Marcus Randall

A parallel implementation of ant colony optimization

Ant Colony Optimization is a relatively new class of meta-heuristic search techniques for optimization problems. As it is a population-based technique that examines numerous solution options at each step of the algorithm, there are a variety of parallelization opportunities. In this paper, several parallel decomposition strategies are examined. These techniques are applied to a specific problem, namely the travelling salesman problem, with encouraging speedup and efficiency results.

A Simulated Annealing Approach to Communication Network Design

This paper explores the use of the meta-heuristic search algorithm Simulated Annealing for solving a minimum cost network synthesis problem. This problem is a common one in the design of telecommunication networks. The formulation we use models a number of practical problems with hop-limit, degree and capacity constraints. Emphasis is placed on a new approach that uses a knapsack polytope to select amongst a number of pre-computed traffic routes in order to synthesise the network. The advantage of this approach is that a subset of the best routes can be used instead of the whole set, thereby making the process of designing large networks practicable. Using simulated annealing, we solve moderately large networks (up to 30 nodes) efficiently.

Near Parameter Free Ant Colony Optimisation

Ant colony optimisation, like all other meta-heuristic search processes, requires a set of parameters in order to solve combinatorial problems. These parameters are often tuned by hand by the researcher to a set that seems to work well for the problem under study or a standard set from the literature. However, it is possible to integrate a parameter search process within the running of the meta-heuristic without incurring an undue computational overhead. In this paper, ant colony optimisation is used to evolve suitable parameter values (using its own optimisation processes) while it is solving combinatorial problems. The results reveal for the travelling salesman and quadratic assignment problems that the use of the augmented solver generally performs well against one that uses a standard set of parameter values. This is attributed to the fact that parameter values suitable for the particular problem instance can be automatically derived and varied throughout the search process.

The accumulated experience ant colony for the travelling salesman problem

Ant colony optimization techniques are usually guided by pheromone and heuristic cost information when choosing the next element to add to a solution. However, while an individual element may be attractive, usually its long term consequences are neither known nor considered. For instance, a short link in a traveling salesman problem may be incorporated into an ants solution, yet, as a consequence of this link, the rest of the path may be longer than if another link was chosen. The Accumulated Experience Ant Colony uses the previous experiences of the colony to guide in the choice of elements. This is in addition to the normal pheromone and heuristic costs. Two versions of the algorithm are presented, the original and an improved AEAC that makes greater use of accumulated experience. The results indicate that the original algorithm finds improved solutions on problems with less than 100 cities, while the improved algorithm finds better solutions on larger problems.

Solution approaches for the capacitated single allocation hub location problem using ant colony optimisation

Abstract Hub and spoke type networks are often designed to solve problems that require the transfer of large quantities of commodities. This can be an extremely difficult problem to solve for constructive approaches such as ant colony optimisation due to the multiple optimisation components and the fact that the quadratic nature of the objective function makes it difficult to determine the effect of adding a particular solution component. Additionally, the amount of traffic that can be routed through each hub is constrained and the number of hubs is not known a-priori. Four variations of the ant colony optimisation meta-heuristic that explore different construction modelling choices are developed. The effects of solution component assignment order and the form of local search heuristics are also investigated. The results reveal that each of the approaches can return optimal solution costs in a reasonable amount of computational time. This may be largely attributed to the combination of integer linear preprocessing, powerful multiple neighbourhood local search heuristic and the good starting solutions provided by the ant colonies.

A General Meta-Heuristic Based Solver for Combinatorial Optimisation Problems

In recent years, there have been many studies in which tailored heuristics and meta-heuristics have been applied to specific optimisation problems. These codes can be extremely efficient, but may also lack generality. In contrast, this research focuses on building a general-purpose combinatorial optimisation problem solver using a variety of meta-heuristic algorithms including Simulated Annealing and Tabu Search. The system is novel because it uses a modelling environment in which the solution is stored in dense dynamic list structures, unlike a more conventional sparse vector notation. Because of this, it incorporates a number of neighbourhood search operators that are normally only found in tailored codes and it performs well on a range of problems. The general nature of the system allows a model developer to rapidly prototype different problems. The new solver is applied across a range of traditional combinatorial optimisation problems. The results indicate that the system achieves good performance in terms of solution quality and runtime.

A simulated annealing code for general integer linear programs

This paper explores the use of simulated annealing (SA) for solving arbitrary combinatorialoptimisation problems. It reviews an existing code called GPSIMAN for solving0‐1 problems, and evaluates it against a commercial branch‐and‐bound code, OSL. Theproblems tested include travelling salesman, graph colouring, bin packing, quadratic assignmentand generalised assignment. The paper then describes a technique for representingthese problems using arbitrary integer variables, and shows how a general simulated annealingalgorithm can also be applied. This new code, INTSA, outperforms GPSIMAN andOSL on almost all of the problems tested.

Using ant colony optimisation to construct meander-line RFID antennas

A method increasingly used to uniquely identify objects (be they pieces of luggage, transported goods or inventory items in shops and warehouses), is Radio Frequency IDentification (RFID). One of the most important components of RFID systems is the antenna and its design is critical to the utility of such tracking systems. Design engineers have traditionally constructed small antennas using their knowledge and intuition, as there is no simple analytical solution relating antenna structure to performance. This, however, does not guarantee optimal results, particularly for larger, more complex antennas. The problem is ideally suited to automated methods of optimisation. This chapter presents an overview of the automatic design of antennas using the meta-heuristic known as Ant Colony Optimisation (ACO). Apart from a description of the necessary mechanics ACO needs to effectively solve this problem, a novel local search refinement operator and a multi-objective version of the problem are also described. The latter is used to optimise both antenna efficiency and resonant frequency. Computational results for a range of antenna sizes show that ACO is a very effective design tool for RFID antennas.

Using Ant Colony Optimisation to Improve the Efficiency of Small Meander Line RFID Antennas

Increasing the efficiency of meander line antennas is an important real-world problem within radio frequency identification (RFID). Meta-heuristic search algorithms, such as ant colony optimisation, are very efficient at solving problems that require paths to be constructed. This search technique is adapted to solve the grid based path problem for meander line antennas and incorporates the NEC evaluation suite. The results for grid sizes up to 10 times 10 grid indicates that ant colony optimisation is extremely effective at this real-world problem.

Automated Selection of Appropriate Pheromone Representations in Ant Colony Optimization

Ant colony optimization (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. Critically, the pheromone representation for a particular problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appear multiple times, increasing the effective size of the search space and potentially misleading ants as to the true learned value of those solutions. In this article, we present a novel system for automatically generating appropriate pheromone representations, based on the characteristics of the problem model that ensures unique pheromone representation of solutions. This is the first stage in the development of a generalized ACO system that could be applied to a wide range of problems with little or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm.