Standard Ant Colony Optimization Research Articles

Ant Colony Optimization Algorithms with Multiple Simulated Colonies Offer Potential Advantages for Solving the Traveling Salesman Problem and, by Extension, Other Optimization Problems

The traveling salesman problem (TSP) is a classic problem in optimization, frequently used for measuring the performance of optimization algorithms. The goal in solving the TSP is to determine the lowest-cost circuit through a set of cities on a graph. Ant colony optimization (ACO) algorithms, inspired by nature, use simulated ants that modify their environment through laying and removing pheromone, represented by weights on the edges of the graph connecting each city. In this study, a novel algorithm is developed, Multi-Colony System (MCS), which uses multiple colonies of simulated ants in combination to produce superior solutions to the TSP. In comparison with Ant Colony System (ACS), a standard well-performing ACO algorithm, MCS has displayed improved performance, producing tours up to 19.4% shorter than those of ACS in the same amount of time. The performance of MCS in this study presents potential advantages in applications beyond the TSP, including the ability of multiple colonies to both develop a greater number of solutions simultaneously and to more efficiently avoid local maxima in the search space.

ADAPTIVE ANT COLONY OPTIMIZATION BASED GRADIENT FOR EDGE DETECTION

Ant Colony Optimization (ACO) is a nature-inspired optimization algorithm which is motivated by ants foraging behavior. Due to its favorable advantages, ACO has been widely used to solve several NP-hard problems, including edge detection. Since ACO initially distributes ants at random, it may cause imbalance ant distribution which later affects path discovery process. In this paper an adaptive ACO is proposed to optimize edge detection by adaptively distributing ant according to gradient analysis. Ants are adaptively distributed according to gradient ratio of each image regions. Region which has bigger gradient ratio, will have bigger number of ant distribution. Experiments are conducted using images from various datasets. Precision and recall are used to quantitatively evaluate performance of the proposed algorithm. Precision and recall of adaptive ACO reaches 76.98 % and 96.8 %. Whereas highest precision and recall for standard ACO are 69.74 % and 74.85 %. Experimental results show that the adaptive ACO outperforms standard ACO which randomly distributes ants.

A local landscape mapping method for protein structure prediction in the HP model

The hydrophobic-polar (HP) model has been widely studied in the field of protein structure prediction both for theoretical purposes and as a benchmark for new optimization strategies. In this work we present results of the recently proposed Hybrid Monte Carlo Ant Colony Optimization heuristic in the HP model using a fragment assembly-like strategy. Moreover we extend that method introducing a general framework for optimization in the HP model, called Local Landscape Mapping, and we test it using the pull moves set to generate solutions. We describe the heuristic and compare results obtained on well known HP instances in the 3-dimensional cubic lattice to those obtained with standard Ant Colony optimization and Simulated Annealing. Fragment assembly-like tests were performed using a modified objective function to prevent the creation of overlapping walks. Results show that our method performs better than the other heuristics in all benchmark instances when the fragment assembly-like strategy is used while in the case of pull moves-based neighborhood its performance is comparable to that of simulated annealing.

A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling

This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling (IPPS). Generally, the process planning and scheduling are studied separately. Due to the complexity of manufacturing system, IPPS combining both process planning and scheduling can depict the real situation of a manufacturing system. The IPPS is represented on AND/OR graph consisting of nodes, and undirected and directed arcs. The nodes denote operations of jobs, and undirected/directed arcs denote possible visiting path among the nodes. Ant colony goes through the necessary nodes on the graph from the starting node to the end node to obtain the optimal solution with the objective of minimizing makespan. In order to avoid local convergence and low convergence, some improved strategy is incorporated in the standard ant colony optimization algorithm. Extensive computational experiments are carried out to study the influence of various parameters on the system performance.

Modified ant colony optimization for topology optimization of geometrically nonlinear structures

Standard Ant Colony Optimization (ACO) algorithm cannot provide an optimal topology for geometrically nonlinear structural problems. The reason is that the tangential stiffness matrix of low-density elements may become zero or negative, which leads to serious numerical instability problems, or the standard ACO algorithm cannot search the really efficient elements because of improper element search method. A modified ant colony optimization (MACO) algorithm is suggested to improve computational efficiency and suitability of standard ACO algorithm in topology optimization for these problems. A continuous variable, called the “Element Contribution Significance (ECS),” is introduced, which serves to replace the positions of ants in the standard ACO algorithm, and assess the importance of each element in the optimization process. The optimized topologies based on MACO algorithm are compared to those of the other topology methods. From the comparison, it is verified that MACO algorithm can successfully be applied to topology optimization for geometrically nonlinear structures, as well as provide stable and robust topologies.

