Basic Ant Colony Optimization Algorithm Research Articles

Dynamic planning method of evacuation route in dam-break flood scenario based on the ACO-GA hybrid algorithm

As the major water storage infrastructure, the failure of a reservoir dam will cause significant losses to people's lives and property. A scientific and reasonable evacuation route is one of the essential measures to reduce casualties. Thus, it is of great significance to realize the dynamic planning of evacuation routes in dam-break flood scenarios to reduce the risk of dam failure. This study employed a physically based mathematical model (Breach model) and a two-dimensional hydrodynamic model to simulate dam-break flood routing. Further, a road network construction method was proposed based on the graph theory and flood routing information. The ant colony optimization algorithm (ACO) was improved by the backtracking, the improved heuristic function, and the elite ants, and the genetic algorithm (GA) was optimized according to the characteristics of flood avoidance route planning. Accordingly, an improved ant colony-genetic optimization hybrid algorithm (ACO-GA) was proposed. Compared with the basic ACO algorithm, the correct rate of simulation results of the ACO-GA hybrid algorithm was improved by 34 %, the average number of iterations was reduced by 18.3 times, and the optimization ability was markedly enhanced. Based on the constructed dam-break flood scenario and the ACO-GA hybrid algorithm, the novel dynamic planning method for evacuation routes in the dam-break flood scenario was proposed. Moreover, this method was applied to a typical water conservancy engineering project, and the planned optimal evacuation routes accurately avoided the flood influence, and the calculation results were reasonable and accurate. The real-time dynamic planning of flood avoidance routes based on flood routing information was realized. This study provides effective guarantee for the emergency transfer of downstream people, which has valuable implications for the theoretical research and engineering practice of reservoir dam safety and management.

Modification of the Ant Colony Optimization Algorithm for Solving Multi-Agent Task Allocation Problem in Agricultural Application

This paper considers the problem of task allocation where the goal is to find a coalition of UAVs (agents) to complete on-farm agricultural tasks. In this study, Ant Colony Optimization (ACO) algorithm is employed to find the best coalition of agents. The performance of the basic ACO algorithm for solving task allocation is improved by modifying the efficiency factor. In the proposed algorithm, the efficiency factor is defined as a function that relates not only to the capability of the agents and the distance between the agents, but also to the distance between the agents and the target. To solve the task allocation problem, the capability list of the agents was also adjusted using common UAV capabilities in agricultural application. Simulation results showed that the proposed ACO algorithm with the modified efficiency factor improved the performance of basic ACO algorithm for solving task allocation problem in terms of the average total travel cost for each agent. The optimum number of ants and agents in the proposed algorithm was also analysed for robust performance. Simulation results revealed that the addition of the numbers of agents and ants increases the average efficiency of the algorithm. In this study, we have also added a function to calculate the system capability utilization. By employing such a function, simulation results show that the total resource used by the agents and total communication cost can be optimized. In addition, a simple experiment using five ground robots with a centralized control was also carried out as a proof of concept for the proposed algorithm.

A novel travel route planning method based on an ant colony optimization algorithm

Abstract As people’s living standards improve, tourism has become an important way for people to spend their time on leisure and entertainment. The growing number of tourists in recent years has given rise to the creation of tourism-related ancillary services. Travelers need to choose a travel route that suits their needs and expectations and do it in a way that does not cause a waste of time, whether it is an emerging self-driving tour or a traditional tour group. Therefore, the optimization of tourist routes is of great significance to the majority of tourists. Given the planning requirements of tourist attractions in the post-epidemic era, an ant colony-based optimization algorithm is proposed to resolve the planning problem of optimal tourist routes. An optimized pheromone update strategy is also proposed based on the basic ant colony optimization algorithm. The optimized ant colony algorithm tries to balance two conflicting concepts, namely, flows into tourist attractions and the carrying capacity of destinations. To analyze the performance of the proposed optimization algorithm, the effects of different optimization algorithms on the route planning of tourist attractions were compared in the experiment, and the acceleration ratio of the optimized ant colony algorithm was tested using the graphics processing unit parallel computing program. The results show that the proposed algorithm provides certain advantages and has certain potential in parallel computing. To sum up, this study provides a better scientific basis for optimal tourist route planning and has a good reference value.

