Improved Ant Colony Optimization Algorithm Research Articles

Improved ant colony optimization algorithms for multi-objective investment decision model based on intelligent fuzzy clustering algorithm

With the continuous changes and development of financial markets, it has brought many difficulties to investment decision-making. For the multi-objective investment decision-making problem, the improved Ant colony optimization algorithms was used to improve the effectiveness and efficiency of the multi-objective investment decision-making. Therefore, based on intelligent Fuzzy clustering algorithm and Ant colony optimization algorithms, this paper studied a new multi-objective investment decision model, and proved the advantages of this method through comparative analysis of experiments. The experimental results showed that the improved Ant colony optimization algorithms has significantly reduced the system’s construction costs, operating costs and financial costs, all of which were controlled below 41%. Compared with the traditional Ant colony optimization algorithms, this method had lower values in policy risk, technical risk and market risk, and can effectively control risks. Meanwhile, the environmental, economic, and social benefits of this method were all above 58%, and the average absolute return rate and success rate in this experiment were 21.5450% and 69.4083%, respectively. Therefore, from the above point of view, the multi-objective investment decision model based on intelligent Fuzzy clustering algorithm and the improved Ant colony optimization algorithms can effectively help decision-makers to find the best investment decision-making scheme, and can improve the accuracy and stability of decision-making. This research can provide reference significance for other matters in the field of investment decision-making.

Whether search directions number affects the efficiency of the path planning algorithm: Taking an improved ACO algorithm with 32 directions for example

An improved Ant Colony Optimization (ACO) algorithm, named IACO, is proposed to address the inherent limitation of slow convergence, susceptibility to local optima and excessive number of inflection in traditional ACO when solving path planning problems. To this end, firstly, the search direction number is expanded from 4 or 8 into 32; Secondly, the distance heuristic information is replaced by an area heuristic function, which deviated from the traditional approach that only considers pheromone information between two points; Then, the influence of path angle and number of turns is taken into account in the local pheromone update. Additionally, a reward and punishment mechanism is employed in the global pheromone update to adjust the pheromone concentrations of different paths; Furthermore, an adaptive update strategy for pheromone volatility factor adaptive is proposed to expand the search range of the algorithm. Finally, simulation experiments are conducted under various scenarios to verify the superiority and effectiveness of the proposed algorithm.

A matheuristic for aircraft maintenance routing problem incorporating cruise speed control

Aircraft maintenance routing problem incorporating cruise speed control (AMRP-CSC) is an extension of the well-known aircraft maintenance routing problem (AMRP) where the flexible cruise times of flights are considered in route construction. Changing cruise times in AMRP can transform the infeasible flight connections into feasible ones, resulting in a larger solution space and further opportunities for efficiently routing. This may open up the possibility for a substantial improvement in aircraft utilization, but also increases resolution complexity. In this study, a new solution methodology, namely a matheuristic approach, is proposed for this problem. It is composed of three main components: an improved ant colony optimization (IACO) algorithm, a set partitioning (SP) procedure, and a neighborhoods search (NS) procedure. The IACO algorithm serves as a route generator, populating a pool of routes with promising feasible aircraft maintenance routes. Then, a SP model, which features the high-quality columns corresponding to the routes in the pool, is solved to produce a possible better solution. Finally, this solution is further improved by a NS procedure that iteratively solves the reduced AMRP-CSC instances to optimality. This matheuristic approach is analyzed and tested using the data extracting from the Bureau of Transportation Statistics (BTS), and then its accuracy and the efficiency have been demonstrated by experiment analyses.

Heterogeneous Fleets for Green Vehicle Routing Problem With Traffic Restrictions

Suffering from environmental distress like carbon emissions, traffic restrictions have been enforced extensively in distribution logistics. Reasonable arrangement of urban freight transportation can effectively improve distribution efficiency, reduce distribution costs, and alleviate the impact of traffic restrictions in distribution logistics. In response to increasingly stringent traffic restrictions, we establish a multi-objective optimization model, including the minimum distributions and the minimum carbon emissions. Given that the limits of battery capacity and cargo capacity, we build a green vehicle routing problem with soft time windows (GVRPTW) model with heterogenous fleets. In this study, three different factors, that is restricted area, travel time of vehicles, and carbon tax prices, are discussed in details. In order to solve the NP-hard model, we propose an improved ant colony optimization algorithm (IACO) by optimizing the state transition probability, and verifies the worth of the algorithm. The experimental results can explore the impacts of traffic restriction policies on the formulation of distribution scheme and offer reference opinions for the government to formulate reasonable restriction policies and better guide logistics enterprises to reduce carbon emissions.

Joint RMSSA scheme and link load balancing for static SD-SCNs from model building to algorithm design

The spatial channel network (SCN) is a new optical network architecture that can support traffic requests from 100 Gb/s to 10 Tb/s and beyond. Software-defined networking (SDN) has superb management and control capabilities, making it easier to obtain network topology and link load information with it than with traditional networks. Combining the advantages of both, we propose a multi-plane SDN-based spatial channel network (SD-SCN) architecture and focus on solving the routing, modulation format, spatial lane, and spectrum block assignment (RMSSA) problem in static SD-SCNs. In this paper, we build a multi-plane network model of an SD-SCN, design the functional modules of each plane, and give the operation principle of the model. Furthermore, we formulate the RMSSA problem in the static SD-SCN model and propose a heuristic spatial lane and spectrum block minimization and load balancing (MLB2) algorithm to solve the problem. Also, we design an improved ant colony optimization (IACO) algorithm (the heuristic function and iteration criterion are optimized) to implement the routing path assignment function of the MLB2 algorithm. The simulation results show that the MLB2 algorithm can carry traffic requests with minimum network resources, and when the total traffic rate is high, the link load can be balanced by invoking the IACO algorithm to increase the network throughput. The MLB2 algorithm performs better in small-scale networks than in large-scale networks.

DESLR: Energy-efficient and secure layered routing based on channel-aware trust model for UASNs

Underwater acoustic sensor networks (UASNs) have garnered significant interest and have found widespread applications in smart ocean applications and auxiliary navigation. Nevertheless, UASNs encounter a multitude of challenges due to the constraints of the underwater environment, such as high energy consumption, secure transmission, and dynamic network topology in uncertain conditions. In this paper, we propose an energy-efficient and secure layered routing method (DESLR) for UASNs to enhance energy efficiency and improve transmission reliability. The DESLR establishes a channel-based trust model which considers factors of direct trust and indirect trust to describe the behaviors of nodes. In order to effectively identify malicious nodes, great consideration is given to the state of the acoustic channel’s reliability, as varying channel conditions can significantly impact packet transmission. An improved Ant Colony Optimization Algorithm (ACOA) is introduced to discover energy-efficient and low-latency routing paths by balancing energy consumption and routing distance. Additionally, DESLR utilizes a Layered Clustering method to group nodes into clusters and a two-layer fuzzy logic approach to select cluster heads (CH) that avoid hotspots and ensure uniform energy distribution. Simulation results demonstrate that DESLR can effectively detect defective nodes by analyzing anomalous behaviors and outperforms other related work in terms of energy consumption, average end-to-end delay, and packet loss.

