Hybrid Ant Colony Optimization Research Articles

Multi-Fault Diagnosis Based on Hybrid Bio-Inspired Algorithm ACO-GA

The fault detection and isolation (FDI) procedure increases the assurance of quality, reliability, and safety of industrial systems. Several faults may appear simultaneously, and the purpose of multi-fault diagnosis is to identify and locate these multiple faults. This work is particularly interested in the diagnosis based on the structural analysis of the system; residuals can be generated and used as fault indicators by model-based fault detection techniques. The isolation is dependent on the structure of the fault signature matrix. A new fault signature that represents the superposition of the fault is produced by simultaneous fault effects, resulting in an additional column in an extended signature matrix. This remedy is rather combinatorial. This research focuses on two methods to isolate multiple faults: (1) A modified enumerative method; (2) A hybrid ant colony optimization algorithm-genetic algorithm (Hybrid ACO-GA) is adapted to the MFD problem which has the advantage of a better research as well as the hybridization with GA.

A hybrid ant colony optimization algorithm to solve a transport problem with stochastic customer demands and electric vehicles of limited load capacity

A hybrid ant colony optimization algorithm to solve a transport problem with stochastic customer demands and electric vehicles of limited load capacity

An Enhanced Location-Aided Ant Colony Routing for Secure Communication in Vehicular Ad Hoc Networks

The dynamic characteristics of vehicular ad hoc networks (VANETs) demand reliable and secure communication over wireless media. However, there are significant contradictions in autonomous vehicular systems related to security and privacy. Furthermore, VANETs require seamless data transmission to exchange time-critical data about accidents and traffic. Therefore, VANETs require secure seamless data transmission mechanisms to provide secure reliable communication without communication interruption to exchange time-critical data between vehicles. This paper proposes an Enhanced Location-Aided Ant colony Routing (ELAACR) for VANETs to achieve seamless secure data transmission. The proposed ELAACR is a two-fold scheme in which Phase 1 implements Location-Aided Key Management (LAKM) and Phase 2 implements Ant Colony Routing (ACR). An ACR combined with an LAKM pattern for offering secure and reliable communication. The LAKM scheme works based on the group signature concept to construct a secure network, and an ACR is used to find an efficient secure shortest path for seamless data transmission. The proposed work is simulated in Network Simulator (NS 2.35). By using an efficient and secure shortest path with secure seamless data transmission, the proposed ELAACR achieves an average throughput (T) of 640 Kbps, packet loss rate (PLR) of 0.018%, packet delivery ratio (PDR) of 98.5%, overhead (OH) of 0.157%, and 0.16 s end-to-end delay (EED) compared to Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) and Fuzzy Based Ant Colony Optimization (F-ANT). The simulation results show that the proposed ELAACR registers better performance in all aspects compared to the existing EHACORP and F-ANT.

Advances and Challenges in Feature Selection Methods: A Comprehensive Review

The feature selection area in data analytics is explored through a comprehensive literature review, and the increasing areas that have a data dependency problem and are being resolved with feature selection are highlighted. Review topics of this course cover the foundations to present use cases, for example, cybersecurity, healthcare, and finance. Particularly crucially for the healthcare domain, it reduces the dimensionality and elucidates complex causal links. The further investigation overlaps contemporary techniques, including optimization-based methods, swarm intelligence and algorithms for the diagnosis of heart diseases. The conclusion builds on the practical assessment and underlines research gaps, serving as a basis to set a diversified technological review. This also exhibits new techniques that have released their efficiency in classification environments, for example, hybrid Ant Colony Optimization and the Gray Wolf Optimizer. The ISSA algorithm stands out as a swarm intelligence technique that is best among others. The paper concludes by demonstrating that feature selection goes beyond the preprocessing stage, but it instead stands as a vital part of the fields of machine learning and data science and thus aids the researchers in both retrospective analysis and forthcoming projects.

