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Vehicle routing problem (VRP) is a classic NP-hard optimization problem. It is generally accepted that an optimized routing scheme can cause huge difference in the cost in all stages of transportation. Consequently, the VRP has evoked interest among the researchers of the field. Usually, a metaheuristic or an evolutionary algorithm is used for the solution of a VRP variant. In the last years, a number of swarm intelligence algorithms have been used for the solution of the problem. Initially, the two most classic swarm intelligence algorithms, the Ant Colony Optimization and the Particle Swarm Optimization, were used for the solution of this kind of problems. However, in the last years, more and more researchers solved the problem using a different swarm intelligence algorithm. In this paper, we focused in the presentation and analysis of the swarm intelligence algorithms that have been used for the solution of the problem. We give the advantages and disadvantages of each method, we focus in those ones that produced the best results in difficult VRPs and we present directions for the future of this kind of algorithms for the solution of a VRP variant. [ABSTRACT FROM AUTHOR]

Vehicular Ad hoc Networks (VANETs) face significant challenges in providing high-quality service. These networks enable vehicles to exchange critical information, such as road obstacles and accidents, and support various communication modes known as Vehicle-to-Everything (V2X). This research paper proposes an intelligent method to improve the quality of service by optimizing path selection between vehicles, aiming to minimize network overhead and enhance routing efficiency. The proposed approach integrates Ant Colony Optimization (ACO) into the Optimized Link State Routing (OLSR) protocol. The effectiveness of this method is validated through implementation and simulation experiments conducted using the Simulation of Urban Mobility (SUMO) and the network simulator (NS3). Simulation results demonstrate that the proposed method outperforms the traditional OLSR algorithm in terms of throughput, average packet delivery rate (PDR), end-to-end delay (E2ED), and average routing overhead. [ABSTRACT FROM AUTHOR]

Succinic acid, also known as dicarboxylic acid, is one of the biochemical products chemically produced from Escherichia coli (E. coli) metabolism. However, by using conventional methods succinic acid cannot be produced sufficiently and it is costly. Hence, there is a lot of ongoing research on E. coli by using in silico methods. Researchers build computational models of E. coli to analyze and modify their metabolic network. This paper proposes a hybrid of ant colony optimization–genetic algorithm–flux balance analysis (ACOGAFBA) in enhancing the succinic acid production of E. coli by identifying genes to be knocked out. Ant colony optimization (ACO) is a swarm intelligent optimization that is inspired based on the natural foraging behavior of ant colony. Local search technique like genetic algorithm (GA) is applied to solve optimization and search problem by approximation. Flux balance analysis (FBA) is used for fitness calculation after gene knockout. FBA identifies a point (fitness) in flux space by using quadratic programming, which is closest to the wild type point. ACOGAFBA produced three sets of gene knockout lists. The dataset i JR904 is used in this paper. The results show that ACOGAFBA can identify the set of knockout genes to improve succinic acid production in E. coli. [ABSTRACT FROM AUTHOR]

The Indonesian government has targeted 2.1 million two-wheeled electric vehicles and 2,200 four-wheeled electric vehicles (EV) by 2025. This is hampered by limited electricity supply and EV charging, which takes long time. Multi device interleaved DC-DC bidirectional converter has been applied and assessed as the most suitable method for battery EV and plug-in hybrid EV because it produces high power >10 kW. For power below 10 kW, it is recommended to use a sinusoidal, Z-Source, and boost amplifier type converter. The smart charging (SC) system will be applied to electric vehicles, which only require a minimum charging power of around 169 W for four lead acid batteries. This paper focuses on an SC system that is capable of charging the battery quickly while still paying attention to the state of health (SoH) of the battery. The SC developed uses a DC-DC boost converter to increase the voltage produced by the switch mode power supply (SMPS). Estimated charging time is less than 30 minutes and still pay attention to the battery SoH. SC will also use pulse width modulation (PWM) as a duty power cycle regulator. This research applies a multi-layer perceptron (MLP) classifier to a neural network (NN). The results of the research show that smart charging can charge up to 600 W with an estimated charging time of around 11 minutes. The charging condition is above 60 % and the power duty cycle setting is 100 %. The power estimation results processed using the ant colony optimization (ACO) based neural network method show a root mean square deviation value of 0.010013430 for charging four lead acid batteries. These results are useful to help solve the problem of capacity requirements and battery charging speed for EVs, with good SoH. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of single ant colony optimization such as many redundant nodes, slow convergence and low efficiency, based on the idea of "selection-crossover" of genetic algorithm, an improved fusion algorithm of ant colony optimization and genetic algorithm is proposed. In this paper, the fusion algorithm includes "optimal strategy" and "genetic region strategy". The optimal strategy is that high-quality parents are selected by roulette in the first M 1 paths of each generation; genetic region strategy is that according to the path information of the parents, the grid map is divided into genetic area and nongenetic area. Genetic area refers to the area where the offspring ants can pass, and nongenetic area refers to the area where the offspring ants can't pass; finally, the offspring ant searches the path in the genetic region to reduce the search range of the offspring ant and improve the convergence speed. Simulation results show that the fusion algorithm has faster searching speed and more stable convergence than the basic ant colony optimization and other improved ant colony optimization. [ABSTRACT FROM AUTHOR]

