Initial Pheromone Research Articles

Band Selection of Hyperspectral Chlorophyll-A Concentration Inversion Based on Parallel Ant Colony Algorithm

In chlorophyll-A concentration inversion which based on hyperspectral remote sensing image, must use ground synchronous spectrum measurement data to find the best three band combination by iterative-method, we proposed the problem to analysis the existing band selection methods of chlorophyll-A concentration inversion and put forward a band selection method of the hyperspectral chlorophyll-A concentration inversion three-band model which based on parallel ant colony algorithm. The method is based on analysis of the optical properties and three band model theory, determines the interval of the sensitive band from hundreds of bands，then takes band reflectance of the sampling points and Correlation coefficient of sample points of chlorophyll-A concentration as the initial pheromone list, and updates distance parameter list constantly to let the parallel work ants find the best band combinations. Experimental results show that the method can select the optimal three-band fast and efficient by the remote sensing data and samples of chlorophyll-A concentration data, and the steps of chlorophyll-A concentration inversion can be simplified greatly.

A Novel Collaborative Optimization Algorithm for Solving TSP

For the premature convergence and initial pheromone distribution problem of the basic ACO algorithm, PSO algorithm and chaos optimizing strategy are introduced into the ant colony algorithm in order to propose a novel collaborative optimization (CPACO) algorithm based on the collaboration theory. The first, the CPACO algorithm divides the ant colony into several subgroups, and the parameters of the subgroup are regarded as the particles. Then these advantages of PSO algorithm and chaos optimization strategy are fully utilized to optimize these parameters of the ACO algorithm in order to obtain the optimal values of these parameters. And the pheromone exchange operation is introduced into the subgroup. In order to validate the performance of the CPACO algorithm, the TSP problems are selected in here. The simulation results show that the proposed CPACO algorithm has better optimization performance than the traditional ACO algorithm.

Pheromone trail initialization with local optimal solutions in ant colony optimization

This paper presents a method to improve the search rate of Max-Min Ant System for the traveling salesman problem. The proposed method gives deviations from the initial pheromone trails by using a set of local optimal solutions calculated in advance.

An Improved Intelligent Ant Colony Algorithm for the Reliability Optimization Problem in Cyber-Physical Systems

In this paper the torsion bar optimization problem of reliability is considered. Since this problem is a difficult optimization problem, an improved intelligent ant colony algorithm is proposed to solve the problem. This algorithm comprises five stages. First stage is the initialization of pheromone and the sensor node configuration. The second stage is to select the next sensor node. The third stage is to update the pheromone of the sensor node path. The fourth stage is to acquire the best path by computing the shortest distance. The last stage is to output the global optimal solution. To evaluate performance of the proposed algorithm, it is compared with the ant colony optimization algorithm and the genetic algorithm. The experimental results show that the proposed algorithm performs better than them.

Adaptive ARA Algorithm (AARA): Proposed Modifications and Experimental Results

Since ARA is a reactive protocol, that is why it is used in such situations where mobility of nodes are higher. In this section, we have proposed the modifications to the algorithm by which the potential of ARA will increase in high mobility scenarios. Pheromone updates play a critical role in the performance of the ant algorithm. In ARA algorithm, initial pheromone value is computed by number hops during the route discovery. This method may not be suitable when nodes are mobile. Pheromone equations are classified in different categories. Two of them are the Classic pheromone filter, where route quality is not taken into consideration, for example the original ARA pheromone equation and the Gamma pheromone filter, which takes time and route quality into consideration.

Multiple depots vehicle routing based on the ant colony with the genetic algorithm

