Pheromone Update Research Articles

An improved ant colony optimization algorithm based on context for tourism route planning.

To solve the problem of one-sided pursuit of the shortest distance but ignoring the tourist experience in the process of tourism route planning, an improved ant colony optimization algorithm is proposed for tourism route planning. Contextual information of scenic spots significantly effect people's choice of tourism destination, so the pheromone update strategy is combined with the contextual information such as weather and comfort degree of the scenic spot in the process of searching the global optimal route, so that the pheromone update tends to the path suitable for tourists. At the same time, in order to avoid falling into local optimization, the sub-path support degree is introduced. The experimental results show that the optimized tourism route has greatly improved the tourist experience, the route distance is shortened by 20.5% and the convergence speed is increased by 21.2% compared with the basic algorithm, which proves that the improved algorithm is notably effective.

Approximate Solution by Ant Colony Programming to Symmetrical Drop Suspended Equilibrium Equation

The paper presents the Ant Colony Programming (ACP) method as a solver for Differential Equations of a suspended symmetric drop. The Ant Colony method developed by Gambardella Dorigo in 1997 has been applied in Routing, Scheduling, Constraint Satisfaction, Graph Coloring, and such other optimization problems. The Differential Equation Solution can be expressed as an expression containing Terminals and Functions and both are represented as a graph with nodes and edges. In this graph, each node (city) represents a function or terminal, the information flow represents pheromone quantity disposed by ants while walking. An initial quantity of pheromone is disposed on links between nodes. After that and until termination criteria, ants construct solutions (paths) from the source to the destination by visiting nodes, and deposing (updating) pheromones quantity at links of the path. This is one of the advantages of Ant Colony Programming, i.e. the diversification by local pheromone update. The select step is fundamental, too. In this step, the ant reflects the performance of the link in the cost path according to fitness value. The simulation experiments show that Ant Colony Programming method can obtain very good results in finding solution.

ACO with Intuitionistic Fuzzy Pheromone Updating Applied on Multiple-Constraint Knapsack Problem

Some of industrial and real life problems are difficult to be solved by traditional methods, because they need exponential number of calculations. As an example, we can mention decision-making problems. They can be defined as optimization problems. Ant Colony Optimization (ACO) is between the best methods, that solves combinatorial optimization problems. The method mimics behavior of the ants in the nature, when they look for a food. One of the algorithm parameters is called pheromone, and it is updated every iteration according quality of the achieved solutions. The intuitionistic fuzzy (propositional) logic was introduced as an extension of Zadeh’s fuzzy logic. In it, each proposition is estimated by two values: degree of validity and degree of non-validity. In this paper, we propose two variants of intuitionistic fuzzy pheromone updating. We apply our ideas on Multiple-Constraint Knapsack Problem (MKP) and compare achieved results with traditional ACO.

Research on inventory path optimization of VMI large logistics enterprises based on ant colony algorithm

Based on the idea of vendor management (VMI) inventory, this paper focuses on the integration and optimization of transportation and inventory control in large-scale logistics system, so as to minimize the total cost of the logistics system. Aiming at the inventory path optimization of VMI large logistics enterprises, based on ant colony algorithm, an improved ant colony algorithm, namely ant colony system algorithm, is designed to solve the model. The algorithm model combines deterministic selection and random selection to obtain a comprehensive probability, and combines local and global pheromone updating. In this paper, we study a two-level supply chain system with multiple customers from one supplier and inventory routing problem. According to the characteristics of inventory routing problem, a single cycle inventory path optimization model with time window constraint based on stochastic demand is established. The goal is to minimize the total system cost, including inventory cost of downstream customers, system transportation cost and penalty cost for not meeting the time window. According to the characteristics of the model, the ant colony algorithm is selected as the optimization method, and the improved ant colony algorithm is designed to solve the example, and the feasibility of the algorithm and model is verified.

