Showing papers on "Ant colony published in 2021"
TL;DR: An excellent multi-threshold image segmentation method using a random spare strategy and chaotic intensification strategy and improved selection mechanism to effectively augment the ability to step out of LO and to refine the convergence accuracy is proposed.
Abstract: Although the continuous version of ant colony optimizer (ACOR) has been successfully applied to various problems, there is room to boost its stability and improve convergence speed and precision. In addition, it is prone to stagnation, which means it cannot step out of the local optimum (LO). To effectively mitigate these concerns, an improved method using a random spare strategy and chaotic intensification strategy is proposed. Also, its selection mechanism is enhanced in our research. Among the new components, the convergence speed is mainly boosted by using a random spare approach. To effectively augment the ability to step out of LO and to refine the convergence accuracy, the chaotic intensification strategy and improved selection mechanism are applied to ACOR. To better verify the effectiveness of the proposed method, a series of comparative experiments are conducted by using 30 benchmark functions. According to all experimental results, it is evident that the convergence rapidity and accuracy of the proposed method is better than other peers. In addition, it is observed that the capability of enhanced RCACO is more reliable than other techniques in stepping out of LO. Furthermore, an excellent multi-threshold image segmentation method is proposed in this paper. On this basis, image segmentation experiments at low threshold levels and high threshold levels are also respectively carried out. The experimental results also adequately disclose that the segmentation results of RCACO for both multi-threshold image segmentation at a low threshold level and high threshold level, are even more satisfactory compared to other studied algorithms. An online homepage supports this research for access to sharable codes, any question and info about this research at https://aliasgharheidari.com .
222 citations
TL;DR: A new mechanism for route selection combining Ad-hoc On-Demand Distance Vector (AODV) protocol with Ant Colony Optimization (ACO) protocol to improve Quality of Service (QoS) in MANET is proposed.
65 citations
TL;DR: In this article, an ant colony system (ACS)-based algorithm was proposed to obtain good enough paths for UAVs and fully cover all regions efficiently, inspired by the foraging behavior of ants that they can obtain the shortest path between their nest and food.
Abstract: Unmanned aerial vehicle (UAV) has been extensively studied and widely adopted in practical systems owing to its effectiveness and flexibility. Although heterogeneous UAVs have an enormous advantage in improving performance and conserving energy with respect to homogeneous ones, they give rise to a complex path planning problem. Especially in large-scale cooperative search systems with multiple separated regions, coverage path planning which seeks optimal paths for UAVs to completely visit and search all of regions of interest, has a NP-hard computation complexity and is difficult to settle. In this work, we focus on the coverage path planning problem of heterogeneous UAVs, and present an ant colony system (ACS)-based algorithm to obtain good enough paths for UAVs and fully cover all regions efficiently. First, models of UAVs and regions are built, and a linear programming-based formulation is presented to exactly provide the best point-to-point flight path for each UAV. Then, inspired by the foraging behaviour of ants that they can obtain the shortest path between their nest and food, an ACS-based heuristic is presented to seek approximately optimal solutions and minimize the time consumption of tasks in the cooperative search system. Experiments on randomly generated regions have been organized to evaluate the performance of the new heuristic in terms of execution time, task completion time and deviation ratio.
63 citations
TL;DR: A new practical model for CRP that takes both fairness and satisfaction into account simultaneously is proposed, and experimental results show that MOACS generally outperforms the greedy algorithm and some other popular multi-objective optimization algorithms, especially on large-scale instances.
