization capability in the complex multimodal objective space, resulting in a nonoptimal path. Thus, in this article, an accurate UAV 3-D path planning approach in accordance with an enhanced multiobjective swarm intelligence algorithm is proposed (APPMS). In the APPMS method, the path planning mission is converted into a multiobjective optimization task with multiple constraints, and the objectives based on the total flight path length and degree of terrain threat are simultaneously optimized. In addition, to obtain the optimal UAV 3-D flight path, an accurate swarm intelligence search approach based on improved ant colony optimization is introduced, which can improve the global and local search capabilities by using the preferred search direction and random neighborhood search mechanism. The effectiveness of the proposed APPMS method was demonstrated in three groups of simulated experiments with different degrees of terrain threat, and a real-data experiment with 3-D terrain data from an actual emergency situation.

Application of AI Techniques and Robotics in Agriculture: A Review

Abstract: The aim of the proposed work is to review the various AI techniques (fuzzy logic (FL), artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO), artificial potential field (APF), simulated annealing (SA), ant colony optimization (ACO), artificial bee colony algorithm (ABC), harmony search algorithm (HS), bat algorithm (BA), cell decomposition (CD) and firefly algorithm (FA)) in agriculture, focusing on expert systems, robots developed for agriculture, sensors technology for collecting and transmitting data, in an attempt to reveal their potential impact in the field of agriculture. None of the literature highlights the application of AI techniques and robots in (Cultivation, Monitoring, and Harvesting) to understand their contribution to the agriculture sector and the simultaneous comparison of each based on its usefulness and popularity. This work investigates the comparative analysis of three essential phases of agriculture: Cultivation, Monitoring, and Harvesting, by knowing the depth of AI involved and the robots utilized. The current study presents a systematic review of more than 150 papers based on the existing automation application in agriculture from 1960 to 2021. It highlights the future research gap in making intelligent autonomous systems in agriculture. The paper concludes with tabular data and charts comparing the frequency of individual AI approaches for specific applications in the agriculture field.

Optimal power flow solution with current injection model of generalized interline power flow controller using ameliorated ant lion optimization

Abstract: Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system’s performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.

Metaheuristic approach to optimal power flow using mixed integer distributed ant colony optimization

Abstract: This paper presents the application of an improved ant colony optimization algorithm called mixed integer distributed ant colony optimization to optimize the power flow solution in power grids. The results provided indicate an improvement in the reduction of operational costs in comparison with other optimization algorithms used in optimal power flow studies. The application was realized to optimize power flow in the IEEE 30 and the IEEE 57 bus test cases with the objective of operational cost minimization. The optimal power flow problem described is a non-linear, non-convex, complex and heavily constrained problem.

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

Abstract: Software needs modifications and requires revisions regularly. Owing to these revisions, retesting software becomes essential to ensure that the enhancements made, have not affected its bug-free functioning. The time and cost incurred in this process, need to be reduced by the method of test case selection and prioritization. It is observed that many nature-inspired techniques are applied in this area. African Buffalo Optimization is one such approach, applied to regression test selection and prioritization. In this paper, the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization. The proposed algorithm converges in polynomial time (O(n2)). In this paper, the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations. An astounding 62.5% drop in size and a 48.57% drop in the runtime of the original test suite were recorded. The obtained results are compared with Ant Colony Optimization. The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities (80%), and a reduction in the overall execution time and size of the resultant test suite. The results and analysis, hence, advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.

HWACOA Scheduler: Hybrid Weighted Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing

Abstract: With the advancement of technology and time, people have always sought to solve problems in the most efficient and quickest way possible. Since the introduction of the cloud computing environment along with many different sub-substructures such as task schedulers, resource allocators, resource monitors, and others, various algorithms have been proposed to improve the performance of the individual unit or structure used in the cloud environment. The cloud is a vast virtual environment with the capability to solve any task provided by the user. Therefore, new algorithms are introduced with the aim to improve the process and consume less time to evaluate the process. One of the most important sections of cloud computing is that of the task scheduler, which is responsible for scheduling tasks to each of the virtual machines in such a way that the time taken to execute the process is less and the efficiency of the execution is high. Thus, this paper plans to propose an ideal and optimal task scheduling algorithm that is tested and compared with other existing algorithms in terms of efficiency, makespan, and cost parameters, that is, this paper tries to explain and solves the scheduling problem using an improved meta-heuristic algorithm called the Hybrid Weighted Ant Colony Optimization (HWACO) algorithm, which is an advanced form of the already present Ant Colony Optimization Algorithm. The outcomes found by using the proposed HWACO has more benefits, that is, the objective for reaching the convergence in a short period of time was accomplished; thus, the projected model outdid the other orthodox algorithms such as Ant Colony Optimization (ACO), Quantum-Based Avian Navigation Optimizer Algorithm (QANA), Modified-Transfer-Function-Based Binary Particle Swarm Optimization (MTF-BPSO), MIN-MIN Algorithm (MM), and First-Come-First-Serve (FCFS), making the proposed algorithm an optimal task scheduling algorithm.