The basic genetic algorithm usually has the following problems when solving robot path planning problems. First, the chromosome (path) of the first generation is randomly generated, so its fitness is often too high, resulting in too slow convergence speed and even unable to obtain the global optimal solution. Second, in the crossover operator, the selection of crossover points is often random, which cannot ensure that the fitness of cross generated offspring is better than that of their parents. Finally, in the mutation stage, because the traditional mutation strategy is to replace the selected mutation node in the individual chromosome with a random node, the offspring path generated by the mutation operator is likely to be discontinuous, so it cannot reflect the effectiveness of the mutation operator. Based on the mentioned contents, some improved strategies are proposed for the discussed three shortcomings of the basic genetic algorithm. First, a new generation mechanism of the first generation is constructed by simulating the path finding rules of ants in ant colony optimization, which greatly improves the quality of the first generation. Second, during crossover operator, the chromosomes of the two parents are disconnected at the same node, and the optimal chromosome fragments in each segment are combined based on greed, so as to gather all the excellent gene fragments of the parents in an individual as much as possible, improve the quality of the offspring after crossover operator, and speed up the convergence speed. Finally, a new mutation strategy is proposed to eliminate redundant sections and the number of corners, so that the individuals always can be mutated in a good direction. A large number of simulation results show that the improved genetic algorithm is effective in solving the robot path planning problem, and the overall performance is better than the basic genetic algorithm and some other improved genetic algorithms. Finally, by modeling and simulating the real enterprise factory environment based on the ROS platform, the experimental results also verify the effectiveness and feasibility of the improved genetic algorithm. [ABSTRACT FROM AUTHOR]

The CloudAnalyst simulation tool offers various load balancing techniques that can be utilized for efficiently distributing tasks. This research paper explores various load balancing techniques by utilizing the CloudAnalyst simulation tool. The research paper intends to evaluate and contrast the effectiveness of conventional load balancing techniques. While gathering information, the technique entails the simulation of diverse cases with varying parameters, including server capacity, workload, and network latency. The results are compared and contrasted to demonstrate that each technique for load balancing has strengths and weaknesses, and that the most suitable technique should be chosen based on the specific scenario. The Round Robin algorithm is easy to implement but may not be suitable for all scenarios. The Least Connection algorithm is suitable for scenarios where server capacity is not uniform. The IP Hash algorithm is useful for stateful applications, while the Weighted Round Robin algorithm is suitable for scenarios where servers have different capacities. The Least time algorithm is useful for scenarios where processing time is critical. The proposed framework for this research paper is based on nature-inspired load balancing using the CloudAnalyst simulation tool. The framework aims to develop a load balancing algorithm that can adapt to changing workload patterns in real-time. [ABSTRACT FROM AUTHOR]

