Ant Colony Algorithm Algorithm Research Articles

Route optimization for hazardous materials transportation vehicles based on BFA-ACO under carbon tax policy

Excessive emission of greenhouse gases leads to the increasing greenhouse effect, adversely affecting the global climate. Carbon dioxide is the dominant part of greenhouse gases; reducing its emission is the most important way to solve the climate problem. Aiming at the characteristics of the dangerous goods transportation market and the development of the carbon tax policy, the carbon tax cost and the cost of dangerous goods transportation are introduced, and the characteristics of the dangerous goods transportation and the road traffic network and the road traffic in each period are fully analyzed. The optimization model of the path of the hazardous goods vehicles is established with the optimization objective of the lowest total cost. Then, by analyzing the advantages and disadvantages of bacterial foraging algorithm (BFA) and ant colony algorithm (ACO), the hybrid BFA-ACO algorithm is established by combining the two, and the replication and convergence operations of bacterial foraging algorithm are introduced into the ACO algorithm to improve the convergence speed and global convergence ability of the algorithm. The hybrid algorithm is then used to optimize and solve the path optimization model of hazardous materials vehicles and compared with the classical algorithms Genetic Algorithm (GA) and ACO for solving the path of dangerous materials vehicles. A comparison of the optimization results reveals that optimizing the model by bacterial foraging-ACO algorithm is better than optimizing the model by a single algorithm.

Omnidirectional AGV Path Planning Based on Improved Genetic Algorithm

To address the issues with traditional genetic algorithm (GA) path planning, which often results in redundant path nodes and local optima, we propose an Improved Genetic Algorithm that incorporates an ant colony algorithm (ACO). Firstly, a new population initialization method is proposed. This method adopts a higher-quality random point generation strategy to generate random points centrally near the start and end of connecting lines. It combines the improved ACO algorithm to connect these random points quickly, thus greatly improving the quality of the initial population. Secondly, path smoothness constraints are proposed in the adaptive function. These constraints reduce the large-angle turns and non-essential turns, improving the smoothness of the generated path. The algorithm integrates the roulette and tournament methods in the selection stage to enhance the searching ability and prevent premature convergence. Additionally, the crossover stage introduces the edit distance and a two-layer crossover operation based on it to avoid ineffective crossover and improve convergence speed. In the mutation stage, we propose a new mutation method and introduce a three-stage mutation operation based on the idea of simulated annealing. This makes the mutation operation more effective and efficient. The three-stage mutation operation ensures that the mutated paths also have high weights, increases the diversity of the population, and avoids local optimality. Additionally, we added a deletion operation to eliminate redundant nodes in the paths and shorten them. The simulation software and experimental platform of ROS (Robot Operating System) demonstrate that the improved algorithm has better path search quality and faster convergence speed. This effectively prevents the algorithm from maturing prematurely and proves its effectiveness in solving the path planning problem of AGV (automated guided vehicle).

Influence of Using ACO Algorithm in STATCOM Performance

Flexible alternating current transmission systems (FACTS) are technologies that rely on the use of high-power electronic equipment and advanced control methods to increase the ability of electrical transmission lines to transmit the largest amount of electrical power.Static_ synchronous _compensator (STATCOM) is one of FACTS_ family It has a rapid ability to supply or absorb reactive power from the system, traditional controller in STATCOM is (PID) or (PI). The controller is used to control the currents and voltages. In (STATCOM) there are two control loop, the inner loop which is represented by controlling the source current, and the outer loop which is used to control the voltage of the DC-link energy –storage- capacitor (vdc). Many algorithms are used in optimization to predict a specific behavior and to find the best solution for it. Two optimization methods were used Ant Colony Algorithm (ACO) and Genetic Algorithm (GA) to adjust the parameters of the outer PI controller. This unit is responsible for adjusting (vdc). The capacitor voltage is converted by the STATCOM inverter into a three-phase voltage, which is used to supply the load with required reactive power. Through the simulation and control process, the results demonstrated the effectiveness of the compensator in supplying the reactive power to the linear or non-linear loads, The results also showed that using the ACO technique that the integrated time absolute error ( ITAE) was reduced which gives more precise in adjusting the PI control parameters Which is achieved best Vd.c response ,compared with the GA and ziklur-nichlos method , by measuring the response of the lowest settling time (TS) and minimal peak overshoot (P-ov) .