Improved ant colony optimization for the vehicle routing problem with split pickup and split delivery

Abstract: The vehicle routing problem (VRP) with split pick-up and delivery of multi-category goods is characterized by low carbon, demand splitting and simultaneous pick-up and delivery. In view of this, a mathematical model for optimizing vehicle routing with the objective of minimizing the total cost (comprising the fixed cost, carbon emission cost and penalty cost) is established by considering traffic conditions, satisfaction, and energy saving and emission reduction. A new improved ant colony optimization (ACO) algorithm is designed to solve the model and an initial solution is generated with pheromones of vehicles and a heuristic algorithm to ensure the quality of the initial population. A tabu search operator containing five neighborhood operators is constructed to improve the local search ability of the algorithm, and simulated annealing mechanisms are introduced to update global pheromones, so as to increase the diversity of populations. The effectiveness of the model and algorithm proposed in this study is verified through numerical simulation experiments on 18 groups of examples with different scales. The research results not only enrich relevant theories considering problems with demand splitting and the simultaneous pick-up and delivery, but also provide effective theoretical supports for decision making in logistics enterprises in the face of such complex problems.

HVDC Fault Detection and Classification with Artificial Neural Network Based on ACO-DWT Method

Abstract: Unlike the more prevalent alternating current transmission systems, the high voltage direct current (HVDC) electric power transmission system transmits electric power using direct current. In order to investigate the precise remedy for fault detection of HVDC, this research proposes a method for the HVDC fault diagnostic methodologies with their limits and feature selection-based probabilistic generative model. The main contribution of this study is using the wavelet transform based on ant colony optimization and ANN to detect the different types of faults in HVDC transmission lines. In the proposed method, ANN uses optimum features obtained from the voltage, current, and their derivative signals. These features cannot be accurate to use in ANN because they cannot give reliable accuracy results. For this reason, first, the wavelet transform applies to the fault and non-fault signals to remove the noise. Then the ACO reduces unimportant features from the feature vector. Finally, the optimum features are used in the training of ANN as faulty and non-faulty signals. The multi-layer perceptron used in the suggested method consists of many layers, enabling the creation of a probability reconstruction over the inputs by the model. A supervised learning method is used to train each layer based on the selected features obtained from the ant colony optimization-discrete wavelet transform metaheuristic method. The artificial neural network technique is used to fine-tune the model to reduce the difference between true and anticipated classes’ error. The input signal and sampling frequencies are changed to examine the suggested strategy’s effectiveness. The obtained results demonstrate that the suggested fault detection and classification model can accurately diagnose HVDC faults. A comparison of the Support vector machine, Decision Tree, K-nearest neighbor algorithm (K-NN), and Ensemble classifier Machine techniques is made to verify the suggested method’s unquestionably higher performance.

Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach

Abstract: Improving models for managing the networks of firefighting unmanned ground vehicles in crowded areas, as a recommendation system (RS), represented a difficult challenge. This challenge comes from the peculiarities of these types of networks. These networks are distinguished by the network coverage area size, frequent network connection failures, and quick network structure changes. The research aims to improve the communication network of self-driving firefighting unmanned ground vehicles by determining the best routing track to the desired fire area. The suggested new model intends to improve the RS regarding the optimum tracking route for firefighting unmanned ground vehicles by employing the ant colony optimization technique. This optimization method represents one of the swarm theories utilized in vehicles ad–hoc networks and social networks. According to the results, the proposed model can enhance the navigation of self-driving firefighting unmanned ground vehicles towards the fire region, allowing firefighting unmanned ground vehicles to take the shortest routes possible, while avoiding closed roads and traffic accidents. This study aids in the control and management of ad–hoc vehicle networks, vehicles of everything, and the internet of things.

An Efficient Allocation for Lung Transplantation Using Ant Colony Optimization

Abstract: A relationship between lung transplant success and many features of recipients’/donors has long been studied. However, modeling a robust model of a potential impact on organ transplant success has proved challenging. In this study, a hybrid feature selection model was developed based on ant colony optimization (ACO) and k-nearest neighbor (kNN) classifier to investigate the relationship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing (UNOS). The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’ functional status (i.e., quality of life) after lung transplantation. The efficacy of the proposed model was verified using 3,684 records and 118 input features from the UNOS. The developed approach examined the reliability and validity of the lung allocation process. The results are promising regarding accuracy prediction to be 91.3% and low computational time, along with better decision capabilities, emphasizing the potential for automatic classification of the lung and other organs allocation processes. In addition, the proposed model recommends a new perspective on how medical experts and clinicians respond to uncertain and challenging lung allocation strategies. Having such ACO-kNN model, a medical professional can summarize information through the proposed method and make decisions for the upcoming transplants to allocate the donor organ.