Abstract: The airline crew rostering problem (CRP) is significant for balancing the workload of crew and for improving the satisfaction rate of crew’s preferences, which is related to the fairness and satisfaction of crew. However, most existing work considers only one objective on fairness or satisfaction. In this study, we propose a new practical model for CRP that takes both fairness and satisfaction into account simultaneously. To solve the multi-objective CRP efficiently, we develop an ant colony system (ACS) algorithm based on the multiple populations for multiple objectives (MPMO) framework, termed multi-objective ACS (MOACS). The main contributions of MOACS lie in three aspects. Firstly, two ant colonies are utilized to optimize fairness and satisfaction objectives, respectively. Secondly, a new hybrid complementary heuristic strategy with three kinds of heuristic information schemes is proposed to avoid ant colonies focusing only on their own objectives. Ant colonies randomly choose one of the three schemes to help explore the Pareto front (PF) sufficiently. Thirdly, a local search strategy with two types of local search respectively for fairness and satisfaction is designed to further approach the global PF. The MOACS is applied to seven real-world monthly CRPs with different sizes from a major North-American airline. Experimental results show that MOACS generally outperforms the greedy algorithm and some other popular multi-objective optimization algorithms, especially on large-scale instances.
57 citations
TL;DR: In this article, the improved ant colony optimization-artificial potential field (ACO-APF) algorithm is proposed to improve path planning of USVs in dynamic environments, which is based on a grid map for both local and global path planning.
Abstract: Path planning is important to the efficiency and navigation safety of USV autonomous operation offshore. To improve path planning, this study proposes the improved ant colony optimization-artificial potential field (ACO-APF) algorithm, which is based on a grid map for both local and global path planning of USVs in dynamic environments. The improved ant colony optimization (ACO) mechanism is utilized to search for a globally optimal path from the starting point to the endpoint for a USV in a grid environment, and the improved artificial potential field (APF) algorithm is subsequently employed to avoid unknown obstacles during USV navigation. The primary contributions of this article are as follows: (1) this article proposes a new heuristic function, pheromone update rule, and dynamic pheromone volatilization factor to improve convergence and mitigate finding local optima with the traditional ant colony algorithm; (2) we propose an equipotential line outer tangent circle and redefine potential functions to eliminate goals unreachable by nearby obstacles (GNRONs) and local minimum problems, respectively; (3) to adapt the USV to a complex environment, this article proposes a dynamic early-warning step-size adjustment strategy in which the moving distance and safe obstacle avoidance range in each step are adjusted based on the complexity of the surrounding environment; (4) the improved ant colony optimization algorithm and artificial potential field algorithm are effectively combined to form the algorithm proposed in this article, which is verified as an effective solution for USV local and global path planning using a series of simulations. Finally, in contrast to most papers, we successfully perform field experiments to verify the feasibility and effectiveness of the proposed algorithm.
57 citations
TL;DR: From the performance evaluation carried out in this research work, it is proved that the proposed MCER-ACO approach is providing optimal energy efficient routing while comparing with few other existing methods.
Abstract: A mobile ad-hoc network (MANET) is a group of advanced mobile devices which are capable of self-organization. Due to the diverse nature of mobile devices and wireless connectivity, MANET faces several issues like topology management, energy management due to battery power limits, data communication issues etc. The utilization rate of battery powered energy and QoS properties are significant in MANET. In order to address these issues, we propose a new ant colony inspired technique for energy efficient routing in MANET. The proposed technique is a multi-objective constraints applied energy efficient routing technique based on ant colony optimization in mobile adhoc networks (MCER-ACO). The proposed MCER-ACO technique selects the next hop node centered on the constraints, residual energy of mobile node, no of packets in path and dynamic movement of topology. By applying ant colony technique on objectives and constraints, probability of choosing next hop node as forwarding node is determined. From the performance evaluation carried out in this research work, it is proved that the proposed MCER-ACO approach is providing optimal energy efficient routing while comparing with few other existing methods.
55 citations
TL;DR: The optimized black hole algorithm is employed to select an optimal CH from sensor nodes and it outperformed the other four comparative methods in terms of packet delivery rate and the number of transmitted packets to the CH and to the sink.
46 citations
TL;DR: In this paper, a fuzzy gain-based dynamic ant colony optimization (FGDACO) for dynamic path planning is proposed to effectively plan collision-free and smooth paths, with feasible path length and the minimum time.