PID controllers can regulate and stabilize processes in response to changes and disturbances. This paper provides a comprehensive review of PID controller tuning methods for industrial applications, emphasizing intelligent and nature-inspired algorithms. Techniques such as Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are explored. Additionally, nature-inspired algorithms, including evolutionary algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Simulated Annealing (SA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Cuckoo Search (CS), Harmony Search (HS), and Grey Wolf Optimization (GWO), are examined. While conventional PID tuning methods are valuable, the evolving landscape of control engineering has led to the exploration of intelligent and nature-inspired algorithms to further enhance PID controller performance in specific applications. The study conducts a thorough analysis of these tuning methods, evaluating their effectiveness in industrial applications through a comprehensive literature review. The primary aim is to offer empirical evidence on the efficacy of various algorithms in PID tuning. This work presents a comparative analysis of algorithmic performance and their real-world applications, contributing to a comprehensive understanding of the discussed tuning methods. Findings aim to uncover the strengths and weaknesses of diverse PID tuning methods in industrial contexts, guiding practitioners and researchers. This paper is a sincere effort to address the lack of specific quantitative comparisons in existing literature, bridging the gap in empirical evidence and serving as a valuable reference for optimizing intelligent and nature-inspired algorithms-based PID controllers in various industrial applications. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) play a vital role in various fields, but ensuring optimal coverage poses a significant challenge due to the limited energy resources that constrain sensor nodes. To address this issue, this paper presents a novel approach that combines the binary ant colony algorithm (BACA), a variant of ant colony optimization (ACO), with other search optimization algorithms, such as hill climbing (HC) and simulated annealing (SA). The BACA is employed to generate an initial solution by emulating the foraging behavior of ants and the pheromone trails they leave behind in their search for food. However, we acknowledge that the BACA alone may not guarantee the most optimal solution. Subsequently, HC and SA are optimization search algorithms that refine the initial solution obtained by the BACA to find a more enhanced solution. Through extensive simulations and experiments, we demonstrate that our proposed approach results in enhanced coverage and energy-efficient coverage in a two-dimensional (2D) field. Interestingly, our findings reveal that HC consistently outperforms SA, particularly in less complex search spaces, leveraging its robust exploitation approach. Our research contributes valuable insights into optimizing WSN coverage, highlighting the superiority of HC in this context. Finally, we outline promising future research directions that can advance the optimization of WSN coverage. [ABSTRACT FROM AUTHOR]

As unmanned ground vehicles (UGV) continue to be adapted to new applications, an emerging area lacks proper guidance for global route optimization methodology. This area is surveillance. In autonomous surveillance applications, a UGV is equipped with a sensor that receives data within a specific range from the vehicle while it traverses the environment. In this paper, the ant colony optimization (ACO) algorithm was adapted to the UGV surveillance problem to solve for optimal paths within sub-areas. To do so, the problem was modeled as the covering salesman problem (CSP). This is one of the first applications using ACO to solve the CSP. Then, a genetic algorithm (GA) was used to schedule a fleet of UGVs to scan several sub-areas such that the total distance is minimized. Initially, these paths are infeasible because of the sharp turning angles. Thus, they are improved using two methods of path refinement (namely, the corner-cutting and Reeds–Shepp methods) such that the kinematic constraints of the vehicles are met. Several test case scenarios were developed for Goheung, South Korea, to validate the proposed methodology. The promising results presented in this article highlight the effectiveness of the proposed methodology for UGV surveillance applications. [ABSTRACT FROM AUTHOR]

Federated search or distributed information retrieval routes the user's search query to multiple component collections and presents a merged result list in ranked order by comparing the relevance score of each returned result. However, the heterogeneity of the component collections makes it challenging for the central broker to compare these relevance scores while fusing the results into a single ranked list. To address this issue, most existing approaches merge the returned results by converting the document ranks to their ranking scores or downloading the documents and computing their relevance score. However, these approaches are not efficient enough, because the former methods suffer from limited efficacy of result merging due to the negligible number of overlapping documents and the latter are resource intensive. The current paper addresses this problem by proposing a new method that extracts features of both documents and component collections from the available information provided by the collections at query time. Each document and its collection features are exploited together to establish the document relevance score. The ant colony optimization is used for information retrieval to create a merged result list. The experimental results with the TREC 2013 FedWeb dataset demonstrate that the proposed method significantly outperforms the baseline approaches. [ABSTRACT FROM AUTHOR]