Improved wireless sensor network data collection using discrete differential evolution and ant colony optimization

The main objective of this study is to optimize the performance of a wireless sensor network (WSN) in terms of energy consumption and data routing delay using evolutionary metaheuristics. A WSN mobile sink-based routing method in which the sink moves to certain selected rendezvous nodes for data collection is used to address the hot-spot problem. However, there are two challenges, namely, clustering and mobile sink shortest trajectory traversal, which significantly affect the network energy consumption, lifetime, and delay. To achieve the goal of this study, first, a formal model is presented to solve the optimization problem of determining the optimal number of clusters and corresponding cluster heads, which are taken as rendezvous nodes accessed by the mobile sink considering the network energy consumption, intracluster communication, and transmission delay. Second, a discrete differential evolution algorithm is proposed to solve the formulated optimization problem. Third, an ant colony optimization-based algorithm is proposed to construct the shortest path for the mobile sink to traverse the selected rendezvous nodes. The experimental evaluations show that the proposed strategy significantly improves cluster head selection, balances the cluster members, increases the network lifetime by 54%, decreases the transmission delay by 63%, and reduces energy consumption by 47% compared to several routing strategies in the literature.

Study on Multi-Objective Optimization of Logistics Distribution Paths in Smart Manufacturing Workshops Based on Time Tolerance and Low Carbon Emissions

In the Industry 4.0 environment, an ideal smart factory should be intelligent, green, and humanized, and the logistics transportation from raw materials to final products in the factory should be completed by smart logistics. In order to address the problems of low efficiency, poor workstation service satisfaction, high distribution costs, and non-greening during the logistics distribution processes in discrete smart manufacturing workshops are required. A mathematical model of optimized multi-objective green logistics distribution paths in a smart manufacturing workshop has been constructed in this study, with low costs, high efficiency, and workstation service satisfaction taken into consideration. Then, this mathematical model was solved with an improved ant colony optimization algorithm. A “time window span” was introduced in the basic ant colony optimization algorithm to prioritize the services to workstations with a relatively high urgency in material demand, with the aim of improving workstation service satisfaction. Lastly, in order to verify the effectiveness of the model and algorithm proposed in this study, a simulation experiment has been conducted on the workstation logistics distribution system in a smart manufacturing workshop to provide convincing evidence for optimizing workstation logistics distribution paths in workshops of discrete manufacturing enterprises.

Specialized path-based technique to test Internet of Things system functionality under limited network connectivity

Contemporary Internet-of-Things (IoT) systems are hindered by several reliability-related issues, especially, the dynamic behavior of IoT systems caused by limited and often unstable network connectivity. Several intuitive ad-hoc approaches can be employed to test this behavior; however, the effectiveness of these approaches in detecting defects and their overall testing costs remain questionable. Therefore, we present a new specialized path-based technique to test the processes of an IoT system in scenarios wherein parts of these processes are influenced by limited or disrupted network connectivity. The proposed technique can be scaled using two levels of test coverage criteria to determine the strengths of the test cases. For this purpose, we propose two algorithms for generating test cases to implement the technique: an ant colony optimization-based search and a graph-traversal-based test case composition. We compared the efficiency of the proposed approach with possible solutions obtained using a standard path-based testing approach based on prime paths computed by a set-covering algorithm. We consider the total number of test case steps as the main proxy for test effort in experiments employing 150 problem models. For the less intensive of the two used test-coverage criteria, EachBorderOnce, an ant colony optimization-based algorithm, produced test sets with the same averaged number of steps as the graph traversal-based test-case composition; however, this algorithm performed with averaged number of steps 10% lower than a prime paths-based algorithm. For the more intensive test coverage criterion, AllBorderCombinations, these differences favoring the ant colony optimization-based algorithm were 18% and 25%, respectively. For these two types of defined test coverage criteria, the ant colony optimization-based search, graph-traversal-based algorithm, and standard path-based testing approach based on prime paths achieved the best results for 93 and 78, 14 and 24, and 13 and 17 models for AllBorderCombinations and EachBorderOnce criterion, respectively. Therefore, to guarantee the best test set, all compared algorithms are combined in a portfolio strategy that yields the best results based on the potential of the produced test sets to detect simulated defects caused by limited network connectivity. Additionally, this portfolio strategy also yields test sets, implying the lowest test effort for experimental problem instances.

Controlling a scattered field output of light passing through turbid medium using an improved ant colony optimization algorithm

An improved ant colony optimization algorithm has been developed in the present study to control an output scattered light field after an incident beam of light passes through a turbid medium. In this newly developed algorithm, the search rules and pheromone release rules of the basic ant colony optimization algorithm were modified which led to an improved search ability of the algorithm. Through reasonable designing of the cost function, the improved ant colony optimization algorithm can shape a scattered light field through turbid medium to predetermined shape. Furthermore, simulations and experiments showed that the improved ant colony optimization algorithm offers many advantages, such as good optimization effect, rapid convergence and strong anti-interference ability.

