Pheromone Update Strategy Research Articles

A Chaotic Ant Colony System for Path Planning of Mobile Robot

Aiming at the path planning problem of mobile robot, a chaotic ant colony system was presented. The idea of the algorithm was that first generated chaotic sequences as the initial pheromone matrix and then ant colony traversed grids environment once, pheromone on the path was updated, as the completeness of the pheromone initialization. Pheromone update strategy adopt self-adaptive chaotic disturbance to avoid the search being trapped in local optimum. Grids environment simulated the robot workspace. Through the process of self-organization and chaos, the ant colony found the optimal path in the robot’s static environment. Simulation results show that chaotic ant colony system not only enhances the global search capability, but also more effective than the traditional ACS, moreover, it’s a novel approach to the robot path planning.

Robot Global Path Planning Based on an Improved Ant Colony Algorithm

Aiming at the disadvantages of the basic ant colony algorithm, this paper proposes an improved ant colony algorithm for robot global path planning. First, adjust the pheromone evaporation rate dynamically to enhance the global search ability and convergence speed, and then modify the heuristic function to improve the state transition probabilities in order to find the optimal solution as quickly as possible; and finally change the pheromone update strategy to avoid premature by strengthening pheromone on the optimal path and limiting pheromone level. Simulation results verify the effectiveness of the improved algorithm.

Study on Thinking Evolution based ant Colony Algorithm in Typical Production Scheduling Application

Aiming at solving the NP-hard workshop production scheduling problems, proposed one kind based on mind evolutionary algorithm. The algorithm in the traditional ant colony algorithm is established, and the combination of evolutionary thought and local optimization idea overcomes the basic ant colony algorithm is easy to fall into local optimal defects, the improved state transition rules, defining a pheromone range, improve the pheromone update strategy, and the increase of neighborhood search. Experimental results show that, for a typical production scheduling problems, based on mind evolutionary ant colony algorithm can obtain the optimal solution in theory, optimal solution, the solution and average three indicators are better than the basic ant colony algorithm, showed good performance.

Pheromone mark ant colony optimization with a hybrid node-based pheromone update strategy

An improved ant colony optimization (ACO) algorithm called pheromone mark ACO abbreviated PM-ACO is proposed for the non-ergodic optimal problems. PM-ACO associates the pheromone to nodes, and has a pheromone trace of scatter points which are referred to as pheromone marks. PM-ACO has a node-based pheromone update strategy, which includes two other rules except a best-so-far tour rule. One is called r-best-node update rule which updates the pheromones of the best-ranked nodes, which are selected by counting the nodes’ passed ants in each iteration. The other one is called relevant-node depositing rule which updates the pheromones of the k-nearest-neighbor (KNN) nodes of a best-ranked node. Experimental results show that PM-ACO has a pheromone integration effect of some neighbor arcs on their central node, and it can result in instability. The improved PM-ACO has a good performance when applied in the shortest path problem.

Optimized Ant Colony Algorithm by Local Pheromone Update

Ant colony algorithm, a heuristic simulated algorithm, provides better solutions for non-convex, non-linear and discontinuous optimization problems. For ant colony algorithm, it is frequently to be trapped into local optimum, which might lead to stagnation. This article presents the city-select strategy, local pheromone update strategy, optimum solution prediction strategy and local optimization strategy to optimize ant colony algorithm, provides ant colony algorithm based on local pheromone update, also inspects and verifies it by TSP problems. The results of the numeric experiments suggest that on some TSP problems, the optimized ant colony algorithm acquired more satisfied solutions than all that we have already known. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.4211

Improved ant algorithms for software testing cases generation.

Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations.

An Improved Model of Ant Colony Optimization Using a Novel Pheromone Update Strategy

The paper introduces a novel pheromone update strategy to improve the functionality of ant colony optimization algorithms. This modification tries to extend the search area by an optimistic reinforcement strategy in which not only the most desirable sub-solution is reinforced in each step, but some of the other partial solutions with acceptable levels of optimality are also favored. therefore, it improves the desire for the other potential solutions to be selected by the following artificial ants towards a more exhaustive algorithm by increasing the overall exploration. The modifications can be adopted in all ant-based optimization algorithms; however, this paper focuses on two static problems of travelling salesman problem and classification rule mining. To work on these challenging problems we considered two ACO algorithms of ACS (Ant Colony System) and AntMiner 3.0 and modified their pheromone update strategy. As shown by simulation experiments, the novel pheromone update method can improve the behavior of both algorithms regarding almost all the performance evaluation metrics. key words: Ant colony optimization, ant colony system, ant-miner, classification rule mining, learning automata, reinforcement learning

Exploring Linear Homeomorphic Clusters on Nonlinear Manifold

This paper proposed a new clustering alogorithm based on ant colony optimization and Grassmann manifold for exploring linear homeomorphic clusters on non-linear dataset manifold. The minimum processed units of algorithm were first lifted to suppress the influence of noise, and then the similarity of unit was measured according to Grassmann manifold and a geodesic-like distance was designed for ensuring the connectivity of cluster. To improve the quality of cluster generated by ant colony clustering, the direction of minimum surface complexity was defined and introduced into the pheromone update strategy as heuristic information. Experiments and analysis on several datasets have shown the successful performance on linear homeomorphic clustering compared to traditional clustering algorithms.