This study developed a risk assessment tool for contract capacity optimization problems using the ant colony optimization and auto-regression model. Based on the historical data of demand consumption, the Least Square algorithm, the Recursive Levinson–Durbin algorithm, and the Burg algorithm were used to derive the auto-regression model. Then, ant colony optimization was used to search for the auto-regression model's best p-order parameters. To avoid the risk of setting the contract capacity, this paper designed the risk tolerance parameter β to correct the predicted value of the auto-regression model. Ant colony optimization was also used to search for the optimal contract capacity with risk assessment under the two-stage time-of-use and three-stage time-of-use. This study employed an industrial consumer with high voltage power in Taiwan as the research object, used the AR model to estimate the contract capacity under the risk assessment, and cut back electricity usage to reduce the operation cost. The results can be used as a basis to develop an efficient tool for industrial customers to select contract capacities with risks to obtain the best economic benefits. [ABSTRACT FROM AUTHOR]

Fog computing has grown in popularity in recent years because to its potential to deliver real-time processing, low latency, and reduce network congestion. However, the implementation of Internet of Things (IoT) enabled smart devices in environments using fog computing may lead to resource limitations and higher computational demands. Load balancing and fault tolerance strategies are necessary to tackle these difficulties for optimal resource usage and system availability. In order to accomplish fault tolerance aware load balancing in fog computing, a hybrid meta-heuristic approach that combines the Modified Harris-Hawks Optimization (MHHO) and Ant Colony Optimization (ACO) is proposed through this paper. The MHHO algorithm is utilized for load balancing, whereas the ACO algorithm is used for fault tolerance. By employing the proposed technique, the load on fog nodes is balanced, the makespan time is minimized, energy consumption and execution costs are minimized, and fault tolerance in fog computing environments is ensured. It is evaluated using a simulation framework built on the iFogSim toolkit. In terms of load balancing, fault tolerance, and other factors, the results of the experiments show that the suggested hybrid algorithm performs better than earlier state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The path planning problem is an important issue in maritime search and rescue. This paper models the path planning problem as a dynamic vehicle routing problem. It first designs a dynamic generator that transforms the existing benchmark sets for the static vehicle routing problem into dynamic scenarios. Subsequently, it proposes an effective Dynamic Ant Colony Optimization (DACO) algorithm, whose novelty lies in that it dynamically adjusts the pheromone matrix to efficiently handle customers' changes. Moreover, DACO incorporates simulated annealing to increase population diversity and employs a local search operator that is dedicated to route modification for continuous performance maximization of the route. The experimental results demonstrated that the proposed DACO outperformed existing approaches in generating better routes across various benchmark sets. Specifically, DACO achieved significant improvements in the route cost, serviced customer quantity, and adherence to time window requirements. These results highlight the superiority of DACO in the dynamic vehicle routing problem, providing an effective solution for similar problems. [ABSTRACT FROM AUTHOR]

Vehicle Ad hoc Networks (VANETs) have high mobility and a randomized connection structure, resulting in extremely dynamic behavior. Several challenges, such as frequent connection failures, sustainability, multi-hop data transfer, and data loss, affect the effectiveness of Transmission Control Protocols (TCP) on such wireless ad hoc networks. To avoid the problem, in this paper, mobility-aware zone-based routing in VANET is proposed. To achieve this concept, in this paper hybrid optimization algorithm is presented. The hybrid algorithm is a combination of Ant colony optimization (ACO) and artificial bee colony optimization (ABC). The proposed hybrid algorithm is designed for the routing process which is transmitting the information from one place to another. The optimal routing process is used to avoid traffic and link failure. The fitness function is designed based on Link stability and Residual energy. The validation of the proposed algorithm takes solution encoding, fitness calculation, and updating functions. To perform simulation experiments, NS2 simulator software is used. The performance of the proposed approach is analyzed based on different metrics namely, delivery ratio, delay time, throughput, and overhead. The effectiveness of the proposed method compared with different algorithms. Compared to other existing VANET algorithms, the hybrid algorithm has proven to be very efficient in terms of packet delivery ratio and delay. [ABSTRACT FROM AUTHOR]

