Showing papers on "Extremal optimization published in 1991"
01 Jan 1991
TL;DR: An application of the proposed methodology to the classical travelling salesman problem shows that the system can rapidly provide very good, if not optimal, solutions.
Abstract: A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and greedy heuristic helps the procedure to find acceptable solutions in the early stages of the search process. An application of the proposed methodology to the classical travelling salesman problem shows that the system can rapidly provide very good, if not optimal, solutions. We report on many simulation results and discuss the working of the algorithm. Some hints about how this approach can be applied to a variety of optimization problems are also given.
376 citations
02 Sep 1991
TL;DR: This paper looks into an implementation of tabu search on dedicated hardware and shows a potential for improvements of two orders of magnitude in the time taken to perform a fixed number of iterations for the traveling salesman problem (TSP).
Abstract: The tabu search is a new promising optimization heuristic used for obtaining near-optimum solutions of combinatorial optimization problems. This paper looks into an implementation of tabu search on dedicated hardware and shows a potential for improvements of two orders of magnitude in the time taken to perform a fixed number of iterations for the traveling salesman problem (TSP). >
7 citations
28 Apr 1991
TL;DR: The proposed algorithm, PBDA', is a massively parallel search algorithm based on the idea of staged search, and solution quality is scalable with the number of processors, and its execution time is directly proportional to the depth of search.
Abstract: Most admissible search algorithms fail to solve reallife problems because of their exponential time and storage requirements. Therefore, to quickljy obtain near-optimal solutions, the use of approximute algorithms and inadmissible heuristics are of practical interest. The use of parallel and distributed ahgorithms [l, 6, 8, 111 further reduces search complexity. I n this paper we present empirical results on a massively parallel search algorithm using a Connection .Machine CM-2. Our algorithm, PBDA', is based on the idea of staged search [9, lo]. Its execution time is directly proportional t o the depth of search, and solution quality is scalable with the number of processors. W e tested it on the 1Bpuzzle problem using both admissible and inadmissible heuristics. The best results gave an average relative error of 1.66% and 66% optimal solutions.
1 citations
01 Jan 1991
TL;DR: Using Genetic Algorithms to Solve Combinatorial Optimization Problems and how they can be used to improve the quality of human-computer interaction.
Abstract: OF THE THESIS Using Genetic Algorithms to Solve Combinatorial Optimization Problems
1 citations