Abstract: Path planning can be perceived as a combination of searching and executing the optimal path between the start and destination locations. Deliberative planning capabilities are essential for the motion of autonomous unmanned vehicles in real-world scenarios. There is a challenge in handling the uncertainty concerning the obstacles in a dynamic scenario, thus requiring an intelligent, robust algorithm, with the minimum computational overhead. In this work, a fuzzy gain-based dynamic ant colony optimization (FGDACO) for dynamic path planning is proposed to effectively plan collision-free and smooth paths, with feasible path length and the minimum time. The ant colony system’s pheromone update mechanism was enhanced with a sigmoid gain function for effective exploitation during path planning. Collision avoidance was achieved through the proposed fuzzy logic control. The results were validated using occupancy grids of variable size, and the results were compared against existing methods concerning performance metrics, namely, time and length. The consistency of the algorithm was also analyzed, and the results were statistically verified.
23 citations
TL;DR: The GSACO algorithm proposed in this paper can effectively accelerate the convergence speed, obtain the global optimal solution faster, and better solve the path planning problem under various constraints.
Abstract: Path planning problems have attracted the interest of more and more researchers due to its widespread existence in recent years. For example, express delivery and food delivery, courier often need to get to their destinations as quickly as possible under the constraints of time, economy etc. Therefore, the path planning issue under various constraints becomes more challenging. In order to solve these problems better, a new ant colony optimization algorithm based on adaptive greedy strategy (GSACO) is proposed this paper. In the process of continuous iteration, the control parameters of the algorithm are constantly adjusted and changed, which can expand the diversity of the population. In the process of ant colony search, the preference degree of ant colony is continuously changed by the greedy strategy, then the ant colony continuously explores the places with high pheromone concentration, and finally the convergence speed of the algorithm is accelerated. The experimental results show that GSACO algorithm has better performance than traditional heuristic algorithm and other evolutionary-based methods. The GSACO algorithm proposed in this paper can effectively accelerate the convergence speed, obtain the global optimal solution faster, and better solve the path planning problem under various constraints.
22 citations
TL;DR: The Ant Colony System-Variable Neighborhood Decent provided the best results among the two implemented versions of the Ant Colony Optimization family and was able to find a new best known solution for two instances.
20 citations
15 Mar 2021
TL;DR: In this paper, an energy-efficient multipath routing algorithm based on the foraging nature of ants is proposed including many meta-heuristic impact factors to provide good robust paths from source to destination to overcome the challenges faced by resource constrained sensors.
Abstract: This paper describes the novel wireless routing protocol made for mobile ad hoc networks or wireless sensor networks using the bio-inspired technique. Bio-inspired algorithms include the routing capabilities taken from the social behavior of ant colonies, bird flocking, honey bee dancing, etc and promises to be capable of catering to the challenges posed by wireless sensors. Some of the challenges of wireless sensor networks are limited bandwidth, limited battery life, low memory, etc. An energy-efficient multipath routing algorithm based on the foraging nature of ants is proposed including many meta-heuristic impact factors to provide good robust paths from source to destination to overcome the challenges faced by resource-constrained sensors. Analysis of individual impact factor is represented which justifies their importance in routing performance. The multi-path routing feature is claimed by showing energy analysis as well as statistical analysis in-depth to the readers. The proposed routing algorithm is analyzed by considering various performance metrics such as throughput, delay, packet loss, network lifetime, etc. Finally, the comparison is done against AODV routing protocol by considering performance metrics where the proposed routing algorithm shows a 49% improvement in network lifetime.
TL;DR: Simulation results show that the proposed framework, in which a subset of devices are selected to receive data from a UAV and then forward the required data to other devices, is more energy-efficient compared to a baseline approach.