Multi objective functions of constraint optimal power flow based on modified ant colony system optimization technique

This article presents the development of Ant Colony System algorithm for solving the constrained Optimal Power Flow (OPF) problem. In this modification of ant colony, a local and global update of pheromone has been used. This optimization technique is used to adjust an optimal control variables of the system while satisfying the constraint of the state variables in their limits. The proposed algorithm tested on the IEEE-30 bus with different cases of single and multiple OPF objectives. The obtained results are analysed and compared with other previous studies. This comparison illustrates the efficiency of the ACS technique for solving different OPF problems of complex objective functions where the propose algorithm of ACS has been reduce the single objective function of total fuel cost, power losses, voltage profile improvement and the total generation emission to minimum value of 800.83 $/h, 3.2723 MW, 0.1194 pu and 0.2066 ton/h respectively with reduction of these objective function of 11.216%, 43.991%, 89.660%, 13.952% respectively. The authors have been used the MATLAB software for programming the propose technique without using any simulators. The Ant Colony System has the facility of simplicity in analysis, easy in program, faster in execution, less iteration with accurate result over the other optimization techniques.

Presenting New Methods for Improving the Ant-Miner Algorithm

Background and Objectives: The Ant-Miner algorithm works based on Ant Colony Optimization as a tool for data analysis , and is used to explore classified laws from a set of data. In the current study, two new methods have been proposed for the purpose of optimizing this algorithm. The first method adopted logical negation operation on the records of the produced laws, while the second employed a new Pheromone Update strategy called “Generalized exacerbation of quality conflict”. The two proposed methods were executed in Visual studio C#.Net , and 8 public datasets were applied in the test. Each one of these datasets was executed 10 times both in an independent way and combined with others, and the average results were recorded. Methods: In this study, we have proposed two approaches for the earlier method. Using the first method in the construction of rule records, idioms that include the rules can be made in the form of . Compared to the idioms of the early algorithm, these idioms are more compatible while constructing rules with high coverage. The advantage of this generalization is the reduction of the produced rules, which results in greater understandability of the output. During the process of pheromone update in the ordinary ACO algorithms, the amount of the sprayed pheromone is a function of the quality of rules. The objective of the second method is to strengthen the conflict between not-found, weak, good, and superior solutions. This method is a new strategy of pheromone update where ants with high-quality solutions are motivated through increasing the amount of pheromone sprayed on the trail that they have found; conversely, the ants that find weaker solutions are punished through eliminating pheromone from their trails. Results: The optimization of the initial algorithm using the two proposed methods produces a smaller number of rules, but increases the number of construction diagrams and prevents the production of low-quality rules. Conclusion: The results of tests performed on the dataset indicated the enhancement of algorithm efficiency in idioms of fewer tests, increased prediction accuracy of laws, and improved comprehensibility of the produced laws using the proposed methods. ======================================================================================================Copyrights©2021 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================

Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control

A global path planning method is proposed based on improved ant colony optimization according to the slow convergence speed in mobile service robot path planning. The distribution of initial pheromone is determined by the critical obstacle influence factor. The influence factor is introduced into the heuristic information to improve the convergence speed of the algorithm at an early stage. A new pheromone update rule is presented using fuzzy control to change the value of pheromone heuristic factor and expectation heuristic factor, adjusting the evaporation rate in stages. The method achieves fast convergence and guarantees global search capability. Finally, the simulation results show that the improved algorithm not only shortens the running time of global path planning, but also has a higher probability of obtaining a global optimal solution. The convergence speed of the algorithm is better than the traditional ant colony algorithm.

A secure multicast routing algorithm Based on distributed PCE in multi-domain optical networks

Aiming at the calculation of secure multicast routing in multi-domain optical network, a new secure multicast routing algorithm based on distributed path calculation element was proposed. Firstly, the distributed PCE multicast routing model was constructed, and the intra-domain cost, inter-domain cost, maximum available wavelength and related constraints were defined. Secondly, the pheromone update strategy of ant colony algorithm with ant queen was locally simplified and modified to make it suitable for the distributed PCE architecture of multi-domain optical network. At the same time, by optimizing the trust value calculation method in the lightweight cross domain trust model, the algorithm can be applied to the calculation of trust value of multicast routing in multi domain optical networks. Finally, the specific implementation steps of the algorithm were given. The analysis and experimental results show that the algorithm can not only ensure the security of cross domain multicast routing, but also prevent the malicious nodes from destroying. Compared with the typical multicast routing algorithm, it has better performance in multicast tree cost and blocking rate in malicious node environment.

