Extremal optimization

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

Performance comparison of sequential and parallel execution of the Ant Colony Optimization algorithm for solving the traveling salesman problem

Abstract: Ant Colony Optimization (ACO) is a metaheuristic algorithm which uses ideas from nature to find solutions to instances of the Travelling Salesman Problem (TSP) and other combinatorial optimisation problems. ACO is taken as one of the high performance computing methods for TSP. In this paper, the impact of parallelizing an ant colony optimization (ACO) algorithm for the traveling salesman problem in increasing performances is studied, using the task parallel library. One of the main reasons for parallelizing this alghoritm is to reduce the time needed to find a solution while the quality of solution is the same as in the algorithm which is not parallelized.

Multiprocessor scheduling by generalized extremal optimization

Abstract: We propose a solution of the multiprocessor scheduling problem based on applying a relatively new metaheuristic technique, called Generalized Extremal Optimization (GEO). GEO is inspired by a simple coevolutionary model known as the Bak---Sneppen model. The model describes an ecosystem consisting of N species. Evolution in this model is driven by a process in which the weakest species in the ecosystem, together with its nearest neighbors, is always forced to mutate. This process shows the characteristics of a phenomenon called punctuated equilibrium, which is observed in evolutionary biology. We interpret the multiprocessor scheduling problem in terms of the Bak---Sneppen model and apply the GEO algorithm to solve the problem. We show that the proposed optimization technique is simple and yet outperforms genetic algorithm-based and swarm algorithm-based approaches to the multiprocessor scheduling problem.

Mutated Ant Colony Algorithm for Assignment Problem

Abstract: Assignment problem, a kind of combinatorial optimization problem, has significant importance for real life. Ant system algorithm is a kind of evolutionary algorithms, which is efficient in solving combinatorial optimization problem. In this paper, we established the mathematical model of assignment problem as well as solved this problem by mutated ant colony algorithm. Experiments show that, by using this algorithm, the best solution can be found rapidly.

Quantum-inspired genetic algorithms applied to ordering combinatorial optimization problems

Abstract: This article proposes a new algorithm based on evolutionary computation and quantum computing. It attempts to resolve ordering combinatorial optimization problems, the most well known of which is the traveling salesman problem (TSP). Classic and quantum-inspired genetic algorithms based on binary representations have been previously used to solve combinatorial optimization problems. However, for ordering combinatorial optimization problems, order-based genetic algorithms are more adequate than those with binary representation, since a specialized crossover process can be employed in order to always generate feasible solutions. Traditional order-based genetic algorithms have already been applied to ordering combinatorial optimization problems but few quantum-inspired genetic algorithms have been proposed. The algorithm presented in this paper contributes to the quantum-inspired genetic approach to solve ordering combinatorial optimization problems. The performance of the proposed algorithm is compared with one order-based genetic algorithm using uniform crossover. In all cases considered, the results obtained by applying the proposed algorithm to the TSP were better, both in terms of processing times and in terms of the quality of the solutions obtained, than those obtained with order-based genetic algorithms.

Genetic Algorithm, Extremal Optimization, and Particle Swarm Optimization Applied to the Discrete Network Configuration Problem.

Abstract: Genetic Algorithm, Extremal Optimization, and Particle Swarm Optimization are applied to the discrete (integer-based) communications network configuration problem. In this preliminary study, each heuristic search scheme is used to determine the optimal (or near optimal) set of communications equipment components needed to satisfy user-specified radio subscriber and wire subscriber requirements. The network configuration problem is based on the U.S. Army’s Mobile Subscriber Equipment communications networking system. Although MSE is no longer a major asset in the U.S. Army’s communications inventory, it continues to provide an excellent platform for configuration optimization due to its non-permutation yet discrete nature. The comparison of the heuristics includes their overall reliability as well as the number of fitness evaluations needed to converge on the optimal solution. General results indicate that all approaches achieve relatively high reliability using some novel operators and adaptations, but the GA does so using considerably fewer fitness evaluations.