Abstract: In this letter, we address the problem of minimizing the energy consumption of disseminating a library of files to a set of Internet-of-Things (IoT) devices using an unmanned aerial vehicle (UAV). A framework is provided, in which a subset of devices are selected to receive data from a UAV and then forward the required data to other devices. Furthermore, optimal energy-efficient path selection is considered in order to realize efficient data dissemination. Specifically, an optimization problem is formulated to minimize the energy expenditure of the IoT devices and UAV while the latter tours to disseminate the required files to the former. An ant colony optimization (ACO) algorithm is developed to solve the optimization problem. Simulation results show that the proposed framework is more energy-efficient compared to a baseline approach, where the UAV hovers above each device to deliver the data. Results also illustrate that the proposed ACO algorithm provides performance close to the optimal solution, which is obtained through exhaustive search.
TL;DR: In this paper, an adaptive improved ant colony algorithm based on population information entropy (AIACSE) is proposed to improve the optimization ability of the algorithm, where the diversity of the population in the iterative process is described by the information entropy.
Abstract: In this paper, an adaptive improved ant colony algorithm based on population information entropy(AIACSE) is proposed to improve the optimization ability of the algorithm The diversity of the population in the iterative process is described by the information entropy The non-uniform distribution initial pheromone is constructed to reduce the blindness of the search at the starting phase The pheromone diffusion model is used to enhance the exploration and collaboration capacity between ants The adaptive parameter adjusting strategy and the novel pheromone updating mechanism based on the evolutionary characteristics of the population are designed to achieve a better balance between exploration of the search space and exploitation of the knowledge during the optimization progress The performance of AIACSE is evaluated on the path planning of mobile robots Friedman’s test is further conducted to check the significant difference in performance between AIACSE and the other selected algorithms The experimental results and statistical tests demonstrate that the presented approach significantly improves the performance of the ant colony system (ACS) and outperforms the other algorithms used in the experiments
TL;DR: A novel service system, including the battery exchange stations and maintenance checkpoints, to provide long-distance delivery services, and a drone path programming model, where a special penalty value is proposed as the objective function to simultaneously minimize the path length and number of landing depots for the delivery service.
Abstract: Drones, with the potential to significantly increase the efficiency of the delivery, have received much attention in recent years. Still, there are some bottleneck problems in the application of the long-distance drone delivery, such as the limited flight range and flight safety. Therefore, the article proposes a novel service system, including the battery exchange stations and maintenance checkpoints, to provide long-distance delivery services. Then, with respect to the service system, we construct a drone path programming model, where a special penalty value is proposed as the objective function to simultaneously minimize the path length and number of landing depots for the delivery service. Thereafter, to efficiently find the optimal flight path among huge solution space, we improve the ant colony optimization with the A* algorithm embedded to avoid the nondirectional searching of ants. Finally, we use a case of Shanghai city to study the feasibility and effectiveness of our approaches, which includes the comparison of our algorithm and the other three heuristics on ten random delivery cases, the verification of the effectiveness of our algorithm on the long-distance delivery service, and a sensitive analysis of the effect of the depot number on the optimal solution.
TL;DR: The classification of cancers is one of the most vital functions of Microarray data analysis and the classification of the gene expression profile is treated as a NP-Hard problem since it is a very demanding job.
Abstract: The classification of cancers is one of the most vital functions of Microarray data analysis. The classification of the gene expression profile is treated as a NP-Hard problem since it is a very demanding job. Compared to the individual search utilized by conventional algorithms, the population search utilized by Evolutionary Algorithm (EA) is visibly more beneficial. In feasible search areas, EA algorithms also are more likely to detect various optimums instantly. Evolutionary techniques which are inspired by nature perform exceptionally well and are extensively used for Microarray data analysis. Ant Colony Optimization (ACO) is a distinct intelligent optimization algorithm based on iterative optimization which uses ideas like evolution and group. ACO algorithm was developed by studying how ants identify paths while food foraging. Ant Lion Optimization (ALO) algorithm is proposed and employed as muted selection process and the ant lions to hunt process is simulated. A hybrid ant lion mutated ant colony optimizer technique is proposed in this work.
TL;DR: An ant colony evacuation planner (ACEP) with a novel solution construction strategy and an incremental flow assignment (IFA) method is introduced, in which fractions of evacuees are assigned step by step to imitate the group-based evacuation process in the real world so that the efficiency of ACEP can be further improved.