An attempt to resolve no-wait flow shop scheduling problems using hybrid ant colony and whale optimization algorithms

The incentive for many developments and scientific progresses within the field of scheduling has emerged from industrial environments, and naturally, it could be utilized in expressing the scheduling concepts regarding terms used in the industry. Generally speaking, scheduling problems are known as limited optimization issues through which decisions related to the machines’ assignment and works processing sequence are probed. Thus, following a review of the related literature, the major goal of this research is to design a mathematical model and to solve it through a meta-heuristic for no-wait flow shop scheduling problem using different machines for the purpose of minimizing the time required to complete the work using whale and ant colony optimization (ACO) algorithms in Sanat-Gostar-e-Hamgam Shoe Company. The ACO and whale algorithm methods are used to compare and predict scheduling activities in manufacturing line of shoe industry. The results showed an ACO algorithm with two stages in mean ideal distance (MID) end amounting to 76.65 and 77.38, respectively. Also, regarding the amounts of standard error mean squares, it could be claimed that the model designed using the improved whale algorithm has a better prediction, and the minimum time required to complete works using the whale algorithm is estimated to be equal to 86.1071. This could lead to an optimal state in achieving the predetermined goals.

Swarm Intelligence-Inspired Meta-Heuristics Hybrid Optimization for Multi-Constraint Routing in Vehicular Adhoc Networks

In recent years, the popularity of vehicle adhoc networks (VANET) in wireless intelligent transportation systems has significantly increased. It is difficult to determine the quickest path between the source and the target in a VANET traffic system. Longer routes feature increased network overhead, more expensive connections, more path failures, and worse routing efficiency. Identifying the shortest route in optimization, often known as the Travelling Salesman Problem (TSP) and traffic congestion, is a well-known combinatorial optimization challenge with various practical applications. To improve routing efficiency, the HACOSMO (Hybrid-Ant Colony Optimization with Spider Monkey Optimization) system’s recommended meta-heuristic approach finds the shortest path using distance and traffic based principles. The simulation (NS-2) outcomes focused on the efficiency of the proposed method beats the other existing methods like ACO, GRACO, OACO, and IDBACOR in terms of overhead, throughput, latency, packet failure, and message transmission ratio. According to HACOSMO, routing overhead is 8% to 11% under IDBACOR, 10% to 14% under GRACO,19% to 31% under OACO, and 24% to 37% under ACO for a variety of vehicle counts and speed ranges. When compared to IDBACOR, the proposed method enhances throughput by 3% to 7%, 6% to 8% when compared to GRACO, 11% to 15% when compared to OACO, and 16% to 25% when compared to ACO.

Mobile robot path planning based on hybrid ant colony optimization

To solve the problems of the ant colony optimization (ACO), such as slow convergence speed, easy to fall into local extremum and deadlock in path planning, this paper proposed an improved ACO, which was hybridized by PSO based on logistic chaotic mapping, called hybrid ant colony optimization (HACO). According to the number of obstacles around the next feasible node, HACO distributes the initial pheromones unevenly to avoid the ant getting stuck in deadlock. According to the orientation of the next node selected by the ant, the heuristic information is adaptively adjusted to guide the ant to the direction of the target position. When updating the pheromone, the local and global search mechanism of the particle swarm optimization is used to improve the pheromone update rule and accelerate convergence speed. Finally, the grid method is used to construct the environment map, and simulation experiments are conducted in different environments. The experimental results verify the effectiveness and feasibility of the improved algorithm.

A dimensionless model and ant colony optimization fusion temperature prediction in tunnel fires

Prior studies have noted the importance of ceiling temperature distribution prediction of tunnel fires. To overcome the limitations of precise modeling difficulties for model-driven methods and biased toward the convergence for the data-driven methods due to loss of physical interpretations, a hybrid model-driven and data-driven fusion algorithm is established to predict ceiling temperature distribution in tunnel fires based on dimensional analysis method and ant colony optimization algorithm. Thus, full-scale tunnel fire experiments with different ventilation speeds are conducted to support the algorithm. In contrast to the experimental data as well as the known theoretical model, data-driven back propagation neural network algorithm and hybrid ant colony optimization and back propagation neural network algorithm, the effectiveness and ability of the algorithm are verified, which can be used for the tunnel fire prediction.