Multi-dimensional QoS Cloud Computing Task Scheduling Strategy Based on Improved Ant Colony Algorithm

This paper proposed a multi-dimensional QoS cloud computing task scheduling algorithm based on improved ant colony algorithm, considering QoS demand of users and load balancing of cloud platform comprehensively. First, this paper defines a QoS model composed of the completion time and execution cost of tasks, and defines the cloud platform load balancing constraint function. Secondly, in view of the shortcomings of ant colony algorithm such as slow convergence speed and easy to fall into local optimum, the pheromone update method and expected heuristic function are improved, and the pheromone strength is dynamically changed. Finally, the simulation is carried out in cloudsim and compared with the ACS algorithm and the MMAS algorithm. Experimental results show that the algorithm in this paper is better than these two algorithms in terms of user satisfaction and cloud platform load.

Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm

In view of the shortcomings of traditional ant colony algorithm (ACO) in path planning of indoor mobile robot, such as a long time path planning, non-optimal path for the slow convergence speed, and local optimal solution characteristic of ACO, an improvement adaptive ant colony algorithm (IAACO) is proposed in this paper. In IAACO, firstly, in order to accelerate the real-time and safety of robot path planning, angle guidance factor and obstacle exclusion factor are introduced into the transfer probability of ACO; secondly, heuristic information adaptive adjustment factor and adaptive pheromone volatilization factor are introduced into the pheromone update rule of ACO, to balance the convergence and global search ability of ACO; Finally, the multi-objective performance indexes are introduced to transform the path planning problem into a multi-objective optimization problem, so as to realize the comprehensive global optimization of robot path planning. The experimental results of main parameters selection, path planning performance in different environments, diversity of the optimal solution show that IAACO can make the robot attain global optimization path, and high real-time and stability performances of path planning.

Research on Route Planning based on improved Ant Colony Algorithm

In order to enhance the safe flight performance of UAV and better complete complex tasks, it is extremely necessary to study route planning. Firstly, the feature points of the landform landform are extracted as the flight track points of the UAV, and the track planning problem is transformed into the traveling salesman problem. Secondly, an adaptive pheromone update method is proposed, and an improved ant colony algorithm for optimizing the local pheromone is proposed to avoid the basic ant colony algorithm from falling into the local optimal solution and improve the global search ability. Finally, the proposed algorithm is verified by simulation experiments. The experimental results show that the improved ant colony algorithm is practical and superior, and can effectively solve the problem of route planning and obtain the optimal path of route planning.

A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

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.

Distributed Path Planning of Unmanned Aerial Vehicle Communication Chain Based on Dual Decomposition

Limited by the insufficiency of single UAV’s load and flight time capabilities, the multi‐UAV (unmanned aerial vehicle) collaboration to improve mission efficiency and expand mission functions has become the focus of current UAV theory and application research. In this paper, the research on UAV global path planning is carried out using the ant colony algorithm, and an indoor UAV path planning model based on the ant colony algorithm is constructed. In order to improve the efficiency of the algorithm, enhance the adaptability and robustness of the algorithm, a distributed path planning algorithm based on the dual decomposition UAV communication chain is proposed. This algorithm improves the basic ant colony algorithm from the aspects of path selection, pheromone update, and rollback strategy in view of the inherent shortcomings of the ant colony algorithm. In order to achieve the best performance of the algorithm, this paper analyzes each parameter in the ant colony algorithm in depth and obtains the optimal combination of parameters. The construction method of the Voronoi diagram was improved, and the method was simulated to verify that the method can obtain a Voronoi diagram path that is safer than the original method under certain time conditions. Through the principle analysis and simulation verification of the Dijkstra algorithm and the dual decomposition ant colony algorithm, it is concluded that the dual decomposition ant colony algorithm is more efficient in pathfinding. Finally, through simulation, it was verified that the dual decomposition ant colony algorithm can plan a safe and reasonable flight path for multiple UAV formation flights in an offline state and achieve offline global obstacle avoidance for multiple UAVs.

An Adaptive Variable Neighborhood Search Ant Colony Algorithm for Vehicle Routing Problem With Soft Time Windows

In this paper, an adaptive variable neighborhood search ant colony algorithm (AVNSACA) is proposed to solve the vehicle routing problem with soft time windows (VRPSTW). The ant colony algorithm’s pheromone update strategy is improved to make up for the lack of pheromone in the algorithm’s early stage. In order to avoid the algorithm falling into local optimum, two variable neighborhood search operators are designed, and the conditions for the algorithm to enter the variable neighborhood search are set. The effectiveness of AVNSACA in solving vehicle routing problem with soft time windows is verified by Solomon benchmark problem. Through the comparative analysis of the experimental results of the two algorithms, the advantages of the improved ant colony algorithm are illustrated. The experimental results show that the proposed algorithm can effectively obtain better solutions.