A Technique for Cluster Head Selection in Wireless Sensor Networks Using African Vultures Optimization Algorithm

Abstract: INTRODUCTION: Wireless Sensor Network (WSN) has caught the interest of researchers due to the rising popularity of Internet of things(IOT) based smart products and services. In challenging environmental conditions, WSN employs a large number of nodes with limited battery power to sense and transmit data to the base station(BS). Direct data transmission to the BS uses a lot of energy in these circumstances. Selecting the CH in a clustered WSN is considered to be an NP-hard problem. OBJECTIVES: The objective of this work to provide an effective cluster head selection method that minimize the overall network energy consumption, improved throughput with the main goal of enhanced network lifetime. METHODS: In this work, a meta heuristic based cluster head selection technique is proposed that has shown an edge over the other state of the art techniques. Cluster compactness, intra-cluster distance, and residual energy are taken into account while choosing CH using multi-objective function. Once the CHs have been identified, data transfer from the CHs to the base station begins. The residual energy of the nodes is finally updated during the data transmission begins. RESULTS: An analysis of the results has been performed based on average energy consumption, total energy consumption, network lifetime and throughput using two different WSN scenarios. Also, a comparison of the performance has been made other techniques namely Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Atom Search Optimization (ASO), Gorilla Troop Optimization (GTO), Harmony Search (HS), Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA) and Biogeography Based Optimization (BBO). The findings show that AVOA's first node dies at round 1391 in Scenario-1 and round 1342 in Scenario-2 which is due to lower energy consumption by the sensor nodes thus increasing lifespan of the WSN network. CONCLUSION: As per the findings, the proposed technique outperforms ABC, ACO, ASO, GTO, HS, WHO, PSO, FA, and BBO in terms of performance evaluation parameters and boosting the reliability of networks over the other state of art techniques.

Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model

Abstract: Internet of things (IOT) possess cultural, commercial and social effect in life in the future. The nodes which are participating in IOT network are basically attracted by the cyber-attack targets. Attack and identification of anomalies in IoT infrastructure is a growing problem in the IoT domain. Machine Learning Based Ensemble Intrusion Detection (MLEID) method is applied in order to resolve the drawback by minimizing malicious actions in related botnet attacks on Message Queue Telemetry Transport (MQTT) and Hyper-Text Transfer Protocol (HTTP) protocols. The proposed work has two significant contributions which are a selection of features and detection of attacks. New features are chosen from Improved Ant Colony Optimization (IACO) in the feature selection, and then the detection of attacks is carried out based on a combination of their possible properties. The IACO approach is focused on defining the attacker’s important features against HTTP and MQTT. In the IACO algorithm, the constant factor is calculated against HTTP and MQTT based on the mean function for each element. Attack detection, the performance of several machine learning models are Distance Decision Tree (DDT), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Mahalanobis Distance Support Vector Machine (MDSVM) were compared with predicting accurate attacks on the IoT network. The outcomes of these classifiers are combined into the ensemble model. The proposed MLEID strategy has effectively established malicious incidents. The UNSW-NB15 dataset is used to test the MLEID technique using data from simulated IoT sensors. Besides, the proposed MLEID technique has a greater detection rate and an inferior rate of false-positive compared to other conventional techniques.

An Efficient End-to-End Obstacle Avoidance Path Planning Algorithm for Intelligent Vehicles Based on Improved Whale Optimization Algorithm

Abstract: End-to-end obstacle avoidance path planning for intelligent vehicles has been a widely studied topic. To resolve the typical issues of the solving algorithms, which are weak global optimization ability, ease in falling into local optimization and slow convergence speed, an efficient optimization method is proposed in this paper, based on the whale optimization algorithm. We present an adaptive adjustment mechanism which can dynamically modify search behavior during the iteration process of the whale optimization algorithm. Meanwhile, in order to coordinate the global optimum and local optimum of the solving algorithm, we introduce a controllable variable which can be reset according to specific routing scenarios. The evolutionary strategy of differential variation is also applied in the algorithm presented to further update the location of search individuals. In numerical experiments, we compared the proposed algorithm with the following six well-known swarm intelligence optimization algorithms: Particle Swarm Optimization (PSO), Bat Algorithm (BA), Gray Wolf Optimization Algorithm (GWO), Dragonfly Algorithm (DA), Ant Lion Algorithm (ALO), and the traditional Whale Optimization Algorithm (WOA). Our method gave rise to better results for the typical twenty-three benchmark functions. In regard to path planning problems, we observed an average improvement of 18.95% in achieving optimal solutions and 77.86% in stability. Moreover, our method exhibited faster convergence compared to some existing approaches.