Purpose: the distribution routing plans of multi-depots vehicle scheduling problem will increase exponentially along with the adding of customers. So, it becomes an important studying trend to solve the vehicle scheduling problem with heuristic algorithm. On the basis of building the model of multi-depots vehicle scheduling problem, in order to improve the efficiency of the multiple depots vehicle routing , the paper puts forward a fusion algorithm on multiple depots vehicle routing based on the ant colony algorithm with genetic algorithm. Design/methodology/approach: to achieve this objective, the genetic algorithm optimizes the parameters of the ant colony algorithm. The fusion algorithm on multiple depots vehicle based on the ant colony algorithm with genetic algorithm is proposed. Findings: simulation experiment indicates that the result of the fusion algorithm is more excellent than the other algorithm, and the improved algorithm has better convergence effective and global ability. Research limitations/implications: in this research, there are some assumption that might affect the accuracy of the model such as the pheromone volatile factor, heuristic factor in each period, and the selected multiple depots. These assumptions can be relaxed in future work. Originality/value: In this research, a new method for the multiple depots vehicle routing is proposed. The fusion algorithm eliminate the influence of the selected parameter by optimizing the heuristic factor, evaporation factor, initial pheromone distribute, and have the strong global searching ability. The Ant Colony algorithm imports cross operator and mutation operator for operating the first best solution and the second best solution in every iteration, and reserves the best solution. The cross and mutation operator extend the solution space and improve the convergence effective and the global ability. This research shows that considering both the ant colony and genetic algorithm together can improve the efficiency multiple depots vehicle routing.

ADAPTIVE EDGE DETECTION USING ADJUSTED ANT COLONY OPTIMIZATION

Abstract. Edges contain important information in image and edge detection can be considered a low level process in image processing. Among different methods developed for this purpose traditional methods are simple and rather efficient. In Swarm Intelligent methods developed in last decade, ACO is more capable in this process. This paper uses traditional edge detection operators such as Sobel and Canny as input to ACO and turns overall process adaptive to application. Magnitude matrix or edge image can be used for initial pheromone and ant distribution. Image size reduction is proposed as an efficient smoothing method. A few parameters such as area and diameter of travelled path by ants are converted into rules in pheromone update process. All rules are normalized and final value is acquired by averaging.

Research of Hybrid Mobile Agent Routing in Wireless Sensor Network

The mobile agent route is essentially a multi-constraint optimization problem, Genetic Algorithms has fast random global search ability, but the feedback information of the system does not use and has the problem of low efficiency of finding exact solutions, propose a genetic hybrid ant colony algorithm for WSN mobile agent route. Use of the fast random global search capabilities of genetic algorithm to find better solutions, then the better solution replaced by the initial pheromone of the ant colony algorithm, finally use the advantages of convergence speed of ant colony algorithm to find the global optimal solution for mobile agent route. Simulation results show that the algorithm can find optimal mobile agent route in a relatively short time, relative to other routing algorithms, reducing network latency and average energy consumption, improving the speed and efficiency of data transfer.

Ants find the shortest path: a mathematical proof

In the most basic application of Ant Colony Optimization (ACO), a set of artificial ants find the shortest path between a source and a destination. Ants deposit pheromone on paths they take, preferring paths that have more pheromone on them. Since shorter paths are traversed faster, more pheromone accumulates on them in a given time, attracting more ants and leading to reinforcement of the pheromone trail on shorter paths. This is a positive feedback process that can also cause trails to persist on longer paths, even when a shorter path becomes available. To counteract this persistence on a longer path, ACO algorithms employ remedial measures, such as using negative feedback in the form of uniform evaporation on all paths. Obtaining high performance in ACO algorithms typically requires fine tuning several parameters that govern pheromone deposition and removal. This paper proposes a new ACO algorithm, called EigenAnt, for finding the shortest path between a source and a destination, based on selective pheromone removal that occurs only on the path that is actually chosen for each trip. We prove that the shortest path is the only stable equilibrium for EigenAnt, which means that it is maintained for arbitrary initial pheromone concentrations on paths, and even when path lengths change with time. The EigenAnt algorithm uses only two parameters and does not require them to be finely tuned. Simulations that illustrate these properties are provided.

Improved Ant Colony Optimization Algorithm and Its Application on Path Planning of Mobile Robot

This paper uses the grid method with coding tactic based on effective vertexes of barriers (EVB-CT-GM) as the method of environment modeling and ant colony optimization algorithm with two-way parallel searching strategy (TWPSS-ACOA) is adopted to accelerate searching speed. In view of that the TWPSS-ACOA has the defects of losing some feasible paths and even optimal paths because of its ants meeting judgment strategy (AMJS), so a new AMJS is proposed. Then a new method to rationally distribute initial pheromone is given to accelerate convergence speed of initial stages of ACO algorithm. Later, in order to avoid running into local optima and to speed up optimization process, a new path selecting method and a new global pheromone updating technique are put forward. Finally simulation researches of path planning of mobile robot based on improved TWPSS-ACOA are made under different two-dimension environments and simulation results show the improved algorithm can find safe paths at higher convergence speed even in complex environment.