A Product-Design-Change-Based Recovery Control Algorithm for Supply Chain Disruption Problem

In very recent years, large-scale disruptions brought by major global and local emergencies have posed many challenges with respect to the recovery control of supply chain systems. This work investigates a problem regarding the optimal control of a supply chain by considering product design change in order to enable manufacturers to recover their disrupted supply chain quickly. A two-layer optimization model is developed, in which the lower model is used to optimize the product design change path, and the upper model is used to select the appropriate alternative suppliers and schedule the delivery of customer orders. To solve the developed model, a hybrid ant colony optimization (HACO) algorithm is designed, which is combined with a Gurobi solver and uses some special strategies. The validity of the proposed algorithm is illustrated experimentally through computational tests and systematic comparison with the existing methods. It is reported that the losses caused by supply chain disruptions are reduced significantly. The proposed model and algorithm can provide a potentially useful tool that can help manufacturers decide upon the optimal form of recovery control when a supply chain system experiences a massive supply disruption.

Electric Vehicle Routing Problem With Variable Vehicle Speed and Soft Time Windows for Perishable Product Delivery

This work studies perishable products’ distribution using electric commercial vehicles (ECVs). Extra energy is consumed for refrigeration to keep such products from deteriorating, which shortens the limited driving range of ECVs. Besides charging at public recharging stations, their travel speed can be adjusted to improve their driving range and decrease distribution cost. We propose an Electric Vehicle Routing problem with variable vehicle speed and soft time windows for perishable products (EVRP-VS). An energy consumption rate function of refrigerated ECVs during driving is introduced into the problem. The function considers refrigeration and has a nonlinear relationship with ECV speed and weight. As long as the carriage is not empty, the vehicle needs refrigeration during driving and during its stay at customers and stations. A mathematical programming model is developed for EVRP-VS, to minimize total distribution cost, including vehicle cost, power cost, refrigeration cost, and penalty cost due to delayed delivery. An adaptive hybrid ant colony optimization (AHACO) with a two-stage speed optimization strategy is proposed to solve EVRP-VS. In the first stage, local speed optimization is used to optimize the speed of each ant (ECV) in each transfer step. In the second one, global speed optimization further optimizes the speed for each fixed route constructed by ants. AHACO is applied to many problem instances. Experimental results show that it can effectively solve EVRP-VS in comparison with CPLEX and other meta-heuristics.

A new approach based on hybrid ant colony optimization-artificial bee colony algorithm for multi-objective electric vehicle routing problems

In real-life, routing plans try to optimize multiple objectives without relying on just one. So, this paper introduces three multi-objective electric vehicle routing problems (MOEVRP) that consider different charging strategies and electric vehicle (EV) charger types while optimizing five conflicting objectives: a total minimization cost of recharging, the number of vehicles required, a total travel distance, load-dependent energy consumption, and the total number of charging stations required. We develop a new hierarchical approach consisting of two phases: a Hybrid Ant Colony Optimization (HACO) and an Artificial Bee Colony Algorithm (ABCA). In the first phase, an initial solution is obtained using a HACO that integrates local search algorithms and simulated annealing (SA) to reduce the solution time. Then in the second phase, the problem is solved using an ABCA considering the initial solution obtained from the first phase. Using the proposed HACO-ABCA as the search engine, two posteriors’ methods, namely the weighted-sum method (WSM) and the conic method (CM), are applied to scalarize the five objectives. The effectiveness of the proposed hierarchical approach is examined on well-known test-based instances and obtained the best new results in most instances. Additionally, the proposed solution is applied to a real-life case study. The results show that multi-objective traditional methods give more effective results than multi-objective evolutionary algorithms, regardless of the MOEVRP problem type. We can also conclude that the partial recharge and multiple recharge technology options can significantly improve the route decisions of logistic companies.

