Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.

Application of ant colony optimization algorithms for transportation problems using the example of the travelling salesman problem

Abstract: This thesis presents the travelling salesman problem and the application of heuristics in ant colony optimization algorithms. The thesis also discusses the results of an experiment carried out to solve the travelling salesman problem using the ant colony system with different heuristics. An example is focused on heuristics application and comparison.

Obtaining ground states of ising spin glasses via optimizing bonds instead of spins

Abstract: Frustrated Ising spin glasses represent a rich class of challenging optimization problems that share many features with other complex, highly multimodal optimization and combinatorial problems. This paper shows that transforming candidate solutions to an alternative representation that is strongly tied to the energy function simplifies the exploration of the space of potential spin configurations and that it significantly improves performance of evolutionary algorithms with simple variation operators on Ising spin glasses. The proposed techniques are incorporated into the simple genetic algorithm, the univariate marginal distribution algorithm, and the hierarchical Bayesian optimization algorithm.

Analysis and synthesis of enhanced ant colony optimization with the traditional ant colony optimization to solve travelling sales person problem

Abstract: Ant Colony optimization is a heuristic technique which has been applied to a number of combinatorial optimization problem and is based on the foraging behavior of the ants. Travelling Salesperson problem is a combinatorial optimization problem which requires that each city should be visited once. In this research paper we use the K means clustering technique and Enhanced Ant Colony Optimization algorithm to solve the TSP problem. We show a comparison of the traditional approach with the proposed approach. The simulated results show that the proposed algorithm is better compared to the traditional approach.

$$\epsilon$$- DANTE : an ant colony oriented depth search procedure

Abstract: The $$\epsilon$$-Depth ANT Explorer ($$\epsilon$$-DANTE) algorithm applied to a multiple objective optimization problem is presented in this paper. This method is a hybridization of the ant colony optimization algorithm with a depth search procedure, putting together an oriented/limited depth search. A particular design of the pheromone set of rules is suggested for these kinds of optimization problems, which are an adaptation of the single objective case. Six versions with incremental features are presented as an evolutive path, beginning in a single colony approach, where no depth search is applied, to the final $$\epsilon$$-DANTE. Versions are compared among themselves in a set of instances of the multiple objective Traveling Salesman Problem. Finally, our best version of $$\epsilon$$-DANTE is compared with several established heuristics in the field showing some promising results.

Improved ant colony optimization based on particle swarm optimization and its application

Abstract: This article introduces a novel algorithm to solve the large time-consuming problem of the existing improved ant colony optimization(ACO) based on particle swarm optimization(PSO).A new pheromone update method which combines the global asynchronous feature and elitist strategy was used in the algorithm.Moreover,the iteration steps of ACO invoked by PSO were reasonably reduced.The algorithm was applied to solve the path planning problem of landfill inspection robots in Asahikawa,Japan.It is shown that the algorithm has a better performance in search speed compared with other algorithms recently reported.