Improved Ant Colony Optimization for the Operational Aircraft Maintenance Routing Problem with Cruise Speed Control

The operational aircraft maintenance routing problem (OAMRP) plays a critical part in producing considerable cost reductions for airlines, since its solution directly influences the number of operating leased aircraft. To reduce the quantity of required aircraft, adopting cruise speed control in OAMRP is a good strategy. In this paper, we investigate the OAMRP with cruise speed control. The objective is to minimize the required quantity of aircraft by finding the optimal aircraft routes through cruise time optimization. The focus is on solving two issues simultaneously: (i) optimization of cruise times and (ii) determination of aircraft routes. Since the combination of two intricate sets of decisions poses significant methodological challenges, the difficulty lies in how to efficiently solve it. Accordingly, the goal of this study is twofold: (i) to design a preprocessing step to reduce the network size and (ii) to develop an improved ant colony optimization (IACO) algorithm with a new state transition mechanism to provide the guidance for cruise times optimization and a new pheromone updating mechanism to enhance the search efficiency and precision. Using data from the Bureau of Transportation Statistics (BTS), we demonstrate the computational efficiency of the preprocessing step and the IACO algorithm.

Hybrid IACO-A*-PSO optimization algorithm for solving multiobjective path planning problem of mobile robot in radioactive environment

Mobile robots are receiving significant interest in the nuclear energy industry because of their potential intelligence features and efficient operation. Mobile robot path planning (PP) in a radioactive environment can be considered as finding a collision-free path constrained by the path length, cumulative radiation dose rate and energy consumption. To solve this multiobjective path planning (MOPP) problem, we propose a hybrid algorithm based on an improved ant colony optimization (IACO), the A* algorithm and particle swarm optimization (IACO-A*-PSO). First, a modified A* algorithm is presented to find a suboptimal path, which is used to improve the initial ACO pheromone. Next, an improvement of the adaptive heuristic function and pheromone update rule based on an elitist system is proposed to balance the global search ability and convergence speed of the ant colony algorithm. Finally, PSO is used to obtain the optimal ACO control parameters and multiobjective weight coefficients. To evaluate the efficiency of the proposed algorithm, a comparative study has been performed between the proposed IACO-A*-PSO, the A*, ACO, and IACO algorithms and a genetic algorithm (GA) in four radioactive indoor environments with different sizes, radioactive sources and obstacles. The simulation results show that the proposed algorithm has the best comprehensive performance and stronger adaptability to different environments than the A*, ACO, IACO and GA algorithms in terms of the path length, cumulative radiation dose, energy consumption, runtime and success rate simultaneously. This demonstrates that the proposed algorithm is an effective method for solving the MOPP problem of mobile robot in radioactive environment, which is beneficial for improving the safety and reliability of robots in nuclear energy.

To Use an Ant Colony Technique to Solve a Crispy Type Bi- and Tri-Objective Transportation Problem

Transportation problem (TP) aims to reduce the entire transportation cost of moving resources from various supply hubs to various demand hubs. However, in real-life situations, all organizations want to achieve numerous objectives while making transportation of goods. The degree of deterioration may vary depending on the mode, route, and time of transport. In some cases, the multiobjective could be used to reduce the use of a scarce resource, such as energy. As a result, the proposed approach was discovered to be an algorithm that improves bi- and triobjective TP techniques. This is an innovative way for solving the new bi- and triobjective transportation algorithm using a modified ant colony optimization (ACO) algorithm. According to the literature, various strategies have been developed in the past to tackle the multiobjective transportation problem (MOTP). The MOTP is solved using goal programming, fuzzy programming, interactive solution algorithms, and other techniques. These strategies are occasionally good or bad in achieving better results in a reasonable amount of time. The heuristic technique used in this work is the improved ACO algorithm, which is based on the ant colony algorithm and has been found to provide solutions with a reasonable degree of satisfaction for two and three objective TPs. When the findings are compared, the solution achieved using the proposed method has delivered the best performance and provides a case study to show the new strategy.