A fine-grained task scheduling mechanism for digital economy services based on intelligent edge and cloud computing

Digital economy is regarded countries as an inevitable choice to promote economic growth and provides new opportunities and new paths for the high-quality development of economy. For the Chinese market, the strong base behind cloud computing is the unstoppable development trend of the digital economy. In digital economy, the cloud as infrastructure becomes the base of the pyramid to build the digital economy. To relieve the pressure on the servers of the digital economy and develop a reasonable scheduling scheme, this paper proposes a fine-grained task scheduling method for cloud and edge computing based on a hybrid ant colony optimization algorithm. The edge computing task scheduling problem is described, and assumptions are set to simplify the difficulty of a scheduling solution. The multi-objective function is solved by using a hybrid ant colony optimization algorithm which solves computational problems by finding the optimal solution with the help of graphs. Ant colony optimization algorithm is easy to use and effective in scheduling problems. The proposed scheduling model includes an end-device layer and an edge layer. A terminal device layer consists of devices used by the clients that may generate computationally intensive tasks and are sometime uncapable to complete the tasks. The proposed scheduling policy migrates these tasks to a suitable place where they can be completed while meeting the latency requirements. The CPUs of the idle users on the end-device layer are used for other CPU-overloaded terminals. The simulation results, in terms of energy consumptions, and task scheduling delays, show that the task scheduling performance is better under the application of this method and the obtained scheduling scheme is more reasonable.

How to achieve sustainable distribution in the fast fashion industry? An electric vehicle solution under the "vehicle-battery separation" mode.

Exploring sustainable urban distribution based on electric vehicles is crucial given the rise in global greenhouse gas emissions, especially for fast fashion industries with extremely high distribution frequencies. However, most studies have overlooked the impact of deep discharge on the distribution route scheme, and few studies fit the characteristics of the fast fashion industry. As a result, this study presents a novel electric vehicle routing problem considering deep discharge model (EVRP-DD) for distribution route optimization, which fully considers deep discharge under the emerging mode of vehicle-battery separation. The characteristics of fashion consumers and products were also integrated into the model, such as consumer satisfaction and 3D loading constraints. To solve this complex programming problem, a sophisticated hybrid ant colony optimization (HACO) algorithm was designed by combining the advantages of ACO and A-star algorithms. Using real-life data, the experimental results verify the effectiveness and superiority of the proposed solution. EVRP-DD achieved reduced driving distance, total distribution cost, and deep discharge distance. HACO can enhance the computation speed and reduce the total distribution cost compared with the two conventional algorithms. The proposed solution showed excellent flexibility and could effectively adjust the optimal route scheme according to the ever-changing external environment. Thus, it can be concluded that this solution is a powerful tool for enterprises to achieve sustainable distribution. This study has realized theoretical innovation in sustainable distribution under the new mode of vehicle-battery separation, and its successful application in the fast fashion industry reflects its valuable application value.

An Effective Hybrid Ant Colony Optimization for the Knapsack Problem Using Multi-Directional Search

An Effective Hybrid Ant Colony Optimization for the Knapsack Problem Using Multi-Directional Search

HBMFTEFR: Design of a Hybrid Bioinspired Model for Fault-Tolerant Energy Harvesting Networks via Fuzzy Rule Checks