Improved Ant Colony Genetic Algorithm for Solving Traveling Salesman Problem

A new improved ant colony genetic algorithm(IACGA) is proposed to solve the problems of the basic ant colony optimization algorithm(ACO), such as the slow convergence rate and the local optimal solution. Based on the ACO model, local pheromone increment is updated differently according to the path quality constructed by ants, the current optimal path pheromone residual factor is adjusted adaptively, pheromone updating rules are improved, genetic operator is embedded and genetic algorithm(GA) is dynamically fused. It makes full use of the positive feedback mechanism of ACO and the searching ability of GA to realize the dynamic fusion of ACO and GA. The experimental results show that compared with the ACO and the GA, the improved algorithm can find the global optimal solution quickly, and the solution quality is relatively good, which improves the efficiency of the ant colony algorithm in solving the traveling salesman problem(TSP).

Cooperative Edge Caching With Location-Based and Popular Contents for Vehicular Networks

In this article, we propose a cooperative edge caching scheme, which allows vehicles to fetch one content from multiple caching servers cooperatively. In specific, we consider two types of vehicular content requests, i.e., location-based and popular contents, with different delay requirements. Both types of contents are encoded according to fountain code and cooperatively cached at multiple servers. The proposed scheme can be optimized by finding an optimal cooperative content placement that determines the placing locations and proportions for all contents. To this end, we first analyze the upper bound proportion of content caching at a single server, which is determined by both the downloading rate and the association duration when the vehicle drives through the server's coverage. For both types of contents, the respective theoretical analysis of transmission delay and service cost (including content caching and transmission cost) are provided. We then formulate an optimization problem of cooperative content placement to minimize the overall transmission delay and service cost. As the problem is a multi-objective multi-dimensional multi-choice knapsack problem, which is proved to be NP-hard, we devise an ant colony optimization-based algorithm to solve the problem and achieve a near-optimal solution. Simulation results are provided to validate the performance of the proposed algorithm, including its convergence and optimality of caching, while guaranteeing low transmission delay and service cost.

A Hybrid Method Based on Genetic Algorithm and Ant Colony System for Traffic Routing Optimization