Abstract: Evacuation path optimization (EPO) is a crucial problem in crowd and disaster management. With the consideration of dynamic evacuee velocity, the EPO problem becomes nondeterministic polynomial-time hard (NP-Hard). Furthermore, since not only one single evacuation path but multiple mutually restricted paths should be found, the crowd evacuation problem becomes even challenging in both solution spatial encoding and optimal solution searching. To address the above challenges, this article puts forward an ant colony evacuation planner (ACEP) with a novel solution construction strategy and an incremental flow assignment (IFA) method. First, different from the traditional ant algorithms, where each ant builds a complete solution independently, ACEP uses the entire colony of ants to simulate the behavior of the crowd during evacuation. In this way, the colony of ants works cooperatively to find a set of evacuation paths simultaneously and thus multiple evacuation paths can be found effectively. Second, in order to reduce the execution time of ACEP, an IFA method is introduced, in which fractions of evacuees are assigned step by step, to imitate the group-based evacuation process in the real world so that the efficiency of ACEP can be further improved. Numerical experiments are conducted on a set of networks with different sizes. The experimental results demonstrate that ACEP is promising.
TL;DR: In this paper, the whole contribution is placed towards the routing which involves in the WSN and the ACO-PSO hybrid approach has been introduced for the optimization process which will enhances the lifetime.
01 Jan 2021
TL;DR: In this article, the authors present a critical approach to the different available methods for sampling ants, their limitations, and their complementarity, focusing on sampling methods for ant inventories; entire ant colony sampling for a range of purposes, such as behavioral, cytogenetic, or population studies; and ant community studies.
Abstract: The current demand for studies on ants in general and their communities in particular has exponentially increased during the last decades. Much has already been said about ant sampling techniques, but we attempt to present a critical approach to the different available methods for sampling ants, their limitations, and their complementarity. We focus on sampling methods for ant inventories; entire ant colony sampling for a range of purposes, such as behavioral, cytogenetic, or population studies; and ant community studies. Methods presented here are valid for both tropical and temperate regions taking into account that ants are essentially thermophilous and found in lower richness and abundance in cold regions or during cold seasons. Sampling depends on the stratum of interest. Thus, different methods, or a combination of them, may be selected for soil-, litter-, or vegetation-associated ants. Regardless of the method used, some considerations must always be taken into account. The first of these is compatibility of data between the sampling methods. Be they pitfall traps, Winkler sack samples, or whatever, such sampling units must be treated independently from each another; otherwise the inferential statistics used may not be valid or used with caution as violation of independence may occur in two non-mutually exclusive general forms: pseudoreplication and spatial (or temporal) autocorrelation, for example, which are common mistakes in ant studies. We provide recommendations for statistical approaches to the data and different suggestions of analyses that can be used for the different kinds of data taken with ants.
12 Mar 2021
TL;DR: In this article, an improved path planning algorithm based on A-star algorithm fused with ant colony algorithm is proposed, which improves the convergence speed of the algorithm by improving the ant colony pheromone enhancement coefficient.
Abstract: Aiming at the problems of ant colony algorithm in robot global path planning, such as slow convergence speed, large number of iterations, and long search time, an improved path planning algorithm based on A-star algorithm fused with ant colony algorithm is proposed. Firstly, A-star the algorithm optimizes the initial pheromone of the ant colony, and improves the convergence speed of the algorithm. Secondly, by improving the ant colony pheromone enhancement coefficient to avoid the local optimization problem caused by excessive pheromone accumulation in the later period of the ant colony algorithm. Finally through the established grid map, set The environmental penalty coefficient solves the problem that the ant colony algorithm is easy to fall into the local optimum in a complex environment. The experimental results show that the improved A-star ant colony algorithm converges faster and has better global optimization capabilities. Even in a complex environment, it is not easy to fall into the local optimum, and it can effectively solve the problem of robot global path planning.