A Data-Driven Prediction Method for Proton Exchange Membrane Fuel Cell Degradation

This paper proposes a long short-term memory (LSTM) network to predict the power degradation of proton exchange membrane fuel cells (PEMFCs), and in order to promote the performance of the LSTM network, the ant colony algorithm (ACO) is introduced to optimize the hyperparameters of the LSTM network. First, the degradation mechanism of PEMFCs is analyzed. Second, the ACO algorithm is used to set the learning rate and dropout probability of the LSTM network combined with partial aging data, which can show the characteristics of the dataset. After that, the aging prediction model is built by using the LSTM and ACO (ACO-LSTM) method. Moreover, the convergence of the method is verified with previous studies. Finally, the fuel cell aging data provided by the Xiangyang Da’an Automotive Testing Center are used for verification. The results show that, compared with the traditional LSTM network, ACO-LSTM can predict the aging process of PEMFCs more accurately, and its prediction accuracy is improved by about 35%, especially when the training data are less. At the same time, the performance of the model trained by ACO-LSTM is also excellent under other operating conditions of the same fuel cell, and it has strong versatility.

Urban traffic flow management on large scale using an improved ACO for a road transportation system

PurposeWith the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.Design/methodology/approachAn improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.FindingsExperimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.Originality/valueThe proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

Improved ant colony algorithm for path planning of mobile robots based on compound prediction mechanism

For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slow optimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and “reward or punishment” update mechanism is used to updates the global pheromone concentration directionally, which increases the pheromone concentration of excellent path nodes and the heuristic effect; Finally, a prediction-backward mechanism to deal with the “deadlock” problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved; the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved.

Optimization of Tool Path Length on Three-Dimensional Drilling Application Using Ant Colony Algorithm

The lower machining time is important characteristic in the drilling machining process. Drilling process costs will increase if the machining time is high. Therefore, the main objective of this research is to develop Ant Colony Algorithm (ACO) to reduce the machining time by obtain the optimal tool path length. By using this algorithm, it can minimize the tool path length and significantly decreasing the machining time of drilling process. Simulating in 3-dimensional drilling on ACO has been constructed to minimize the shortest path of the drilling process. There are two type of workpiece has been used, which is simple block with 10 holes and complex block design that has 154 holes. ACO algorithm has been developed in Matlab R2017b to determine the optimal parameters of ACO of tool path length in drilling. Besides, simulation also has been done to investigate the effect of ACO parameter which is weight of pheromone (α), weight of trail (β), evaporation coefficient (e), and number of iterations. As a result, by define the parameter of iteration number at 900, the optimum parameter of weight of pheromone (α) is 5, weight of trail (β) is 4 and evaporation coefficient (e) is 0.4. Based on these parameters, the minimal tool path length obtain for simple and complex model are 286.965 mm and 6770.9860 mm respectively. Then, the result of tool path length of ACO simulation has been compared with the Mastercam outcome. ACO achieves a total tool path length of 286.965 mm while Mastercam achieved 569.878 mm for simple block design. Meanwhile, for complex block design, ACO produces a total tool path length of 6770.9860 mm while Mastercam has generate 55828.9050 mm of tool path length. By comparing these two approaches, ACO and Mastercam, ACO has that the short total tool path length by 49.64% on simple block design and 87.87% for complex block design.