Improving the Transmission Efficiency of a WSN with the IACO Algorithm

The goal of this study is to reduce the energy consumption of the sensing network and enhance the overall life cycle of the network. This study proposes a data fusion algorithm for wireless sensor networks based on improved ant colony optimization (IACO) to reduce the amount of data transmitted by wireless sensor networks (WSN). This study updates pheromones for multiple optimal routes to improve the global optimal route in search function. The algorithm proposed in this study can reduce node energy consumption, improve network load balancing and prolong network life cycle. Through data fusion, regression analysis model and information processing of each node, this study uses an improved ant colony algorithm to identify the transferals avoid superfluous energy waste caused by long-span network transferal, set the shortest route and transmit data to the central node. The algorithm proposed in this study is conducive to improving the life cycle and stable network, that is, the most suitable and effective way to improve the energy consumption rate of the sensing nodes.

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

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.

English teaching resources recommendation based on improved Ant colony optimization algorithms

To achieve the goal of exploiting all the advantages and avoiding the disadvantages, this paper presents a genetic algorithm-based development to integrate the spider colony algorithm for teaching optimization of English data recommendation methods. This algorithm first takes advantage of the genetic algorithm to extract the pheromone required by the ant colony algorithm, and then obtains a good solution through the fast and comprehensive integration mechanism of the global optimization of the ant colony algorithm. Therefore, combining the two algorithms can increase the processing time and efficiency. According to the experimental results, compared to using the genetic algorithm or the ant colony algorithm, the violation of the soft parameters of the training data based on the genetic ant hybrid algorithm is reduced to a minimum, and the feasibility and quality of the training data are good. alone. The power of our algorithm is directly proportional to the number of assignments. The security of the algorithm seems to be stable when the number of scales reaches one. For the same scale, the power of the hybrid genetic colony algorithm is the highest, the ant colony algorithm is the second, and the genetic algorithm is the last. When the class setting is 500, when the motor reaches 300, the algorithm stops working and the test is repeated 10 times. The genetic ant colony hybrid algorithm needs the shortest time to reach the fitness value. Conclusion: this algorithm is much better than genetic algorithm or spider colony algorithm.

Multi-Objective Optimal Travel Route Recommendation for Tourists by Improved Ant Colony Optimization Algorithm

With the swift development of tourism all around the world, it has become vital to improve the recommendation of useful travel information to tourists to assure their convenience and satisfaction. In this paper, we propose a novel multi-objective optimal travel route recommendation framework, which collects tourists’ travel trajectories from their mobile phone signaling data. Then, the proposed framework preprocesses the mobile signaling data to transform raw trajectories into tourists’ travel sequences. Subsequently, the framework finds the popular attractions and frequent travel routes from the travel pattern sequences by using a frequent pattern mining method. Finally, an improved ant colony optimization (ACO) algorithm with a novel extensible heuristic factor approach is adopted to search the multi-objective optimal travel routes according to the popularity of attractions and travel time of tourists. The experimental results indicate that the proposed framework is efficient in recommending multi-objective optimal travel routes considering tourists’ travel time and attractions’ popularity while ensuring that the recommended travel route is suitable.

Risk Propagation Evolution Analysis of Oil and Gas Leakage in FPSO Oil and Gas Processing System by Mapping Bow-Tie into Directed Weighted Complex Network

An innovative methodology is proposed to identify potential risk factors and possible accident escalation consequences, and to determine the evolution of an accident from cause to consequence, thereby to identify the most probable path and discover key risk factors along the path rapidly. Based on the principle of a directed weighted complex network (DWCN), the bow-tie (BT) model, risk entropy and the improved ant colony optimization (IACO) algorithm are integrated into this methodology. First, the qualitative analysis of risk evolution based on the BT model is carried out. The evolution development based on accident suppression can be divided into two stages: accident precursor stage and accident evolution stage. Then, a new method for mapping BT into DWCN is proposed. Lastly, the shortest path analysis of risk evolution based on the IACO algorithm is carried out, fuzzy set theory (FST) is introduced to calculate the failure probability of risk factors, and risk entropy is used to represent the uncertainty of risk propagation. Thus, the IACO algorithm can be used to calculate the shortest path of risk evolution. The proposed method is applied to oil and gas leakages in the FPSO oil and gas processing system. The results show that it is an effective method to identify the shortest evolution path and the most vulnerable risk factors.