TL;DR: In this paper, the effects of the Tuned Mass Damper (TMD) device on the response of a 40-story building including three types of soils and experiencing 16 far-field earthquakes were investigated.
Abstract: This paper probes the effects of the Tuned Mass Damper (TMD) device on the response of a 40-story building including three types of soils and experiencing 16 far-field earthquakes. The Ant Colony O...
TL;DR: A new hybrid approach based on GIS and ant colony is developed to provide optimal shared-routes through integrating three main procedures sequentially and finding the optimum shared-route is found by the ant colony optimization (ACO) algorithm.
Abstract: The increasing use of private cars in large cities is accompanied by adverse ramifications such as severe shortage of parking spaces, traffic congestion, air pollution, a high level of fuel consumption, and travel cost. Ridesharing is one of the emerging solutions that facilitate the simultaneous match of drivers and passengers with similar travel schedules. In this paper, ridesharing equals carsharing which involves a cooperative trip of at least two passengers who share an automobile and must match their itineraries. The main objective of this paper is to develop a ridesharing system based on the geosocial network to be employed in Tehran, capital of Iran. In this regard, a new hybrid approach based on GIS and ant colony is developed to provide optimal shared-routes through integrating three main procedures sequentially. First, the spatio-temporal clustering of passengers is carried out using the K-means algorithm, second spatio-temporal matching of passengers ‘clusters, and drivers’ has been carried out by combining Voronoi continuous range query (VCRQ), a region connected calculus (RCC5) and Allen’s temporal interval algebra. Third, the optimum shared-route is found by the ant colony optimization (ACO) algorithm. The proposed hybrid model integrates metric and topological GIS-based methods with a metaheuristic algorithm. It is implemented via a bot “@Hamsafar” within the platform of a robot Telegram messenger. The proposed ridesharing application is applied with 220 passengers and 70 drivers with 61 shared trips in District # 6 of Tehran, Iran. The system are evaluated based on the statistical results, usability questionnaire, time performance, and comparison to some other metaheuristic approaches which in turn demonstrate the efficiency of the proposed algorithm.
TL;DR: It is found that preventing access to an important food source causespolydomous wood ant colony networks to fragment into smaller components and begin foraging on previously unused food sources, demonstrating that polydomous colonies can adjust to environmental changes by altering their social network.
Abstract: Animal social structure is shaped by environmental conditions, such as food availability. This is important as conditions are likely to change in the future and changes to social structure can have cascading ecological effects. Wood ants are a useful taxon for the study of the relationship between social structure and environmental conditions, as some populations form large nest networks and they are ecologically dominant in many northern hemisphere woodlands. Nest networks are formed when a colony inhabits more than one nest, known as polydomy. Polydomous colonies are composed of distinct sub-colonies that inhabit spatially distinct nests and that share resources with each other. In this study, we performed a controlled experiment on 10 polydomous wood ant (Formica lugubris) colonies to test how changing the resource environment affects the social structure of a polydomous colony. We took network maps of all colonies for 5 years before the experiment to assess how the networks changes under natural conditions. After this period, we prevented ants from accessing an important food source for a year in five colonies and left the other five colonies undisturbed. We found that preventing access to an important food source causes polydomous wood ant colony networks to fragment into smaller components and begin foraging on previously unused food sources. These changes were not associated with a reduction in the growth of populations inhabiting individual nests (sub-colonies), foundation of new nests or survival, when compared with control colonies. Colony splitting likely occurred as the availability of food in each nest changed causing sub-colonies to change their inter-nest connections. Consequently, our results demonstrate that polydomous colonies can adjust to environmental changes by altering their social network.
TL;DR: In this article, a new metaheuristic algorithm, called adaptive coordination ant colony optimization (ACO), is proposed to solve the multi-robot multi-point dynamic aggregation problem.