Multi-label learning is a machine learning subclass that aims to assign more than one label simultaneously for each instance. Many real-world tasks include high-dimensional data which reduces the performance of machine learning methods. To solve this issue, a filter and multi-label feature selection is proposed in this paper. The main idea of the proposed method is to choose highly relevant and non-redundant features with the lowest information loss. The proposed method first uses a novel graph-based density peaks clustering to group similar features to reach this goal. It then uses the ant colony optimization search process to rank features based on their relevancy to a set of labels and also their redundancy with the other features. A graph first represents the feature space, and then a novel density peaks clustering is used to group similar features. Then, the ants are searched through the graph to select a set of non-similar features by remaining in the clusters with a low probability and jumping among the clusters with a high probability. Moreover, in this paper, to evaluate the solutions found by the ants, a novel criterion based onmutual informationwas used to assign a high pheromone value to highly relevant and non-redundant features. Finally, the final features are chosen based on their pheromone values. The results of experiments on a set of real-world datasets show the superiority of the proposed method over a set of baseline and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation. [ABSTRACT FROM AUTHOR]

In the recent past, vehicular adhoc networks (VANETs) have gained a lot of importance. The routing protocols play a vital role to deliver payloads from one vehicle to another one while they are moving at relative speeds. It is a challenging task to select a routing protocol for VANETs because of the uneven distribution and high mobility of vehicles. In this paper, we have analysed the two standard reactive protocols, adhoc on-demand distance vector (AODV) and ant colony optimization (ACO). The performance comparison of AODV and ACO routing protocols has been presented in this paper. The results show AODV performed better in terms of energy consumption and routing overhead. While considering the throughput, energy loss ratio, and delay ACO has performed. ACO resulted as upperhand. [ABSTRACT FROM AUTHOR]

Ant colony optimization is an intelligent evolutionary algorithm that has been widely used in the field of path planning, and the research on its improved methods is proliferating. In this paper, the improvement methods of single and hybrid ant colony optimization in path planning are reviewed in terms of initial pheromone concentration, heuristic function, state transition rule, pheromone update rule, and parameter optimization, respectively. The future research directions of ant colony optimization have been prospected in terms of theoretical aspects, parameter setting and optimization, integration with other algorithms, and multiobjectives. [ABSTRACT FROM AUTHOR]

In cloud networks, especially those with distributed computing setups and data centers, one of the biggest obstacles is allocating resources. This is the key area, and this must be balanced between optimizing system performance on one side and affordability, stability (reliance) of operation, and energy efficiency. The importance of improving resource allocation methodologies in these complex cloud computing systems is recognized, and therefore this paper comes with an appropriate title-"Enhanced Feature-Driven Multi-Objective Learning for Optimal Cloud Resource Allocation" (OCRA), which integrates together both the latest machine learning techniques as well as traditional concepts from research into cloud computing. OCRA capably analyzes historical files on CPU, memory, disk and network usage. In addition to neatly assimilating large data sets such as that was the compliance rate with past SLAs or workload frequencies over certain time periods and resource allocations; even their patterns of service requests are an important piece of information for many busy people's lives today the adaptive mechanism is one of the defining traits of the model. It can accurately anticipate changes in resource demand and immediately adjust supply, fully able to respond rapidly when fluctuations arise suddenly or unexpectedly. Multi-Objective Random Forests are at the very core of OCRA. Each tree for decision making is specially designed to meet a particular performance objective in mind. Combining these trees into a Random Forest ensemble increases not only the model's predictive accuracy but also its stability. Pareto optimization is wisely used to maintain a balance among performance indicators, without an excessive focus on one effect alone. OCRA is proven empirically through experimental studies where key performance indicators such as Resource Utilization Rate and Quality of Service (QoS) Adherence Rate are taken into account. OCRA is both energy-efficient, an important attribute in today's environmentally conscious world, and does not sacrifice performance. As far as speed, flexibility and overall efficiency are concerned, OCRA has always been superior to the other cloud resources allocation programs of its own day. While it's still not quite ready for users who don't have a firm background in computer science or programming skills (ocra is plotted on 0-x), with sufficient memory and dominant minutes turn into mechanical equipment without configuration services. [ABSTRACT FROM AUTHOR]