TThis paper presents a hybrid method that combines the genetic algorithm (GA) and the ant colony system algorithm (ACS), namely GACS, to solve the traffic routing problem. In the proposed framework, we use the genetic algorithm to optimize the ACS parameters in order to attain the best trips and travelling time through several novel functions to help ants to update the global and local pheromones. The GACS framework is implemented using the VANETsim package and the real city maps from the open street map project. The experimental results show that our framework achieves a considerably higher performance than A-Star and the classical ACS algorithms in terms of the length of the global best path and the time for trips. Moreover, the GACS framework is also efficient in solving the congestion problem by online monitoring the conditions of traffic light systems.
 KeywordsTraffic routing; Ant colony system; Genetic algorithm; VANET simulator.
 References
 [1] M. Dorigo, Ant colony optimization, Scholarpedia 2(3) (2007) 1461. https://doi.org/10/4249/scholarpedia.1461.[2] M.V. Dorigo, Maniezzo, A. Colorni, Ant system: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 26(1) (1996) 29-41.[3] M. Dorigo, L.M. Gambardella, Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Transactions on evolutionary computation 1(1) (1997) 53-66.[4] M. Dorigo, T. St¨utzle, Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.[5] D. Favaretto, E. Moretti, P. Pellegrini, On the explorative behavior of MAX-MIN Ant. System, In: St¨utzle T, Birattari M, Hoos HH (eds) Engineering Stochastic Local Search Algorithms, Designing, Implementing and Analyzing Effective Heuristics. SLS 2009, LNCS, Springer, Heidelberg, Germany 5752 (2009) 115-119.[6] F. Lobo, C.F. Lima, Z. Michalewicz (eds), Parameter Setting in Evolutionary Algorithms, Springer, Berlin, Germany, 2007.[7] T. Stützle, Manuel López-Ibáñez, Paola Pellegrini, Michael Maur, Marco Montes de Oca, Mauro Birattari, Marco Dorigo, Parameter adaptation in ant colony optimization in Autonomous search, Springer, 2011, pp. 191-215.[8] Z. Cai, H. Huang, Ant colony optimization algorithm based on adaptive weight and volatility parameters in Intelligent Information Technology Application, 2008. IITA'08, Second International Symposium IEEE, 2008.[9] J. Liu, Shenghua Xu, Fuhao Zhang, Liang Wang, A hybrid genetic-ant colony optimization algorithm for the optimal path selection, Intelligent Automation & Soft Computing, 2016, pp. 1-8.[10] D.Gaertner K.L. Clark, On Optimal Parameters for Ant Colony Optimization Algorithms, In IC-AI, 2005.[11] X.Wei, Parameters Analysis for Basic Ant Colony Optimization Algorithm in TSP, Reason 7(4) (2014) 159-170.[12] J.H. Holland, Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence, U Michigan Press, 1975.[13] K.D.E. Sastry, Goldberg, G. Kendall, Genetic algorithms, In Search methodologies, Springer, 2014, pp. 93-117.[14] S.M. Odeh, Management of an intelligent traffic light system by using genetic algorithm, Journal of Image and Graphics 1(2) (2013) 90-93.[15] A.M. Turky, M.S. Ahmad, M.Z.M. Yusoff, B.T. Hammad, Using Genetic Algorithm for Traffic Light Control System with a Pedestrian Crossing, In: Wen P., Li Y., Polkowski L., Yao Y., Tsumoto S., Wang G. (eds) Rough Sets and Knowledge Technology, RSKT 2009, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg 5589 (2009) 512-519.[16] Y.R.B. Al-Mayouf, Mahamod Ismail1, Nor Fadzilah Abdullah, Salih M. Al-Qaraawi, Omar Adil Mahdi, Survey On Vanet Technologies And Simulation Models, 2006.[17] S.A. Ben Mussa, M. Manaf, K.Z. Ghafoor, Z. Doukha, "Simulation tools for vehicular ad hoc networks: A comparison study and future perspectives", 2015 International Conference on Wireless Networks and Mobile Communications (WINCOM), Marrakech, 2015, pp. 1-8.[18] V. Cristea, Victor Gradinescu, Cristian Gorgorin, Raluca Diaconescu, Liviu Iftode, Simulation of vanet applications, Automotive Informatics and Communicative Systems, 2009.[19] L. Liang, J. Ye, D. Wei, Application of improved ant colony system algorithm in optimization of irregular parts nesting, In 2008 Fourth International Conference on Natural Computation, IEEE, 2008.[20] X. Yan, Research on the Hybrid ant Colony Algorithm based on Genetic Algorithm. International Journal of Signal Processing, Image Processing and Pattern Recognition 9(3) (2016) 155-166.

A joint energy- and QoS-aware routing mechanism for WMNs using software-defined networking paradigm

Providing energy conservation together with the quality of service (QoS) becomes more challenging in wireless mesh networks. A promising way to achieve this goal is to design a routing mechanism that takes both energy and QoS into account. On the other hand, software-defined networking (SDN) is an emerging network paradigm which separates the control plane from the data plane and facilitates a high level of programmability and manageability. In this paper, we propose FACOR, a novel SDN-based routing mechanism which aims at minimizing the energy consumption while providing a certain level of QoS for multimedia applications in wireless mesh networks. This technique is essentially suitable for battery-operated mesh nodes with solar panels. To obtain optimal paths, FACOR first estimates the cost of each network link using a fuzzy logic system. The fuzzy part takes parameters related to the quality of service and the energy in order to calculate the cost of the links. The routing problem is then formulated as an integer linear programming (ILP) model. Since ILP is NP-complete, we propose an ant colony optimization-based algorithm to solve the model. The proposed method is implemented in the well-known OpenDayLight controller and strictly follows a modular design for the sake of efficiency. Simulation results confirm the effectiveness of our mechanism which achieves a gain of about 17% and 10% in terms of network lifetime and PSNR, respectively, compared to other methods.

An improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments

One of the important issues in ubiquitous geographic information science (GIS) is designing user-centric path finding algorithms to meet user needs. Mostly, in a route planning problem, the user’s purpose is optimization of two or more objective functions simultaneously. Thus, the problem is a multi-objective problem. In the present study, having considered multi-objective optimization methods in path finding, we developed an algorithm for solving this problem using an improved multi-objective ant colony optimization (ACO) algorithm. Modifications are introduced for various components of the ant colony metaheuristics; specifically, for those associated with the ‘ant decision rule’. The proposed algorithm was tested on the studied network. The results demonstrate that the proposed approach has acceptable settings, repeatability and run time. In addition, one of the important research outputs is a pareto-front which allows the user to select the final path according to the desired priorities.

