Population-based Ant Colony Optimization Research Articles

A Modified Ant Colony Optimization Algorithm with Pheromone Mutations for Dynamic Travelling Salesman Problem

Ant Colony Optimization (ACO) is an algorithm based on Meta-heuristics and swarm intelligence, taking inspiration from the exploring behavior of social insects like ants. Some very challenging optimization problems, like the vehicle routing problem, have been solved by Ant colony optimization. But, the Standard ACO algorithm has problems like trapping in local optimum. This paper discusses an improved version of the ACO algorithm, known as Modified ACO (M-ACO). The main motivation of the proposed algorithm is to use pheromone mutation techniques, such as swap and random, in dynamic environments, to overcome the problems of standard ACO and solve the Travelling Salesman Problem, which is a well-known NP-hard problem. Mutation allows better searching and exploring in other areas, for better results. Also, the exploitation is increased that uses local information to search local regions more intensively for finding minimum distance and increasing the rate of convergence. Evaporation-based Ant colony optimization (E-ACO) framework and population-based ant colony optimization (P-ACO) framework are the two groups into which the algorithms under study are separated. Both frameworks are evaluated on dynamic environments produced using TSPLIB base instances and updated for pheromone values using mutation technique. Experiments and Statistical tests were conducted on the TSPLIB datasets such as pr439 and lin318, in dynamic environments and on comparing ACO with M-ACO and P-ACO, better results were obtained for M-ACO and P-ACO. Metrics like offline have been considered to measure algorithm performance and behavior.

A computational study on ant colony optimization for the traveling salesman problem with dynamic demands

Ant colony optimization (ACO) algorithms have originally been designed for static optimization problems, where the input data is known in advance and is not subject to changes over time. Later, the long term memory of ACO proved effective for reoptimization over environment changes when extended to deal with dynamic combinatorial optimization problems (DCOPs). Among the major proposals of this kind, several adaptations of ACO procedures to improve information reuse can be identified, as well as a population-based ACO algorithm (P-ACO) specifically designed for DCOPs. Indeed, P-ACO drew the attention of the research community due to its ability to faster process pheromone information, but the few works assessing the effectiveness of the adapted ACO procedures and also P-ACO are not enough to reach more general conclusions on the current state-of-the-art in ACO for dynamic optimization. In this work, we conduct an extensive experimental campaign to evaluate the most common ACO procedure adaptations identified in the literature, using as underlying algorithms the state-of-the-art in ACO for static optimization (MAX-MIN Ant System, MMAS) and the most relevant ACO algorithm proposed for dynamic optimization (P-ACO). A variant of the traveling salesman problem with dynamic demands (DTSP) is used as test benchmark, similarly to most investigations on ACO for combinatorial optimization. Besides the carefully designed experimental setup we adopt, our work represents a significant contribution for, at least, three reasons. First, our work is the first to acknowledge that DCOPs require custom-configured parameter settings, and also the first to use automatic configuration tools for this task. Concretely, we show how the hypervolume indicator can be used to evaluate and configure the anytime behavior of algorithms for DCOPs. Second, we directly compare MMAS and P-ACO, isolating local search as an experimental factor. While P-ACO proves indeed effective in the absence of local search, MMAS is able to consistently outperform it when local search is adopted. Finally, we conduct an experimental investigation on the DCOP-specific components proposed for ACO, once again isolating local search. Results show that those components contribute very little to performance when algorithms are allowed to use local search, but are remarkably effective in its absence. In fact, coupled with DCOP components, MMAS outperforms P-ACO for a large part of our experimental setup.

On Randomness and Structure in Euclidean TSP Instances: A Study With Heuristic Methods

Prediction of the quality of the result provided by a specific solving method is an important factor when choosing how to solve a given problem. The more accurate the prediction, the more appropriate the decision on what to choose when several solving applications are available. In this article, we study the impact of the structure of a Traveling Salesman Problem instance on the quality of the solution when using two representative heuristics: the population-based Ant Colony Optimization (ACO) and the local search Lin-Kernighan (LK) algorithm. The quality of the result for a solving method is measured by the computation accuracy, which is expressed using the percent error between its solution and the optimum one. We classify the instances in structured, semi-structured, and unstructured and perform a between classes and inside-classes analysis. All the structured instances were solved to optimality by the ACO implementation, which was not the case for the LK application. On small random instances, the ACO implementation used in this paper also optimally found the solutions. We show that the quality of the results on semi-structured and unstructured instances can be predicted using some instance parameters when using the ACO implementation. Using the same parameters, the accuracy of the solutions provided by the Lin-Kernighan application cannot be predicted. We also propose several new structured, clustered, and unstructured 2D Euclidean Traveling Salesman Problem instances that can be used by the research community for further investigations.