Wireless mesh network (WMNs) can replace the physical existence of wired network; nevertheless, to solve the problem of traffic and congestion, they must carefully arrange radio resource assignment. In this paper, we focus on the major issues while transferring the data over the network and optimization of resources. WMN mainly suffers from congestion problem when numbers of channel transfer data at a same time which indirectly leads to overlapping channels and sometime data cannot be mitigated to destination. To overcome this problem and provide optimal solution we proposed hybrid channel assignment technique which offers four phase solution technique, in this technique firstly by mitigating the traffic than assigning the optimal path based on greedy and BFS. The purpose of this congestion control hybrid ant based greedy-BFS-load balancing (CCHAGBL) technique is to diminish the number of used slots, which is directly related to the overall resource assignment. We also provide optimal solution using Ant colony optimization technique with hybrid algorithm to achieve the network efficiency and resource utilization with randomly generated traffic. With the help of hybrid ant based greedy-BFS-load balancing (GBL) channel assignment algorithm throughput and packet delivery ratio is upgraded and delay is reduced to minimum. [ABSTRACT FROM AUTHOR]

The purpose of geographic location selection is to make the best use of space. Geographic location selection contains a large amount of spatiotemporal data and constraints, resulting in too many solutions. Therefore, this paper adopts the ant colony algorithm in the meta-heuristic search method combined with the incomplete quadtree to improve the searchability of the space. This paper proposes an improved ant colony algorithm in nonuniform space to solve the P-center facility location problem. The geographic space is divided by the incomplete quadtree, and the ant colony path is constructed on the level of the quadtree division. Ant colonies can leave pheromones on multiple search paths, and optimized quadtree encoding in nonuniform space stores pheromone matrices and distance matrices. The algorithm proposed in this paper improves the pheromone diffusion algorithm and the optimization objective at the same time to update the pheromone in the nonuniform space and obtain the ideal solution. The results show that the algorithm has excellent performance in solving the location problem with good convergence accuracy and calculation time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Reverse logistics (RL) network can be adequately planned and implemented to gain additional benefits such as maximizing customer satisfaction and a positive image of the business organization, even though most distribution networks are not equipped with reverse channels to deal with the return of merchandise. Therefore, the main objective of this paper is to develop a new mixed-integer nonlinear programming (MINLP) mathematical model with a single-objective, single-product, multi-stage closed-loop supply chain network design (CLSC ND), considering the fixed transportation charge in the distribution network that has been neglected in the recently published papers in the field of CLSC ND. Since such network design challenges belong to the class of NP-hard problems, an algorithm based on ant colony optimization (ACO) is proposed to design a multi-stage RL network with fixed transportation cost and variable cost for the routes. Four network characteristics of different sizes were designed, and 30 instances were randomly generated for each network characteristic to evaluate the effectiveness of the proposed algorithm. The computational analysis of the results shows the high-quality effectiveness of the proposed ACO algorithm compared with the exact results. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes. [ABSTRACT FROM AUTHOR]

Car-like mobile robots (CLMRs) are extensively utilized in various intricate scenarios owing to their exceptional maneuverability, stability, and adaptability, in which path planning is an important technical basis for their autonomous navigation. However, path planning methods are prone to inefficiently generate unsmooth paths in narrow and large-size scenes, especially considering the chassis model complexity of CLMRs with suspension. To this end, instead of traditional path planning based on an integer order model, this paper proposes fractional-order enhanced path planning using an improved Ant Colony Optimization (ACO) for CLMRs with suspension, which can obtain smooth and efficient paths in narrow and large-size scenes. On one hand, to improve the accuracy of the kinematic model construction of CLMRs with suspension, an accurate fractional-order-based kinematic modelling method is proposed, which considers the dynamic adjustment of the angle constraints. On the other hand, an improved ACO-based path planning method using fractional-order models is introduced by adopting a global multifactorial heuristic function with dynamic angle constraints, adaptive pheromone adjustment, and fractional-order state-transfer models, which avoids easily falling into a local optimum and unsmooth problem in a narrow space while increasing the search speed and success rate in large-scale scenes. Finally, the proposed method's effectiveness is validated in both large-scale and narrow scenes, confirming its capability to handle various challenging scenarios. [ABSTRACT FROM AUTHOR]

This paper introduces an agent-based model grounded in the ACO algorithm to investigate the impact of partitioning ant colonies on algorithmic performance. The exploration focuses on understanding the roles of group size and number within a multi-objective optimization context. The model consists of a colony of memory-enhanced ants (ME-ANTS) which, starting from a given position, must collaboratively discover the optimal path to the exit point within a grid network. The colony can be divided into groups of different sizes and its objectives are maximizing the number of ants that exit the grid while minimizing path costs. Three distinct analyses were conducted: an overall analysis assessing colony performance across different-sized groups, a group analysis examining the performance of each partitioned group, and a pheromone distribution analysis discerning correlations between temporal pheromone distribution and ant navigation. From the results, a dynamic correlation emerged between the degree of colony partitioning and solution quality within the ACO algorithm framework. [ABSTRACT FROM AUTHOR]