Beam Selection for mmWave Massive MIMO Systems Under Hybrid Transceiver Architecture

Hybrid analog/digital precoding is a promising technology to cut down hardware cost in massive multi-input multi-output systems at millimeter wave frequencies. In this letter, we propose a joint beam selection scheme for analog domain precoding under discrete lens array. By considering channel correlation among users, the proposed scheme is able to avoid inter-user interference and maximize system sum rate. Moreover, a bio-inspired ant colony optimization-based algorithm is proposed to obtain a near-optimal solution with dramatically reduced computational complexity. Finally, simulations show the advantages of the proposed scheme in improving system sum rate.

A Novel Hybrid Ant Colony Optimization Algorithm for Emergency Transportation Problems During Post-Disaster Scenarios

The increasing impacts of natural disasters have led to concerns regarding predisaster plans and post-disaster responses. During post-disaster responses, emergency transportation is the most important part of disaster relief supply chain operations, and its optimal planning differs from traditional transportation problems in the objective function and complex constraints. In disaster scenarios, fairness and effectiveness are two important aspects. This paper investigates emergency transportation in real-life disasters scenarios and formulates the problem as an integer linear programming model (called cum-MDVRP), which combines cumulative vehicle routing problem and multidepot vehicle routing problem. The cum-MDVRP is NP-hard. To solve it, a novel hybrid ant colony optimization-based algorithm is proposed by combining both saving algorithms and a simple two-step 2-opt algorithm. The proposed algorithm allows ants to go in and out the depots for multiple rounds, so we abbreviate it as ACOMR. Moreover, we present a smart design of the ants’ tabus, which helps to simplify the solution constructing process. The ACOMR could yield good solutions quickly, then the decision makers for emergency responses could do expert planning at the earliest time. Computational results on standard benchmarking data sets show that the proposed cum-MDVRP model performs well, and the ACOMR algorithm is more effective and stable than the existing algorithms.

A joint internal production and external supplier order lot size optimization under defective manufacturing and rework

The traditional production-inventory problems do not consider joint production and order lot sizes in manufacturing systems. This paper considers a defective system to jointly decide on order and production lot sizes in a manufacturing company. The manufacturer produces items with finite processing rate where a percentage of these items are of imperfect quality. At the end of production process, the rework of imperfect items is carried out. The demand rate and the number of defective items are considered to be uncertain parameters. The aim is to determine optimal number of sub-production cycles, as well as optimal order quantity. To address this problem, we first define the problem and describe its characteristics, build the model via deriving total cost function, design an appropriate solution algorithm, and then illustrate and evaluate the results. The solution algorithm is based on an ant colony optimization (ACO) which is enhanced by an adaptive learning strategy. A numerical example is presented and discussed, and the results are compared with basic ACO and genetic algorithm (GA). Finally, sensitivity analysis and robustness analysis are performed and discussed. The observed results reveal the robust performance of proposed approach against basic ACO and GA. Moreover, we observed that the performance of basic ACO and GA is approximately the same, and the basic ACO is more robust than GA in our experiments.

Improved ant colony optimization algorithm based on RNA computing

RNA computing is a new intelligent optimization algorithm, which combines computer science and molecular biology. Aiming at the weakness of slow convergence rate and poor global search ability in the basic ant colony optimization algorithm due to the unreasonable selection of parameters, this paper utilizes the combination of RNA computing and basic ant colony optimization algorithm to overcome the defects. An improved ant colony optimization algorithm based on RNA computing is proposed. In the iterative process of ant colony optimization algorithm, transformation operation, recombination operation and permutation operation in RNA computing are introduced to optimize the initial parameters including importance factor of pheromone trail α, importance factor of heuristic function β and pheromone evaporation rate ρ to improve the convergence efficiency and global search ability. The performance of the algorithm is evaluated on five instances of the library of traveling salesman problems (TSPLIB) and six typical test functions. The experimental results demonstrate that the proposed RNA-ant colony optimization algorithm is superior than basic ant colony optimization algorithm in optimization ability, reliability, convergence efficiency, stability and robustness.