Hybridization of population-based ant colony optimization via data mining

Hybridization of population-based ant colony optimization via data mining

Omicron ACO. A New Ant Colony Optimization Algorithm


 
 
 Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). This paper proposes the Omicron ACO (OA), a novel population-based ACO alternative originally designed as an analytical tool. To experimentally prove OA advantages, this work compares the behavior between the OA and the MMAS as a function of time in two well-known TSP problems. A simple study of the behavior of OA as a function of its parameters shows its robustness.
 
 

Adjustability of a discrete particle swarm optimization for the dynamic TSP

This paper presents a detailed study of the discrete particle swarm optimization algorithm (DPSO) applied to solve the dynamic traveling salesman problem which has many practical applications in planning, logistics and chip manufacturing. The dynamic version is especially important in practical applications in which new circumstances, e.g., a traffic jam or a machine failure, could force changes to the problem specification. The DPSO algorithm was enriched with a pheromone memory which is used to guide the search process similarly to the ant colony optimization algorithm. The paper extends our previous work on the DPSO algorithm in various ways. Firstly, the performance of the algorithm is thoroughly tested on a set of newly generated DTSP instances which differ in the number and the size of the changes. Secondly, the impact of the pheromone memory on the convergence of the DPSO is investigated and compared with the version without a pheromone memory. Moreover, the results are compared with two ant colony optimization algorithms, namely the mathcal {MAX}–mathcal {MIN} ant system (MMAS) and the population-based ant colony optimization (PACO). The results show that the DPSO is able to find high-quality solutions to the DTSP and its performance is competitive with the performance of the MMAS and the PACO algorithms. Moreover, the pheromone memory has a positive impact on the convergence of the algorithm, especially in the face of dynamic changes to the problem’s definition.

Simple Probabilistic Population-Based Optimization

A generic scheme is proposed for designing and classifying simple probabilistic population-based optimization (SPPBO) algorithms that use principles from population-based ant colony optimization (PACO) and simplified swarm optimization (SSO) for solving combinatorial optimization problems. The scheme, called SPPBO, identifies different types of populations (or archives) and their influence on the construction of new solutions. The scheme is used to show how SSO can be adapted for solving combinatorial optimization problems and how it is related to PACO. Moreover, several new variants and combinations of these two metaheuristics are generated with the proposed scheme. An experimental study is done to evaluate and compare the influence of different population types on the optimization behavior of SPPBO algorithms, when applied to the traveling salesperson problem and the quadratic assignment problem.

Refined ranking relations for selection of solutions in multi objective metaheuristics

Abstract Two methods for ranking of solutions of multi objective optimization problems are proposed in this paper. The methods can be used, e.g. by metaheuristics to select good solutions from a set of non dominated solutions. They are suitable for population based metaheuristics to limit the size of the population. It is shown theoretically that the ranking methods possess some interesting properties for such applications. In particular, it is shown that both methods form a total preorder and are both refinements of the Pareto dominance relation. An experimental investigation for a multi objective flow shop problem shows that the use of the new ranking methods in a Population-based Ant Colony Optimization algorithm and in a genetic algorithm leads to good results when compared to other methods.

A memetic ant colony optimization algorithm for the dynamic travelling salesman problem

Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms.

FPGA implementation of population-based ant colony optimization

We present a hardware implementation of population-based ant colony optimization (P-ACO) on field-programmable gate arrays (FPGAs). The ant colony optimization meta-heuristic is adopted from the natural foraging behavior of real ants and has been used to find good solutions to a wide spectrum of combinatorial optimization problems. We describe the P-ACO algorithm and present a circuit architecture that facilitates efficient FPGA implementations. The proposed design shows modest space requirements but leads to a significant reduction in runtime over software-based solutions. Several modifications and extensions of the basic algorithm are also presented, including the approximation of the heuristic function by a small, dynamically changing set of favorable decisions.

FPGA implementation of population-based ant colony optimization

We present a hardware implementation of population-based ant colony optimization (P-ACO) on field-programmable gate arrays (FPGAs). The ant colony optimization meta-heuristic is adopted from the natural foraging behavior of real ants and has been used to find good solutions to a wide spectrum of combinatorial optimization problems. We describe the P-ACO algorithm and present a circuit architecture that facilitates efficient FPGA implementations. The proposed design shows modest space requirements but leads to a significant reduction in runtime over software-based solutions. Several modifications and extensions of the basic algorithm are also presented, including the approximation of the heuristic function by a small, dynamically changing set of favorable decisions.