The fixed fleet heterogeneous open vehicle routing problem (HFFOVRP) is one of the most practical versions of the vehicle routing problem (VRP) defined because the use of rental vehicles reduces the cost of purchasing and routing for shipping companies nowadays. Also, applying a heterogeneous fleet is recommended due to the physical limitations of the streets and efforts to reduce the running costs of these companies. In this paper, a mixed-integer linear programming is proposed for HFFOVRP. Because this problem, like VRP, is related to NP-hard issues, it is not possible to use exact methods to solve real-world problems. Therefore, in this paper, a hybrid algorithm based on the ant colony algorithm called MACO is presented. This algorithm uses only global updating pheromones for a more efficient search of feasible space and considers a minimum value for pheromones on the edges. Also, pheromones of some best solutions obtained so far are updated, based on the quality of the solutions at each iteration, and three local search algorithms are used for the intensification mechanism. This method was tested on several standard instances, and the results were compared with other algorithms. The computational results show that the proposed algorithm performs better than these methods in cost and CPU time. Besides, not only has the algorithm been able to improve the quality of the best-known solutions in nine cases but also the high-quality solutions are obtained for other instances. [ABSTRACT FROM AUTHOR]

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This paper focuses on addressing the "last 100 metres" home delivery in rural areas, using a cooperated delivery method of drones and truck. Considering the constraints of drone load, drone energy consumption and customer time window, a mixed integer linear programming model is established to minimize the delivery cost. Owing to the computational complexity of this problem, a double ant colony optimization with neighbourhood search is proposed. First, the raw data are sorted and encoded. Second, the ant colony optimization with search operators is used to solve drone routes and truck route. Finally, the local search algorithm with search operators is used to solve the connection point between the drones and truck to obtain the cooperated delivery routes. Extensive experiments are conducted on the instances randomly generated in the Solomon dataset, and results demonstrate the proposed algorithm effectively solves problems within reasonable runtimes. Sensitivity analysis is conducted on factors that may affect the delivery cost of the solution and provide insights about drones participating in the "last hundred metres" home delivery service. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in the probabilistic traveling salesman problem (PTSP) as a variant of the well-known traveling salesman problem (TSP), where city distances follow a probability distribution. The goal is to find a tour that visits all cities with the minimum expected length. To solve this combinatorial problem, we consider four ant colony optimization (ACO) variants: Ant System (AS), Max-Min Ant System (MMAS), a Novel Max-Min System (NMMAS) and Ant Colony System (ACS). The performance of the algorithms is evaluated, considering different examples from the literature, based on the quality of the obtained solution and the computation time. The experiment results show that MMAS is more efficient compared to the other algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an approach to solve the variant of the well-known Travelling Salesman Problem (TSP) by using a gamesourcing approach. In contemporary literature is TSP solved by wide spectra of modern as well as classical computational methods. We would like to point out the possibility to solve such problems by computer game plying that is called a gamesourcing. Gamesourcing can be understood as a version of game-driven crowdsourcing. The main part and contribution of this paper is a demonstration of gamesourcing use in the game called Labyrinth that reflects TSP structure. The game has a form of a maze-labyrinth that enables players to move through it like the Ant Colony Optimization. The playing of the Labyrinth, thus, by playing, solves the problem. The performance of the "human ant-like system" is then evaluated and compared against some well-known versions of ACO. As we believe, our experiments suggest that this approach can serve as an alternative way that employs gamesourcing to assist a combinatorial optimizer in achieving better results on a well-known NP-hard optimization problem. [ABSTRACT FROM AUTHOR]