Abstract: Multipoint dynamic aggregation is a meaningful optimization problem due to its important real-world applications, such as post-disaster relief, medical resource scheduling, and bushfire elimination. The problem aims to design the optimal plan for a set of robots to execute geographically distributed tasks. Unlike the majority of scheduling and routing problems, the tasks in this problem can be executed by multiple robots collaboratively. Meanwhile, the demand of each task changes over time at an incremental rate and is affected by the abilities of the robots executing it. This poses extra challenges to the problem, as it has to consider complex coupled relationships among robots and tasks. To effectively solve the problem, this article develops a new metaheuristic algorithm, called adaptive coordination ant colony optimization (ACO). We develop a novel coordinated solution construction process using multiple ants and pheromone matrices (each robot/ant forages a path according to its own pheromone matrix) to effectively handle the collaborations between robots. We also propose adaptive heuristic information based on domain knowledge to promote efficiency, a pheromone-based repair mechanism to tackle the tight constraints of the problem, and an elaborate local search to enhance the exploitation ability of the algorithm. The experimental results show that the proposed adaptive coordination ACO significantly outperforms the state-of-the-art methods in terms of both effectiveness and efficiency.
TL;DR: A chaotic ant colony optimized (CACO) link prediction algorithm is proposed, which integrates the chaotic perturbation model and ant colony optimization and achieves significantly higher prediction accuracy and robustness than most of the state-of-the-art algorithms.
Abstract: The mining missing links and predicting upcoming links are two important topics in the link prediction. In the past decades, a variety of algorithms have been developed, the majority of which apply similarity measures to estimate the bonding probability between nodes. And for these algorithms, it is still difficult to achieve a satisfactory tradeoff among precision, computational complexity, robustness to network types, and scalability to network size. In this article, we propose a chaotic ant colony optimized (CACO) link prediction algorithm, which integrates the chaotic perturbation model and ant colony optimization. The extensive experiments on a wide variety of unweighted and weighted networks show that the proposed algorithm CACO achieves significantly higher prediction accuracy and robustness than most of the state-of-the-art algorithms. The results demonstrate that the chaotic ant colony effectively takes advantage of the fact that most real networks possess the transmission capacity and provides a new perspective for future link prediction research.
TL;DR: Rapid evolution of metabolic rate and locomotor performance in acorn‐dwelling ants in response to urban heat island effects is explored to compensate for evolved increases in metabolic rate by allowing workers to more quickly scout and retrieve resources.
Abstract: Metabolic rates of ectotherms are expected to increase with global trends of climatic warming. But the potential for rapid, compensatory evolution of lower metabolic rate in response to rising temperatures is only starting to be explored. Here, we explored rapid evolution of metabolic rate and locomotor performance in acorn-dwelling ants (Temnothorax curvispinosus) in response to urban heat island effects. We reared ant colonies within a laboratory common garden (25°C) to generate a laboratory-born cohort of workers and tested their acute plastic responses to temperature. Contrary to expectations, urban ants exhibited a higher metabolic rate compared with rural ants when tested at 25°C, suggesting a potentially maladaptive evolutionary response to urbanization. Urban and rural ants had similar metabolic rates when tested at 38°C, as a consequence of a diminished plastic response of the urban ants. Locomotor performance also evolved such that the running speed of urban ants was faster than rural ants under warmer test temperatures (32°C and 42°C) but slower under a cooler test temperature (22°C). The resulting specialist-generalist trade-off and higher thermal optimum for locomotor performance might compensate for evolved increases in metabolic rate by allowing workers to more quickly scout and retrieve resources.
TL;DR: A multiple ant colony optimization (LDTACO) based on novel Long Short-Term Memory network and adaptive Tanimoto communication strategy that can lead to more accurate solution accuracy and faster convergence speed is proposed.