An Improved Ant Colony Optimization Algorithm for Vehicle Routing Problem with Time Windows

Distribution plays an important role in the supply chain system. One of the critical problems in distribution is the vehicle routing problem. This research proposes the Improved Ant Colony Optimization (IACO) algorithm to solve the Vehicle Routing Problem with Time Windows (VRPTW). The main objective is to minimize the total vehicle mileage by considering the vehicle capacity and customer time windows. The proposed IACO algorithm is inspired by the conventional Ant Colony Optimization (ACO) algorithm by adding local search and mutation processes. Numerical experiments were conducted to test that the routes generated did not violate the customer's time window constraints. In addition, this study also compares the proposed IACO algorithm routes with other metaheuristic algorithms, namely ACO classic and Tabu Search. In addition, this investigation was carried out by experimenting with the number of iterations. The results of numerical experiments prove that the proposed IACO algorithm can minimize the total vehicle mileage without violating capacity constraints and time windows.

Research on global path planning of unmanned vehicles based on improved ant colony algorithm in the complex road environment

When planning the path of a non-urbanized road, the default ant colony optimization (ACO) algorithm does not consider complex road state function such as uneven surface, road attachment coefficient, and vehicle turning angle limit. Based on the actual situation of roads and vehicles, a pavement state function that considers uneven areas such as road bumps and pavement attachment is proposed to improve the description of path length. Then, a heuristic function based on the A* algorithm and an improved mechanism for the initialization of pheromone distribution is proposed, which changes the blindness of ant colony search, accelerates the convergence of the ACO, and improves the search efficiency. The global search capability of the algorithm is enhanced by improving the path selection strategy and path transition probability function. The pheromone updating method is improved by using the MAX-MIN Ant System, which increases the algorithm diversity and avoids local optima. Further, using the pruning algorithm to reduce the number of paths significantly increases the convergence speed. Simulation results show that the improved ACO algorithm has better convergence speed and global search ability. Combining road state processing with vehicle corner processing can effectively improve the safety, adaptability, and reliability of autonomous vehicles. And the global optimal path planning of unmanned vehicles on complex roads can also be realized.

An Overall Improved Ant Colony Optimization algorithm trained BPNN for PV MPPT

A novel PV MPPT algorithm based on the overall improved ant colony optimization algorithm-trained BP neural network (OIACO-BPNN) has been proposed in this paper to overcome the poor prediction accuracy and slow convergence rate of the BP Neural Network (BPNN). Firstly, the pheromone updating model of the Ant Colony Optimization (ACO) algorithm is improved, and the weight coefficient is added to improve the convergence rate of the ACO algorithm. Secondly, the optimal weight threshold of BPNN is updated by Overall Improved Ant Colony Optimization (OIACO) algorithm. Thirdly, the optimized BPNN is employed to predict the Maximum Power Point (MPP) voltage of the photovoltaic (PV) array. Finally, the deviation value between the voltage of the PV array and the predicted voltage is employed as the input of PID controller. In addition, the duty cycle of the Boost circuit is adjusted by PID controller to achieve MPPT. Matlab/Simulink is employed to verify the feasibility and effectiveness of the proposed MPPT algorithm. Simulation results illustrate that the OIACO-BPNN algorithm is superior to the ACO and the BPNN in prediction accuracy and tracking performance, moreover has a good robustness and response speed.