An Ant Colony Optimization-Simulated Annealing Algorithm for Solving a Multiload AGVs Workshop Scheduling Problem with Limited Buffer Capacity

Abstract: In this paper, we address a multiload AGVs workshop scheduling problem with limited buffer capacity. This has important theoretical research value and significance in the manufacturing field in considering the efficient multiload AGVs widely used today, and in the limited buffer area in production practice. To minimize the maximum completion time, an improved ant colony optimization-simulated annealing algorithm based on a multiattribute dispatching rule is proposed. First, we introduce a multiattribute dispatching rule, which combines two attributes, delivery completion time and input queue through dynamic weights that are determined by the information about the system, using the multiattribute dispatching rule to construct the initial solution. Then, with the ant colony optimization-simulated annealing algorithm as the basic framework, we propose a method for calculating transfer probability based on the multiattribute dispatching rule, which obtains heuristic information through the proposed rule. Further, we propose a path branch mechanism and dynamic equilibrium mechanism, aiming to efficiently construct the ant path and dynamically adjust ant path distribution. We propose a key job strategy and design a 2-opt neighborhood search method based on key jobs. Data experiments demonstrate the multiattribute dispatching rule is superior over other heuristic dispatching rules; the algorithm improvement strategies proposed are effective when used simultaneously or separately. Further, the proposed algorithm in this paper is superior over other heuristic algorithms and adapts to all kinds of instances.

Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments

Abstract: This paper addresses the problem of multi-robot task scheduling in Antarctic environments. There are various algorithms for multi-robot task scheduling, but there is a risk in robot operation when applied in Antarctic environments. This paper proposes a practical multi-robot scheduling method using ant colony optimization in Antarctic environments. The proposed method was tested in both simulated and real Antarctic environments, and it was analyzed and compared with other existing algorithms. The improved performance of the proposed method was verified by finding more efficiently scheduled multiple paths with lower costs than the other algorithms.

Two-Stage Optimal Task Scheduling for Smart Home Environment Using Fog Computing Infrastructures

Abstract: The connection of many devices has brought new challenges with respect to the centralized architecture of cloud computing. The fog environment is suitable for many services and applications for which cloud computing does not support these well, such as: traffic light monitoring systems, healthcare monitoring systems, connected vehicles, smart cities, homes, and many others. Sending high-velocity data to the cloud leads to the congestion of the cloud infrastructure, which further leads to high latency and violations of the Quality-of-Service (QoS). Thus, delay-sensitive applications need to be processed at the edge of the network or near the end devices, rather than the cloud, in order to provide the guaranteed QoS related to the reduced latency, increased throughput, and high bandwidth. The aim of this paper was to propose a two-stage optimal task scheduling (2-ST) approach for the distribution of tasks executed within smart homes among several fog nodes. To effectively solve the task scheduling, this proposed approach uses a naïve-Bayes-based machine learning model for training in the first stage and optimization in the second stage using a hyperheuristic approach, which is a combination of both Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In addition, the proposed mechanism was validated against various metrics such as energy consumption, latency time, and network usage.

Ant-Inspired Metaheuristic Algorithms for Combinatorial Optimization Problems in Water Resources Management

Abstract: Ant-inspired metaheuristic algorithms known as ant colony optimization (ACO) offer an approach that has the ability to solve complex problems in both discrete and continuous domains. ACOs have gained significant attention in the field of water resources management, since many problems in this domain are non-linear, complex, challenging and also demand reliable solutions. The aim of this study is to critically review the applications of ACO algorithms specifically in the field of hydrology and hydrogeology, which include areas such as reservoir operations, water distribution systems, coastal aquifer management, long-term groundwater monitoring, hydraulic parameter estimation, and urban drainage and storm network design. Research articles, peer-reviewed journal papers and conference papers on ACO were critically analyzed to identify the arguments and research findings to delineate the scope for future research and to identify the drawbacks of ACO. Implementation of ACO variants is also discussed, as hybrid and modified ACO techniques prove to be more efficient over traditional ACO algorithms. These algorithms facilitate formulation of near-optimal solutions, and they also help improve cost efficiency. Although many studies are attempting to overcome the difficulties faced in the application of ACO, some parts of the mathematical analysis remain unsolved. It is also observed that despite its popularity, studies have not been successful in incorporating the uncertainty in ACOs and the problems of dimensionality, convergence and stability are yet to be resolved. Nevertheless, ACO is a potential area for further research as the studies on the applications of these techniques are few.

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

Abstract: 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: problem model, optimization framework, and algorithm design. 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 in DTSP is caused by the change of node location, and therefore the new environment is somehow similar 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.

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Abstract: Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.