Abstract: Ant Colony Optimization (ACO) tends to fall into local optima and has insufficient convergence when solving the Traveling Salesman Problem (TSP). To overcome this problem, this paper proposes a multiple ant colony optimization (LDTACO) based on novel Long Short-Term Memory network and adaptive Tanimoto communication strategy. Firstly, we introduce an Artificial Bee Colony-based Ant Colony System (ABC-ACS), which along with the classic Ant Colony System (ACS) and Max-Min Ant System (MMAS), form the final proposed algorithm. These three types of subpopulations complement each other to improve overall optimization performance. Secondly, the evaluation reward mechanism is proposed to enhance the guiding role of the Recommended paths, which can effectively accelerate convergence speed. Besides, an adaptive Tanimoto communication strategy is put forward for interspecific communication. When the algorithm is stagnant, the homogenized information communication method is activated to help the algorithm jump out of the local optima, thus improving solution accuracy. Finally, the experimental results show that the proposed algorithm can lead to more accurate solution accuracy and faster convergence speed.
TL;DR: Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows and has practical implications for vehicle routing problem.
Abstract: In this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.
TL;DR: An intelligent optimization algorithm of adaptive ant colony and particle swarm optimization is proposed that has fast convergence speed, strong optimization ability, and can obtain better optimization results and has some advantages in solving vehicle routing problem.
Abstract: Aiming at vehicle routing problem and combining the advantages of ant colony and particle swarm optimization, an intelligent optimization algorithm of adaptive ant colony and particle swarm optimization is proposed. Through the simulation of ant colony and bird swarm intelligence mechanism, the particle swarm algorithm and the ant colony algorithm heuristic strategy are combined, and different search strategies are used in different stages of the algorithm. The adaptive adjustment is adopted, and the feedback information is obtained by dynamic interaction with the environment, thus speeding up the convergence speed, improving the learning ability, avoiding the local optimum, getting the best solution and improving the efficiency. The simulation experiment shows that the algorithm has fast convergence speed, strong optimization ability, and can obtain better optimization results. It has some advantages in solving vehicle routing problem.
01 Apr 2021
TL;DR: In this article, the authors proposed a double ant colony algorithm (NDACA) based on dynamic feedback to realize an accurate and efficient route planning for ships in a complex marine environment, where the number of ants in each colony is continuously adjusted, which ensures the solution quality and speed of convergence of the algorithm.
Abstract: To realize an accurate and efficient route planning for ships in a complex marine environment, the novel double ant colony algorithm (NDACA) based on dynamic feedback is proposed in this work. The planning process to identify the lowest energy consumption route is used as an example to introduce the applications of the proposed algorithm. First, the energy consumption model is established by analyzing the ship's motion, which is used in the pheromone updating strategy. Next, based on the energy consumption information of the route, the ant colony is divided into exploratory and optimized ants. Using a closed-loop feedback strategy, the number of ants in each colony is continuously adjusted, which ensures the solution quality and speed of convergence of the algorithm. Simulations in the working attribute environment show that NDACA has an all-round good performance. Compared with other algorithms, NDACA can plan a more energy-saving route while considering the influence of marine environmental factors, which has great practical significance for ship operations management.
TL;DR: In this article, the authors proposed a node payload balanced ant colony optimal cooperative routing (PB-ACR) protocol for multi-hop UASNs, through combining the ant colony algorithm and cooperative transmission.
Abstract: For a given source-destination pair in multi-hop underwater acoustic sensor networks (UASNs), an optimal route is the one with the lowest energy consumptions that usually consists of the same relay nodes even under different transmission tasks. However, this will lead to the unbalanced payload of the relay nodes in the multi-hop UASNs and accelerate the loss of the working ability for the entire system. In this paper, we propose a node payload balanced ant colony optimal cooperative routing (PB-ACR) protocol for multi-hop UASNs, through combining the ant colony algorithm and cooperative transmission. The proposed PB-ACR protocol is a relay node energy consumption balanced scheme, which considers both data priority and residual energy of each relay node, aiming to reduce the occurrence of energy holes and thereby prolong the lifetime of the entire UASNs. We compare the proposed PB-ACR protocol with the existing ant colony algorithm routing (ACAR) protocol to verify its performances in multi-hop UASNs, in terms of network throughput, energy consumption, and algorithm complexity. The simulation results show that the proposed PB-ACR protocol can effectively balance the energy consumption of underwater sensor nodes and hence prolong the network lifetime.