A reliable path between target users and clients in social networks using an inverted ant colony optimization algorithm

Internet has become an integral section of human life. Millions of people are joining online social networks every day, interacting with others whom they did not know already. Establishing trust among those indirectly connected users performs a crucial role in improving the quality of social network services and creating the security for them. Nowadays, there are many paths between clients (service requesters) and target users (service providers) in online social networks. Among existing paths finding a trust path for trustworthy services is a vital job. Also, in many previous methods, such as ant colony optimization algorithm (ACO) load balancing among target users is inefficient. Therefore, in this paper we propose an inverted ant colony optimization algorithm to find a reliable path along with improving load balancing among the target users. The inverted ant colony optimization algorithm is a diversity of the basic ant colony optimization algorithm in which, the updated pheromone has a reverse effect on the selected path by the ants. Finally, we simulate the proposed method by using the original experimental dataset and evaluate the proposed method in terms of load balancing, waiting time and execution time in comparison with the ant colony optimization algorithm. The obtained results are very promising.

An ant colony optimization algorithm-based classification for the diagnosis of primary headaches using a website questionnaire expert system

The purpose of this research was to evaluate the classification accuracy of the ant colony optimization algorithm for the diagnosis of primary headaches using a website questionnaire expert system that was completed by patients. This cross-sectional study was conducted in 850 headache patients who randomly applied to hospital from three cities in Turkey with the assistance of a neurologist in each city. The patients filled in a detailed web-based headache questionnaire. Finally, neurologists' diagnosis results were compared with the classification results of an ant colony optimization-based classification algorithm. The ant colony algorithm for diagnosis classified patients with 96.9412% overall accuracy. Diagnosis accuracies of migraine, tension-type, and cluster headaches were 98.2%, 92.4%, and 98.2% respectively. The ant colony optimization-based algorithm has a successful classification potential on headache diagnosis. On the other hand, headache diagnosis using a website-based algorithm will be useful for neurologists in order to gather quick and precise results as well as tracking patients for their headache symptoms and medication usage by using electronic records from the Internet.

ACO-Based Thermal-Aware Thread-to-Core Mapping for Dark-Silicon-Constrained CMPs

The limitation on thermal budget in chip multiprocessor (CMP) results in a fraction of inactive silicon regions called dark silicon, which significantly impacts the system performance. In this paper, we propose a thread-to-core mapping method for dark-silicon-constrained CMPs to address their thermal issue. We first propose a thermal prediction model to forecast CMP temperature after the CMP executes a forthcoming application. Then, we develop an ant colony optimization-based algorithm to conduct the thread-to-core mapping process, such that the CMP peak temperature is minimized and, consequently, the probability of triggering CMP dynamic thermal management is decreased. Finally, we evaluate our method and compare it with the baseline (a standard Linux scheduler) and other existing methods (NoC-Sprinting, DaSiM mapping, and TP mapping). The simulation results show that our method gains good thermal profile and computational performance, and performs well with chip scaling. Specifically, it eliminates all thermal emergency time, outperforming all other methods, and gains million instructions per second improvement up to 12.9% against the baseline.

Research on an Improved ACO Algorithm Based on Multi-Strategy for Solving TSP

Ant colony optimization (ACO) algorithm is a metaheuristic inspired by the behavior of real ants in their search for the shortest path to food sources. The ACO algorithm takes on these characteristics of robust, positive feedback distributed computing, easy fusing with other algorithms. But the basic ACO algorithm has some deficiencies of premature and stagnation phenomenon in the evolution process, and is easily trapped into local optimal solution. And it is difficult to explore other solutions in the neighbor space. So a improved ACO(DPSEMACO) algorithm based on dual population strategy, bi-directional dynamic adjust evaporation factor strategy of the pheromone and parallel strategy is proposed to solve the traveling salesman problem(TSP). In the DPSEMACO algorithm, the ants are divided into the two subpopulations by borrowing the mutual cooperation mechanism of biological community, which evolve separately and exchange information timely. The bi-directional dynamic adjusting evaporation factor strategy of the pheromone is used to change the corresponding path pheromone of different subpopulations in order to avoid to trap into a local optimum. The parallel strategy can avoid falling into a local optimum. And the DPSEMACO algorithm can expand the search space and improve the overall searching performance by repeated changing the pheromone of the each subpopulation and adaptive adjusting evaporation factor. Finally, in order to prove the optimization performance of the proposed DPSEMACO algorithm, some classic TSP instances are selected from the TSPLIB in this paper. And some existing methods are selected to compare the optimization performance with the proposed DPSEMACO algorithm. The experimental results demonstrate that the proposed DPSEMACO algorithm is feasible and effective in solving TSP, and takes on a good global searching ability and high convergence speed.