Ant colony optimisation-based multiobjective frame design under seismic conditions

Ordinary moment-resisting frames are studied for multiobjective design under seismic loading, using the ant colony optimisation method. The objective function contains minimisation of the total weight as well as the corresponding seismic base shear. The modal spectral method is applied for the analysis of ordinary moment-resisting frames. The structure is under strength and serviceability constraints, enforced by penalty functions. All design requirements correspond to uniform building code (UBC) and American Institute of Steel Construction allowable stress design codes of practice. The frame sections are selected from the standard set of American Institute of Steel Construction W-shapes. A new type of initial pheromone depositing is introduced in the ant colony optimisation. Also, to enable improvements on the results, a novel local search is developed and applied to moment frames. For the purpose of comparison, a genetic algorithm is applied to the same problems.

Studies on the development of a mating disruption system to control the tomato leafminer, Tuta absoluta Povolny (Lepidoptera: Gelechiidae)

The tomato leafminer (Tuta absoluta Povolny) has rapidly colonised the whole Mediterranean and South-Atlantic coasts of Spain, and it has become a key problem in both outdoor and greenhouse crops. New control methods compatible with biological control are required, and mating disruption appears to be a perfect method in current agriculture, as it is an environmentally friendly and residue-free technique. IPM packages tested have included the use of pheromones to detect populations, but there has not been much previous research on mating disruption of T. absoluta. In this work, pheromone doses varying from 10 to 40 g ha(-1), emitted at a constant rate over 4 months, were tested in greenhouses with different levels of containment in order to evaluate the efficacy of mating disruption on T. absoluta. Trials on containment level revealed that the flight of T. absoluta was satisfactorily disrupted with an initial pheromone dose of 30 g ha(-1), and levels of damage did not significantly differ from those in reference plots with insecticide treatments. Later efficacy trials confirmed previous experiences, and release studies showed that control of damage and flight disruption were taking place when releasing at least 85 mg pheromone per ha per day. Effective control using pheromone application against T. absoluta can be achieved, in greenhouses with high containment levels, for 4 months, with initial doses of 30 g ha(-1). Further research must be conducted in order to evaluate the prospect of outdoor application of mating disruption systems.

A Novel Hybrid Stochastic Searching Algorithm Based on ACO and PSO: A Case Study of LDR Optimal Design

With the rapid development of electronic commerce, the logistics distribution system brings to the widespread attention. And the logistics distribution routing (LDR) optimization is playing the very important role as one of core technologies in the logistics distribution system. This paper proposed a novel hybrid stochastic searching algorithm to solve the LDR optimization design problem, the algorithm unified the ant colony optimization (ACO) and particle swarm optimization (PSO) algorithm effectively, which uses the randomness, the rapidity and the global characteristics of PSO to obtain the initial pheromone distribution firstly, then uses the ACO advantages of the concurrency, the positive feedback and the higher solving precision to find the exact solution. The results of simulation experiment show that the hybrid algorithm has superior global seeking optimization ability and the rapid convergence rate. The method is quick and effective to optimize the LDR problem, and can obtain the optimal solution or approximate optimal solution.

Ant colony optimization technique for the sequence-dependent flowshop scheduling problem

In the real world, production scheduling systems, usually optimal job scheduling, requires an explicit consideration of sequence-dependent setup times. One of the most important scheduling criteria in practical systems is makespan. In this paper, the author presents an ant colony optimization (ACO) algorithm for the sequence-dependent permutation flowshop scheduling problem. The proposed ACO algorithm benefits from a new approach for computing the initial pheromone values and a local search. The proposed algorithm is tested on randomly generated problem instances and results indicate that it is very competitive with the existing best metaheuristics.