Path planning of mobile robot based on improved ant colony algorithm for logistics.

The path planning of robot is of great significance for the logistics industry, which helps to improve the efficiency of warehousing, sorting and distribution. On the basis of ant colony algorithm, multi step search strategy is used instead of single step search strategy, pheromone update mechanism is redesigned, and path smoothing is configured to improve the performance of the algorithm. The experimental results show that the improved ant colony algorithm proposed in this paper can plan a shorter optimal path on the 16 * 16 grid logistics storage site, and the path length is saved by 9.21%.

Novel Method for Solving Balance and Unbalance Assignment Problem Using New Approach for Ant Colony Algorithm

Assignment Problem (AP) is the fundamental application of TP studied in the area of Operations research. The AP is also an essential problem in the field of optimization, where the goal is to optimize production time or cost by allocating one task to one machine, one machine to one job, one destination to one source, or one source to one destination. Various solutions for resolving the assignment problem have been offered in the literature. Excellent response to task problems is obtained using the Hungarian technique. In this study, however, we are discussing a novel alternative strategy that is the almost best performance. Here, we are looking at another technique to resolve algorithm and solution steps AP. In addition, we present an innovative alternative strategy that provides the best performance of APs by utilizing a modified ant colony optimization (ACO) algorithm. A few revisions to the ant colony algorithm (Transition Rule and Pheromone Update Rule) are created, resulting in a solution that is extremely close to the ideal solution. This method is also to be noticed that, requires the least number of steps to reach optimality as compare the obtained results with other well-known meta-heuristic algorithms. Finally, a numerical example is provided to demonstrate this procedure.

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.

Simulation of English intelligent system based on CA-IAFSA algorithm and artificial intelligence

The traditional English teaching mode mostly relies on rote memorization of textbooks, but it lacks the training of oral expression skills and lacks intelligent guidance for students. Taking machine learning algorithm as the system algorithm, this paper combines the CA-IAFSA algorithm to construct an English intelligent system based on artificial intelligence. The system uses image recognition technology, introduces population pheromone and tribal pheromone, and adopts multiple ant colony planning and dual pheromone feedback strategies. Moreover, this paper improves the heuristic information search strategy, pheromone update strategy, and state transition probability of the basic ant colony algorithm. In addition, this paper proposes the MACDPA path planning algorithm to realize the intelligent analysis of English textbook images. Finally, after constructing the model, this paper conducts research and analysis on the performance of the model and uses controlled experimental methods and mathematical statistics to analyze the data. The research results show that the model constructed in this paper performs well in assisted teaching and intelligent translation and meets the expected requirements.

ACOTSP-MF: A memory-friendly and highly scalable ACOTSP approach

Ant Colony Optimization (ACO) is a population-based meta-heuristic inspired by the social behavior of ants. It is successfully applied in solving many NP-hard problems, such as the Traveling Salesman Problem (TSP). Large-sized instances pose two memory problems to the ACOTSP algorithm: the memory size and the memory bandwidth.This work has focused on developing ACOTSP-MF, a new ACOTSP algorithm proposed to adequately manage the memory issues that arise while solving large TSP instances. ACOTSP-MF uses the nearest neighbor list, introducing a novel class of cities, the backup cities, while grouping cities into three classes: the nearest neighbor cities, the backup cities, and the rest of the cities (the majority). ACOTSP-MF also modifies how the base ACOTSP carries out the tour construction and pheromone update phases depending on the group to which a city belongs. This way, ACOTSP-MF reduces both the memory requirements of its data structures (from O(n∗n) to O(n)), and the memory bandwidth needs (thanks to better exploitation of the memory data locality).In this paper, we have carried out an in-depth analysis of ACOTSP-MF performance for medium and large TSP instances, covering vectorization and scalability issues and showing its main bottlenecks. For medium-size instances, the paper reports speedup factors of 20-500X for the rl11849 instance compared to the base ACOTSP version. ACOTSP-MF is intended and especially adequate for large-size instances. In this context, the paper reports excellent execution time for the Tour Construction phase, with less than 500 ms per iteration for the earring-200k instance. Finally, a study about the solution quality of ACOTSP-MF has been included, showing that ACOTSP-MF paired with local search offers high solution quality (within 2% of the best-known solution).