Designing energy harvesting networks requires modelling of energy distribution under different real-time network conditions. These networks showcase better energy efficiency, but are affected by internal & external faults, which increase energy consumption of affected nodes. Due to this probability of node failure, and network failure increases, which reduces QoS (Quality of Service) for the network deployment. To overcome this issue, various fault tolerance & mitigation models are proposed by researchers, but these models require large training datasets & real-time samples for efficient operation. This increases computational complexity, storage cost & end-to-end processing delay of the network, which reduces its QoS performance under real-time use cases. To mitigate these issues, this text proposes design of a hybrid bioinspired model for fault-tolerant energy harvesting networks via fuzzy rule checks. The proposed model initially uses a Genetic Algorithm (GA) to cluster nodes depending upon their residual energy & distance metrics. Clustered nodes are processed via Particle Swarm Optimization (PSO) that assists in deploying a fault-tolerant & energy-harvesting process. The PSO model is further augmented via use of a hybrid Ant Colony Optimization (ACO) Model with Teacher Learner Based Optimization (TLBO), which assists in value-based fault prediction & mitigation operations. All bioinspired models are trained-once during initial network deployment, and then evaluated subsequently for each communication request. After a pre-set number of communications are done, the model re-evaluates average QoS performance, and incrementally reconfigures selected solutions. Due to this incremental tuning, the model is observed to consume lower energy, and showcases lower complexity when compared with other state-of-the-art models. Upon evaluation it was observed that the proposed model showcases 15.4% lower energy consumption, 8.5% faster communication response, 9.2% better throughput, and 1.5% better packet delivery ratio (PDR), when compared with recently proposed energy harvesting models. The proposed model also showcased better fault prediction & mitigation performance when compared with its counterparts, thereby making it useful for a wide variety of real-time network deployments.

An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication

The vehicular ad hoc network, VANET, is one of the most popular and promising technologies in intelligent transportation today. However, VANET is susceptible to several vulnerabilities that result in an intrusion. This intrusion must be solved before VANET technology can be adopted. In this study, we suggest a unique machine learning technique to improve VANET’s effectiveness. The proposed method incorporates two phases. Phase I detects the DDoS attack using a novel machine learning technique called SVM-HHO, which provides information about the vehicle. Phase II mitigates the impact of a DDoS attack and allocates bandwidth using a reliable resources management technique based on the hybrid whale dragonfly optimization algorithm (H-WDFOA). This proposed model could be an effective technique predicting and utilizing reliable information that provides effective results in smart vehicles. The novel machine learning-based technique was implemented through MATLAB and NS2 platforms. Network quality measurements included congestion, transit, collision, and QoS awareness cost. Based on the constraints, a different cost framework was designed. In addition, data preprocessing of the QoS factor and total routing costs were considered. Rider integrated cuckoo search (RI-CS) is a novel optimization algorithm that combines the concepts of the rider optimization algorithm (ROA) and cuckoo search (CS) to determine the optimal route with the lowest routing cost. The enhanced hybrid ant colony optimization routing protocol (EHACORP) is a networking technology that increases efficiency by utilizing the shortest route. The shortest path of the proposed protocol had the lowest communication overhead and the fewest number of hops between sending and receiving vehicles. The EHACORP involved two stages. To find the distance between cars in phase 1, EHACORP employed a method for calculating distance. Using starting point ant colony optimization, the ants were guided in phase 2 to develop the shortest route with the least number of connections to send information. The relatively short approach increases protocol efficiency in every way. The pairing of DCM and SBACO at H-WDFOA-VANET accelerated packet processing, reduced ant search time, eliminated blind broadcasting, and prevented stagnation issues. The delivery ratio and throughput of the H-WDFOA-packet VANET benefitted from its use of the shortest channel without stagnation, its rapid packet processing, and its rapid convergence speed. In conclusion, the proposed hybrid whale dragonfly optimization approach (H-WDFOA-VANET) was compared with industry standard models, such as rider integrated cuckoo search (RI-CS) and enhanced hybrid ant colony optimization routing protocol (EHACORP). With the proposed method, throughput could be increased. The proposed system had energy consumption values of 2.00000 mJ, latency values of 15.61668 s, and a drop at node 60 of 0.15759. Additionally, a higher throughput was achieved with the new method. With the suggested method, it is possible to meet the energy consumption targets, delay value, and drop value at node 60. The proposed method reduces the drop value at node 80 to 0.15504, delay time to 15.64318 s, and energy consumption to 2.00000 mJ. These outcomes demonstrate the effectiveness of our proposed method. Thus, the proposed system is more efficient than existing systems.