Ant colony based rule generation for reusable software component retrieval

Storage and representation of reusable software components in software repositories to facilitate convenient identification and retrieval has been always a concern for software reuse researchers. This paper discusses and demonstrated an ant colony algorithm based technique that generates rules to store and then identify the component from software repository for possible reuse. Proposed technique help user in organization and storage of components in repository and later can help in identifying most appropriate component for given context. In first stage while searching it makes use of keywords, their synonyms and their inter-relationships. Then it makes use of ant colony optimization; initial pheromone of one is assigned to all domain representative terms of components. By updating pheromone for participating terms and non-participating terms iteratively and by calculating the quality of each rule generated, it leads to quality rules to represent and retrieve the reusable components

Trail formation in ants. A generalized Polya urn process

Faced with a choice of paths, an ant chooses a path with a higher concentration of pheromone. Subsequently, it drops pheromone on the path chosen. The reinforcement of the pheromone-following behavior favors the selection of an initially discovered path as the preferred path. This may cause a long path to emerge as the preferred path, were it discovered earlier than a shorter path. However, the shortness of the shorter path offsets some of the pheromone accumulated on the initially discovered longer path. In this paper, we model the trail formation behavior as a generalized Polya urn process. For k equal length paths, we give the distribution of pheromone at any time and highlight its sole dependence on the initial pheromone concentrations on paths. Additionally, we propose a method to incorporate the lengths of paths in the urn process and derive how the pheromone distribution alters on its inclusion. Analytically, we show that it is possible, under certain conditions, to reverse the initial bias that may be present in favor of paths that were discovered prior to the discovery of more efficient (shorter) paths. This addresses the Plasticity-Stability dilemma for ants, by laying out the conditions under which the system will remain stable or become plastic and change the path. Finally, we validate our analysis and results using simulations.

First Order Deceptive Problem of ACO and Its Performance Analysis

Ant colony optimization(ACO), which is one of the intelligential optimization algorithm, has been widely used to solve combinational optimization problems. Deceptive problems have been considered difficult for ant colony optimization. It was believed that ACO will fail to converge to global optima of deceptive problems. This paper proves that the first order deceptive problem of ant colony algorithm satisfies value convergence under certain initial pheromone distribution, but does not satisfy solution convergence. We also present a first attempt towards the value-convergence time complexity analysis of ACO on the first-order deceptive systems taking the n -bit trap problem as the test instance. We prove that time complexity of MMAS, which is an ACO with limitations of the pheromone on each edge, on n -bit trap problem is O( n 2 m. log n ), here n is the size of the problem and m is the number of artificial ants. Our experimental results confirm the correctness of our analysis.

Robust and Efficient Covering of Unknown Continuous Domains with Simple, Ant-Like A(ge)nts

We propose a new `Mark-Ant-Walk' algorithm for robust and efficient covering of continuous domains by ant-like robots with very limited capabilities. The robots can mark places visited with pheromone marks and sense the level of the pheromone in their local neighborhood. In the case of multiple robots, these pheromone marks can be sensed by all robots and provide the only way of (indirect) communication between the robots. The robots are assumed to be memoryless, and to have no information besides that mentioned above. Despite the robots' simplicity we show that they are able, by running a very simple rule of behavior, to ensure efficient covering of arbitrary connected domains, including non-planar and multidimensional domains. The novelty of our algorithm lies in the fact that, unlike previously proposed methods, our algorithm works on continuous domains without relying on some induced underlying graph effectively reducing the problem to a discrete case of graph covering. In addition, we demonstrate various benefits of our algorithm such as ability to cope with arbitrary initial pheromone profile and a bounded constant time between two successive visits of the robot at the same location, making it suitable for patrolling. Finally, we provide a new theoretical bound on covering time of a wide family of such mark and walk algorithms.

Improved Clonal Selection Algorithm Combined with Ant Colony Optimization

Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.

An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups

In many real-world production systems, it requires an explicit consideration of sequence-dependent setup times when scheduling jobs. As for the scheduling criterion, the weighted tardiness is always regarded as one of the most important criteria in practical systems. While the importance of the weighted tardiness problem with sequence-dependent setup times has been recognized, the problem has received little attention in the scheduling literature. In this paper, we present an ant colony optimization (ACO) algorithm for such a problem in a single-machine environment. The proposed ACO algorithm has several features, including introducing a new parameter for the initial pheromone trail and adjusting the timing of applying local search, among others. The proposed algorithm is experimented on the benchmark problem instances and shows its advantage over existing algorithms. As a further investigation, the algorithm is applied to the unweighted version of the problem. Experimental results show that it is very competitive with the existing best-performing algorithms.