An Efficient VLSI Test Data Compression Scheme for Circular Scan Architecture Based on Modified Ant Colony Meta-heuristic

A new test data compression scheme for circular scan architecture is proposed in this paper. A stochastic heuristic based bio-inspired optimization approach namely ant colony algorithm (ACO) is applied after modification and customization to improve compression efficiency. In circular scan architecture, test data compression is achieved by updating the conflicting bits between the most recently captured response and test vector to be applied next. The quantity of conflicting bits also manifests the Hamming distance between the most recently captured response and the next test vector. A significant reduction in test data volume and test application time is achieved by reducing Hamming distance. The problem is renovated as a traveling salesman problem (TSP). The test vectors are presumed as cities and Hamming distance between a pair of test vectors is treated as intercity distance and a modified ACO algorithm in combination with mutation operator is applied here to resolve this combinatorial optimization problem. The experimental results confirm the efficacy of this approach. An average improvement of 6.36% in compression ratio and 4.77% in test application time is achieved. The exhibited technique sustains an optimal level of performance without incurring any extra DFT (design for testability) cost.

Ant Colony Optimization With an Improved Pheromone Model for Solving MTSP With Capacity and Time Window Constraint

The optimization of logistics distribution can be defined as the multiple traveling salesman problem (MTSP). The purpose of existing heuristic algorithms, such as Genetic Algorithm (GA), Ant Colony Algorithm (ACO), etc., is to find the optimal path in a short time. However, two important factors of logistics distribution optimization, including work time window and the carrying capacity of the vehicle in distribution system, have been ignored. In this paper, we consider the influences of time limitation of modern commercial logistics and carrying capacity of the vehicle on the logistics optimization, and then propose a MTSP with constraints of time window and capacity of each salesman. We design a novel hybrid algorithm by combining the minimum spanning 1-tree with ACO to find the optimal solution. In addition, we improve the pheromone update rules to increase the search efficiency of ACO algorithm. The experiments show that the novel hybrid algorithm achieves a shorter path than the other algorithms.

A Shared Parking Space Optimization Model to Alleviate China’s Parking Problem Considering Travelers’ Tiered Credit Risk

ABSTRACT A shared parking space optimization (SPSO) model is established to alleviate China’s parking problem considering travelers’ tiered credit risk. The SPSO model has the following characteristics: travelers’ possibilities for default are measured by credit value; travelers with different credit tiers are classified and managed; the success of the shared parking space will be affected by the traveler’s credit value. In order to solve the SPSO model, we transform it into a vertex coloring problem of an undirected graph and then modify the ant colony algorithm (ACA) by improving in two aspects of pheromone concentration and termination rule of the traditional ACA algorithm. Numerical experiments of different sizes are performed to evaluate the validity and efficiency of the proposed model. This study may cater for the deficiencies in the research of credit risk in the implementation of shared parking mechanism to a certain extent.

Dynamic ant colony algorithm based on cw saving Value

As a swarm intelligence optimization algorithm, ant colony algorithm (ACO) has a good application in combinatorial optimization problems, in which traveling salesman problem (TSP) is an important application of ACO algorithm. It shows the powerful ability of ant colony algorithm to find short paths through graphics. However, there are obvious defects in the ant colony algorithm. When the scale of the ant colony is large, the convergence time of the algorithm becomes longer and the local optimal state is easy to fall into. In this paper, a dynamic pheromone ant colony optimization algorithm based on CW saving algorithm is proposed. Initially, a general path range is found by CW saving value algorithm, and the pheromone matrix can be reasonably configured, so that the ant colony algorithm can quickly get a better solution in the initial optimization. At the same time, the optimization scheme can be adjusted in real time according to the situation of path optimization. Large ant colony searches for other paths. Combined with 3-opt local search algorithm, the ant colony can find the optimal path more quickly. The experimental results show that the improved ant colony algorithm has better convergence speed and solution quality than other ant colony algorithms.