An improved ant colony optimization approach for optimization of process planning.

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPCs). A pheromone updating strategy proposed in this paper is incorporated in the standard ACO, which includes Global Update Rule and Local Update Rule. A simple method by controlling the repeated number of the same process plans is designed to avoid the local convergence. A case has been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach.

Ant Colony Routing algorithm for freeway networks

Dynamic traffic routing refers to the process of (re)directing vehicles at junctions in a traffic network according to the evolving traffic conditions. The traffic management center can determine desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex non-linear, non-convex optimization problem. To solve this problem, Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO is implemented online to determine the control signal – i.e., the splitting rates at each node. However, using standard ACO for traffic routing is characterized by four main disadvantages: 1. traffic flows for different origins and destinations cannot be distinguished; 2. all ants may converge to one route, causing congestion; 3. constraints cannot be taken into account; and 4. neither can dynamic link costs. These problems are addressed by adopting a novel ACO algorithm with stench pheromone and with colored ants, called Ant Colony Routing (ACR). Using the stench pheromone, the ACR algorithm can distribute the vehicles over the traffic network with less or no traffic congestion, as well as reduce the number of vehicles near some sensitive zones, such as hospitals and schools. With colored ants, the traffic flows for multiple origins and destinations can be represented. The proposed approach is also implemented in a simulation-based case study in the Walcheren area, the Netherlands, illustrating the effectiveness of the approach.

A Hybrid Monte Carlo Ant Colony Optimization Approach for Protein Structure Prediction in the HP Model

The hydrophobic-polar (HP) model has been widely studied in the field of protein structure prediction (PSP) both for theoretical purposes and as a benchmark for new optimization strategies. In this work we introduce a new heuristics based on Ant Colony Optimization (ACO) and Markov Chain Monte Carlo (MCMC) that we called Hybrid Monte Carlo Ant Colony Optimization (HMCACO). We describe this method and compare results obtained on well known HP instances in the 3 dimensional cubic lattice to those obtained with standard ACO and Simulated Annealing (SA). All methods were implemented using an unconstrained neighborhood and a modified objective function to prevent the creation of overlapping walks. Results show that our methods perform better than the other heuristics in all benchmark instances.

Distribution Systems Reconfiguration Using Ant Colony Optimization and Harmony Search Algorithms

One objective of the feeder reconfiguration problem in distribution systems is to minimize the distribution network total power loss for a specific load. For this problem, mathematical modeling is a non-linear mixed integer problem that is generally hard to solve. This article proposes two heuristic algorithms inspired from natural phenomena to solve the network reconfiguration problem: (1) “real ant-behavior-inspired” ant colony optimization implemented in the hyper cube framework and (2) the “musician behavior-inspired” harmony search algorithm. The optimization problem is formulated taking into account the operational constraints of distribution systems. A 32-bus system and a 118-bus distribution were selected for optimizing the configuration to minimize the losses. The results of reconfiguration using the proposed algorithms show that both of them yield the optimum configuration with minimum power loss for each case study; however, the harmony search required shorter simulation time but more practice of the iterative process than the hyper cube–ant colony optimization. Implementing the ant colony optimization in the hyper cube framework resulted in a more robust and easier handling of pheromone trails than the standard ant colony optimization.

Torch Relay Route Optimization by Using the Artificial Intelligence

Ceremonial fire is an important symbol for an athletic game. Through the torch relay process, the atmosphere of peace and union is delivered and people are encouraged to participate in the athletic games together. Based on the data obtained from the global position system (GPS), in the study, various artificial intelligence approaches were used to solve the torch relay routing problem. The objective of the torch relay routing problem was to minimize the total distance of torch relay route. By reasonably combining genetic algorithms (GA) and particle swarm optimization (PSO), we developed a fast and easily implemented hybrid algorithm (HA) for solving the considered problem. The effectiveness and efficiency of the proposed HA were demonstrated and compared with those of standard ant colony optimization (ACO), PSO and GA by numerical results of the simulated instance with 10 spots and the real torch relay routing problems of National Intercollegiate Athletic Games in 2010. Numerical results indicate that the total distance of torch relay routes by HA was 335 km, which was 11.68% shorter than the original routes adopted by National Intercollegiate Athletic Games in 2010. It implies that the proposed HA can use the GPS information to schedule the torch relay routes, and it can reduce the cost of reconnoitering and management in an athletic game. Therefore, the proposed HA approach is an effective approach, and it can improve the efficiency for an athletic game.