Integrated Inbound Vehicle Routing and Scheduling Under a Fixed Outbound Schedule at a Multi-Door Cross-Dock Terminal

This paper presents a newly formulated model for the integrated inbound vehicle routing and scheduling problem at a multi-door crossdocking terminal and develops a new cooperative co-evolutionary decomposition-based algorithm for its solution. The approach employs two different metaheuristic algorithms to solve the two decomposed subproblems alternatively and iteratively. For solving the routing subproblem, a hybrid of artificial bee colony (ABC) with simulated annealing (SA) is used. For solving the scheduling subproblem, a hybrid ant colony optimization (ACO) with local search is used. The proposed solution method is called SAABC-HACO. For comparison, also developed are three methods called ABCAL-HACO (ABCAL is short for ABC with adaptive limit), HABC-HACO (HABC is an ABC variant hybridized with local search), and ABC-ACO (simplified version of SAABC-HACO). All four methods were tested with 22 newly generated data sets. The test results indicate that: (i) all 4 methods are capable of finding the optimal solution identified by enumeration for a small dataset; (ii) SAABC-HACO emerges to be the best in terms of both solution quality and computational time; and (iii) both fewer number of doors and earlier outbound vehicle departure times can make it more difficult to find near-optimal solutions. Moreover, a number of issues are discussed; managerial insights are highlighted; and topics for further research are mentioned.

A hybrid ant colony optimization with fireworks algorithm to solve capacitated vehicle routing problem

A hybrid ant colony optimization with fireworks algorithm to solve capacitated vehicle routing problem

Metaheuristic-Based Optimization Methods for the Segmentation of Tuberculosis Sputum Smear Images

Tuberculosis (TB) is a worldwide health crisis and second primary infectious disease that causes death. An attempt has been made to detect the presence of bacilli in sputum smears. The smear images recorded under standard image acquisition protocol are segmented by metaheuristic-based methods. Morphological operators are embedded in Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) segmentation to retain concavity of rod-shaped bacilli. Results demonstrate that hybrid ACO segmentation is able to retain the shape of bacilli in TB images. Segmented images are validated with ground truth using overlap, distance and probability-based measures. Significant shape-based features such as area, perimeter, compactness, shape factor and tortuosity are extracted from the segmented images. It is observed that hybrid method preserves more edges, detects the presence of bacilli and facilitates direct segmentation with reduced number of redundant searches to generate edges. Thus this hybrid ACO-morphology segmentation technique aid in the diagnostic relevance of TB images.

Modelling and solving the split-delivery vehicle routing problem, considering loading constraints and spoilage of commodities

In a typical supply chain, an aggregation hub is a strategically located facility that collects, processes and distributes commodities. This requires determining the collection/distribution fleet size, and the shortest routes to/from the served locations. Perishable commodities further require considering the order of collection/delivery and the loading positions inside the trucks. In this paper, the split-delivery vehicle routing problem (SDVRP) is modelled and solved, considering multiple perishable commodities, loading constraints, cross-docking and a heterogeneous fleet of trucks. The objective is to manage a fleet of trucks operating from an aggregation hub in order to minimise the total transportation and products spoilage costs. The problem is first formulated using as a novel mixed integer linear programming (MILP) model. Due to the complexity of the problem, a hybrid ant colony optimisation (ACO) algorithm is proposed to solve the problem. The ACO is embedded with local search (LS) techniques to improve its exploitation capabilities and convergence speed. Experiments are conducted to assess the performance of the ACO/LS algorithm in solving a number of benchmarked SDVRP instances. Results show that the proposed algorithm has an acceptable performance regarding the SDVRP, and can further handle all the aspects considered in the current problem efficiently.