Energy Efficient Routing Protocol for Maximizing the Lifetime in Wsns Using Ant Colony Algorithm and Artificial Immune System

Energy limitations have become fundamental challenge for designing wireless sensor networks. Network lifetime represent the most important and interested metric. Several attempts have been made for efficient utilization of energy in routing techniques. This paper proposes an energy efficient routing technique for maximizing the networks lifetime called swarm intelligence routing. This is achieved by using ant colony algorithm (ACO) and artificial immune system (AIS). AIS is used for solving packet LOOP problem and to control route direction. While ACO algorithm is used for determining optimum route for sending data packets. The proposed routing technique seeks for determining the optimum route from nodes towards base station so that energy exhaustion is balanced and lifetime is maximized. Proposed routing technique is compared with Dijkstra routing method. Results show significant increase in network lifetime of about 1.2567.

Multiprocessor Scheduling Problem Based on Ant Colony Optimization Algorithm

Multiprocessor Scheduling is a problem of discrete optimization. The strong optimization capacity of Ant Colony Algorithm (ACA) on solving the discrete optimization suggests the feasibility to solve the multiprocessor scheduling problem using the ACA algorithm. Contrary to the shortcomings of traditional ant colony algorithm, this is easy to fall into the local convergence, In order to improve the calculation accuracy. combined of multiprocessor scheduling problem, propose a more efficient ant colony optimization algorithm and present a new state transition rule, at the same time use dynamically update the strategy of ant pheromones and the optimal parameter selection. it can find a better scheduling strategy in a short time, and it has excellent global optimization properties, The simulation results show that the credibility and the validity of the improved ant colony optimization algorithm for multiprocessor scheduling problem.

An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

This paper presents a modified Ant Colony Algorithm(ACA) called route-update ant colony algorithm(RUACA). The research attention is focused on improving the computational efficiency in the TSP problem. A new impact factor is introduced and proved to be effective for reducing the convergence time in the RUACA performance. In order to assess the RUACA performance, a simply supported data set of cities, which was taken as the source data in previous research using traditional ACA and genetic algorithm(GA), is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented RUACA has successfully solved the TSP problem. The results of the proposed algorithm are found to be satisfactory.

A modified ant colony algorithm for the stacking sequence optimisation of a rectangular laminate

This paper presents a modified Ant Colony Algorithm (ACA) called multi-city-layer ant colony algorithm (MCLACA). The research attention is focused on improving the computational efficiency in the stacking sequence optimisation of a laminated composite plate for maximum buckling load. A new operator, the so-called two point interchange, is introduced and proved to be effective for reducing the convergence time and enhancing the robustness in the MCLACA performance. The laminate optimisation is subject to balanced and symmetric layup with ply contiguous and strength constraints. In order to assess the MCLACA performance, a simply supported rectangular laminate plate, which was taken as numerical example in previous research using traditional ACA and genetic algorithm (GA) is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented MCLACA has better performance in terms of computational efficiency and robustness. To demonstrate the applicability of the MCLACA to a general case, an additional example of laminate optimisation has been taken with more design variables and five different boundary conditions by finite element analysis.

The ant colony algorithm for feature selection in high-dimension gene expression data for disease classification

The use of gene expression data to diagnose complex diseases represents an exciting area of medicine; however, such data sets are often noisy, requiring the selection of feature subsets to obtain maximum classification accuracy. Due to the high dimensions of many expression data sets, filter-based methods are commonly used, but often yield inconsistent results. Optimization algorithms can outperform filter methods, but often require preselection of features to achieve good results. To address the problems of many commonly used feature selection methods, the ant colony algorithm (ACA) is proposed for use on data sets with large numbers of features. The ACA is an optimization algorithm capable of incorporating prior information, allowing it to search the sample space more efficiently than other optimization methods. When applied to several high-dimensional data sets, the ACA was able to identify small subsets of highly predictive and biologically relevant genes without the need for extensive preselection of features. Using the selected genes to train a latent variable model yielded substantial increases in prediction accuracy when compared to several rank-based methods and results obtained in previous studies. The superiority of the ACA algorithm was validated through simulation.