Two-stage updating pheromone for invariant ant colony optimization algorithm

Ant colony optimization (ACO) is a metaheuristic approach for combinatorial optimization problems. With the introduction of hypercube framework, invariance property of ACO algorithms draws more attention. In this paper, we propose a novel two-stage updating pheromone for invariant ant colony optimization (TSIACO) algorithm. Compared with standard ACO algorithms, TSIACO algorithm uses solution order other than solution itself as independent variable for quality function. In addition, the pheromone trail is updated with two stages: in one stage, the first r iterative optimal solutions are employed to enhance search capability, and in another stage, only optimal solution is used to accelerate the speed of convergence. And besides, the pheromone value is limited to an interval. We prove that TSIACO not only has the property of linear transformational invariance but also has translational invariance. We also prove that the pheromone trail can limit to the interval (0, 1]. Computational results on the traveling salesman problem show the effectiveness of TSIACO algorithm.

Automated Multigravity Assist Trajectory Planning with a Modified Ant Colony Algorithm

The paper presents an approach to transcribe a multigravity assist trajectory design problem into an integrated planning and scheduling problem. A modified Ant Colony Optimization (ACO) algorithm is then used to generate optimal plans corresponding to optimal sequences of gravity assists and deep space manoeuvers to reach a given destination. The modified Ant Colony Algorithm is based on a hybridization between standard ACO paradigms and a tabu-based heuristic. The scheduling algorithm is integrated into the trajectory model to provide a fast time-allocation of the events along the trajectory. The approach demonstrated to be very effective on a number of real trajectory design problems.

An improved ant colony optimization for the design of planar steel frames

This paper presents an improved ant colony optimization (IACO) for the design of planar steel frames. The algorithm consists of two phases: the global search phase and the local search phase. In the global search phase, IACO employs a sub-optimization mechanism (SOM) based on the principles of finite element methods to reduce the search space, the size of the trail matrix, the number of analyses and consequently the optimization time. In the local search phase, a final optimum design is obtained considering W-shaped sections from the available steel sections specified by the American Institute of Steel Construction (AISC) in the neighborhood of the result gained in the previous phase. The proposed method is tested on several frames from the literature. In order to verify the suitability of the present method, the results of optimum design for several planar steel frames are obtained by the present study and compared to those of the standard ant colony optimization, the genetic algorithm and the harmony search algorithm. The results indicate that the computational time required for the improved ant colony optimization is considerably reduced.

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.

Novel ant colony optimization approach to optimal control

PurposeThe purpose of this paper is to propose a novel ant colony optimization (ACO) approach to optimal control. The standard ACO algorithms have proven to be very powerful optimization metaheuristic for combinatorial optimization problems. They have been demonstrated to work well when applied to various nondeterministic polynomial‐complete problems, such as the travelling salesman problem. In this paper, ACO is reformulated as a model‐free learning algorithm and its properties are discussed.Design/methodology/approachFirst, it is described how quantizing the state space of a dynamic system introduces stochasticity in the state transitions and transforms the optimal control problem into a stochastic combinatorial optimization problem, motivating the ACO approach. The algorithm is presented and is applied to the time‐optimal swing‐up and stabilization of an underactuated pendulum. In particular, the effect of different numbers of ants on the performance of the algorithm is studied.FindingsThe simulations show that the algorithm finds good control policies reasonably fast. An increasing number of ants results in increasingly better policies. The simulations also show that although the policy converges, the ants keep on exploring the state space thereby capable of adapting to variations in the system dynamics.Research limitations/implicationsThis paper introduces a novel ACO approach to optimal control and as such marks the starting point for more research of its properties. In particular, quantization issues must be studied in relation to the performance of the algorithm.Originality/valueThe paper presented is original as it presents the first application of ACO to optimal control problems.

Fuzzy ant colony optimization for estimating chlorophyll concentration profile in offshore sea water

The determination of some inherent optical properties can be addressed by estimating the ocean chlorophyll concentration, if bio-optical models can be applied – such as for the offshore sea water. This inverse problem can be formulated as an optimization problem and iteratively solved, where the radiative transfer equation is the direct model. An objective function is given by the square difference between computed and in situ experimental radiances at every iteration. In the standard ant colony optimization (ACO), the pheromone is reinforced only on the best ant of the population. The fuzzy strategy consists in including additional pheromone quantity on the best ant, but a small pheromone quantity is also spread over the other solutions close to the best one. Test results show that the fuzzy-ACO produces better inverse solutions.