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm is essential for analyzing path planning. However, the current ant colony optimization algorithm applied to the path planning of mobile robots still has some limitations, including early blind search, slow convergence speed, and more turns. To overcome these problems, an improved ant colony optimization algorithm is proposed in this paper. In the improved algorithm, we introduce the idea of triangle inequality and a pseudo-random state transfer strategy to enhance the guidance of target points and improve the search efficiency and quality of the algorithm. In addition, we propose a pheromone update strategy based on the partition method with upper and lower limits on the pheromone concentration. This can not only improve the global search capability and convergence speed of the algorithm but also avoid the premature and stagnation phenomenon of the algorithm during the search. To prevent the ants from getting into a deadlock state, we introduce a backtracking mechanism to enable the ants to explore the solution space better. Finally, to verify the effectiveness of the proposed algorithm, the algorithm is compared with 11 existing methods for solving the robot path planning problem, including several ACO variants and two commonly used algorithms (A* algorithm and Dijkstra algorithm), and the experimental results show that the improved ACO algorithm can plan paths with faster convergence, shorter path lengths, and higher smoothness. Specifically, the algorithm produces the shortest path length with a standard deviation of zero while ensuring the most rapid convergence and the highest smoothness in the case of the shortest path in four different grid environments. These experimental results demonstrate the effectiveness of the proposed algorithm in path planning. [ABSTRACT FROM AUTHOR]

The Capacitated Vehicle Routing Problem (CVRP) is a classical combinatorial optimization problem, which objective is to minimize the vehicle travel distance. In this paper a hybrid ant colony optimization based on Fireworks Algorithm (FWA) is proposed to solve the CVRP. The main work of hybrid algorithm is reflected in three aspects: firstly, FWA is introduced on the basis of Ant Colony Optimization (ACO) algorithm to increase the diversity of algorithms and avoid falling into local optimum too early in the search process; secondly, the elite ant strategy is added to increase the attractiveness of the local optimal path to the ants; thirdly, the Max-Min Ant System (MMAS) is added to limit the pheromone value within a range to prevent the pheromone from increasing too fast on the path. In this paper, the proposed hybrid algorithm is tested in 71 instances and compared with other intelligent algorithms. Experimental results show that the proposed hybrid algorithm finds new best solutions and is competitive with other algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The integration of discrete simulations, artificial intelligence methods, and the theory of probability in order to obtain a high flexibility of the production system is crucial. In this paper, the concept of a smart factory operation is proposed along with the idea of data exchange architecture, simulation creation, performance optimization, and predictive analysis of the production process conditions. A Digital Twin for a hybrid flow shop from the automotive industry is presented as a case study. In the paper, the Ant Colony Optimization (ACO) algorithm is developed for multi-criteria scheduling problems in order to obtain a production plan without delays and maximum resource utilization. The ACO is compared to the immune algorithm and genetic algorithm. The best schedules are achieved with low computation time for the Digital Twin. By predicting the reliability parameters of the limited resources of the Digital Twin, stable deadlines for the implementation of production tasks are achieved. Mean Time To Failure and Mean Time of Repair are predicted for a real case study of an electric steering gear production line. The presented integration and data exchange between the elements of the smart factory: a Digital Twin, a computing module including an optimization, prediction, and simulation methods fills the gap between theory and practice for Industry 4.0. The paper presents measurable benefits of integration of discrete simulation tools, historical data analysis, and optimization methods. [ABSTRACT FROM AUTHOR]

In this paper, a two-phase ant colony optimization (ACO) based approach has been presented to solve a single depot multiple travelling salesmen problem (mTSP) in Type-2 Gaussian fuzzy environment. In the single depot mTSP, a set of nodes and a set of salesmen are present, and each of the cities must be visited exactly once by the salesmen such that all of them start and finish at a depot. In this paper, a two-phase algorithm has been devised with ACO algorithm and some features of genetic algorithm (GA) to solve the projected problem. The devised algorithm is working appropriately with single depot mTSP. Here, in the first phase, the ACO algorithm is used for creating complete paths, and after that in the second phase, the GA features are used for optimizing the paths of multiple travellers. Moreover, the travelling cost is considered as Type-2 Gaussian fuzzy in nature and is reduced to its approximate crisp value using the reduction method of critical values. Some benchmark instances from TSPLIB have been used for performance analysis of the proposed algorithm. Computated results show that the devised algorithm is a competitive one for solving mTSP. Computational results with different datasets have been presented and some sensitivity analysis has also been done for fuzzy instances. [ABSTRACT FROM AUTHOR]