Some Research on Non-Identical Parallel Machine Scheduling Problem - An Example to Plane De-Icing Operation

Under the context of deicing in the airport, and in order to solve a kind of Non-identical Parallel Multi-machine Schedule Problem for minimizing the number of passengers delayed, an ant colony algorithm was proposed. Considering the characteristic of scheduling model and its constraints, an improved pheromone update strategy was developed by deploying different parameters. Simulation was made for proving the effectiveness of the algorithm, and the sensitivity analysis was conducted. Besides, the improved ant colony algorithm was compared further with FIFO algorithm used in realistic backgrounds. In all, the result indicated that the improving ant colony algorithm was valid, and it can fit for large-scale non-identical parallel Multi-machine scheduling problem.

Sensor Compensation Based on Adaptive Ant Colony Neural Networks

In order to improve work stability and measurement accuracy of drilling inclinometer, and overcome the poor stability of Elman networks and lower compensation precision of genetic Elman neural networks, we combined ant colony algorithm and neural networks, using the Adaptive Ant Colony Algorithm that its pheromone evaporation factorand pheromone update strategy adjust adaptively to optimize Elman neural network weights and thresholds, and applied it to drilling inclinometer sensor compensation. Simulation results show that the compensation effect of adaptive ant colony Elman neural networks is better than that of Elman networks and genetic Elman networks, the compensation accuracy is 10-8.

An Improved Ant Colony Algorithm for the Vehicle Routing Problem in Time-Dependent Networks

Vehicle routing is an important combinatorial optimization problem. In real transport networks,the travel speed and travel time of roads have large time-variability and randomness. The study of vehicle routing problem in time-dependent network has even more practical value than static network VRP problem. This paper combines the features of time-dependent networks and gives the mathematical models of the time-dependent vehicle routing problem. On this basis, the traditional ant colony optimization algorithm is improved. A new path transfer strategy of ants and new dynamic pheromone update strategy applicable to time-dependent network are proposed. Based on these strategies, the improved ant colony algorithm is given for solving the vehicle routing problem in time-dependent networks. The simulation results show that the algorithm can effectively solve the vehicle routing problem in time-dependent network and has better computational efficiency and convergence speed.

Ant estimator with application to target tracking

Standard ant colony optimization (ACO) algorithm is usually utilized to solve various hard combinatorial optimization problems. In this paper, however, the idea of the generic ACO is extended to the scope of recursive parameter estimation, i.e., ant estimator is first proposed and investigated to track target of interest. In the proposed ant estimator framework, some basic properties of particle filter (PF) and ant colony optimization (ACO) are inherited, and the “fittest variables” are determined by ants with probability decisions. In addition, the pheromone update strategy is also well defined in order to guide more ants to better solutions. Finally, two improved versions of the original ant estimator are investigated and applied to some benchmark target tracking problems. Numerical experiments demonstrate that these proposed ant estimators perform well, and moreover, each could deal with maneuvering target tracking problem without any auxiliary technique.

An Improved Ant Colony Algorithm for the Shortest Path Problem in Time-Dependent Networks

Research of the shortest path problem in time-dependent networks has important practical value. An improved pheromone update strategy suitable for time-dependent networks was proposed. Under this strategy, the residual pheromone of each road can accurately reflect the change of weighted value of each road. An improved selection strategy between adjacent cities was used to compute the cities' transfer probabilities, as a result, the amount of calculation is greatly reduced. To avoid the algorithm converging to the local optimal solution, the ant colony algorithm was combined with genetic algorithm. In this way, the solutions after each traversal were used as the initial species to carry out single-point crossover. An improved ant colony algorithm for the shortest path problem in time-dependent networks based on these improved strategies was presented. The simulation results show that the improved algorithm has greater probability to get the global optimal solution, and the convergence rate of algorithm is better than traditional ant colony algorithm.

Superior/Inferior Segment-Discriminated Ant System for combinatorial optimization problems

The Ant Colony Optimization method is a heuristic algorithm for solving various optimization problems, particularly the combinatorial optimization problems. Traditional ant-optimization methods might encounter search stagnation owing to a biased pheromone map that is dominated by local optimal trails. To overcome this drawback and lower the number of solution constructions for finding the optima, this paper presents an improving ant-optimization system, the Superior/Inferior Segment-Discriminated Ant System (SDAS). This system proposes a segment-based pheromone update strategy to deposit pheromone on superior segments and withdraw pheromone from inferior ones. The method uses the control-chart technique to define superior and inferior limits to partition the constructed solutions into superior, inferior, and ordinary solutions. Inferior and superior segments are then extracted from the superior and inferior solutions by stochastic set operations. Since the pheromone map is not easily dominated by any local optimal trail, the solution search is more efficient and effective. Several benchmarks from the TSP-LIB and OR-LIB were used as sample problems to test the proposed system against other ant-optimization systems, including the AS, ACS, AS_rank, AS_elite, and MMAS. Numerical results indicated that the SDAS obtains solutions that are similar to or better than others. Maturity index for the pheromone map was discussed and experimental results showed that the proposed method was able to prolong the time for the map to maturity to avoid earlier search stagnation.