Human Gait Recognition Using Deep Learning and Improved Ant Colony Optimization

Human gait recognition (HGR) has received a lot of attention in the last decade as an alternative biometric technique. The main challenges in gait recognition are the change in in-person view angle and covariant factors. The major covariant factors are walking while carrying a bag and walking while wearing a coat. Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are presented in the literature. The requirement of an efficient framework is always required for correct and quick gait recognition. We proposed a fully automated deep learning and improved ant colony optimization (IACO) framework for HGR using video sequences in this work. The proposed framework consists of four primary steps. In the first step, the database is normalized in a video frame. In the second step, two pre-trained models named ResNet101 and InceptionV3 are selected and modified according to the dataset's nature. After that, we trained both modified models using transfer learning and extracted the features. The IACO algorithm is used to improve the extracted features. IACO is used to select the best features, which are then passed to the Cubic SVM for final classification. The cubic SVM employs a multiclass method. The experiment was carried out on three angles (0, 18, and 180) of the CASIA B dataset, and the accuracy was 95.2, 93.9, and 98.2 percent, respectively. A comparison with existing techniques is also performed, and the proposed method outperforms in terms of accuracy and computational time.

Meta-Heuristic Algorithms for the Generalized Extensible Bin Packing Problem With Overload Cost

In this paper, we consider a generalized the extensible bin packing problem with overload cost, first proposed by Denton et al. [1] in 2010, in which the total size of items packed into a bin is allowed to exceed its capacity, and the cost incurred each bin is equal to the fixed cost plus the overload cost, the objective is to minimize the total cost of all bins. According to the characteristics of the problem, we first propose an improved ant colony optimization algorithm (IACO), which enhances the positive feedback effect of ACO by improving the update method of pheromone and the adaptive adjustment parameters. We also introduce a variable neighborhood search method in ACO to improve the convergence of the algorithm and get rid of the phenomenon of local extrema. Then, we present a discrete particle swarm optimization algorithm (DPSO) to solve the problem. In order to ensure the uniform distribution and high quality of the initial particle swarm, we use some heuristic methods in the initialization process of the swarm, so that the initial particle can cover the entire search space with a large probability, which effectively improves the performance of DPSO algorithm. Finally, we compare and analyze the performance of these proposed algorithms through two sets of computational experimental frameworks. Compared with some algorithms in the literature, computational results signify that the improved ACO algorithm and MDPSO algorithm are more competitive than some other metaheuristic algorithms..

Feature Selection for Cross-Scene Hyperspectral Image Classification via Improved Ant Colony Optimization Algorithm

Hyperspectral images (HSIs) generally contain a large amount of spectral bands (features), and the redundant information in them will cause the Hughes phenomenon in the classification process. And feature extraction and feature selection are the two main existing methods to effectively reduce the redundancy of spectral information in the field of HSIs classification. Compared with feature extraction methods, feature selection methods can preserve most of the features of the original HSIs data without losing their valuable details. However, most existing feature selection methods based on single scene (domain) perform poorly in some scenes (domains) with insufficient labeled samples. Therefore, how to adopt an efficient feature selection method to select the optimal feature subsets of source scene and target scene and use the sample information of source scene to assist in the classification of target scene so as to improve the classification accuracy of images in the target scene as much as possible is still very challenging. In order to solve the above problem, this paper proposes a new cross-scene algorithm: Improved Ant Colony Optimization Algorithm-Based Cross-Scene Feature Selection Algorithm (IMACO-CSFS). In order to obtain more accurate feature subsets of the two scenes, IMACO-CSFS proposes a priority sorting-based ant colony strategy to make the subsequent search process focus on the global optimal solution (optimal feature subset) found in the previous iteration. In addition, in order to further accelerate the convergence speed of the global optimal solution, an ant colony strategy based on elite ants is proposed in IMACO-CSFS to more efficiently obtain the optimal feature subsets of the two scenes for training the classifier. Furthermore, this paper simultaneously considers overall classification accuracies of the optimal feature subsets for both scenes and dynamically adjusts their scale to ensure the consistency of the selected features between the two scenes, attenuating the effect of spectral shift and achieving the higher image classification accuracy in the target scene. Experimental results on three cross-scene HSI data pairs demonstrate that IMACO-CSFS is superior in cross-scene feature selection and cross-scene HSIs classification.