Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
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06 Jul 2013
TL;DR: This work introduces Group-Based Ant Colony Optimization which uses a parallel construction principle on group-structured solution encodings and compares the parallel construction method with the classical sequential one.
Abstract: We introduce Group-Based Ant Colony Optimization which uses a parallel construction principle on group-structured solution encodings. We compare the parallel construction method with the classical sequential one. In this context we also perform simulation experiments for the Vehicle Routing Problem with Time Windows using the Solomon [8] and the Homberger & Gehring [5] instances.
3 citations
21 Aug 2006
TL;DR: The conjecture of a globally convex structure for the solution space of the bi-criteria TSP is confirmed and may support successful applications using state of the art metaheuristics based on Ant Colony or Evolutionary Computation.
Abstract: This work studies the solution space topology of the Traveling Salesman Problem or TSP, as a bi-objective optimization problem The concepts of category and range of a solution are introduced for the first time in this analysis These concepts relate each solution of a population to a Pareto set, presenting a more rigorous theoretical framework than previous works studying global convexity for the multi-objective TSP The conjecture of a globally convex structure for the solution space of the bi-criteria TSP is confirmed with the results presented in this work This may support successful applications using state of the art metaheuristics based on Ant Colony or Evolutionary Computation
3 citations
12 Jun 2011
TL;DR: The approach introduces generalized extremal optimization (GEO), a relatively new heuristic algorithm derived from co-evolution to solve the identification problem of identifying the topology and parameters in Hindmarsh-Rose-neuron networks.
Abstract: In the last few years bio-inspired neural networks have interested an increasing number of researchers. In this paper, a novel approach is proposed to solve the problem of identifying the topology and parameters in Hindmarsh-Rose-neuron networks. The approach introduces generalized extremal optimization (GEO), a relatively new heuristic algorithm derived from co-evolution to solve the identification problem. Simulation results show that the proposed approach compares favorably with other heuristic algorithms based methods in existing literatures with smaller estimation errors. And it presents satisfying results even with noisy data.
3 citations
3 citations
TL;DR: In this article, a new hybrid optimization algorithm which combines the strong global search ability of artificial immune system (AIS) with a strong local search ability (LSA) of extremal optimization (EO) algorithm is proposed.
Abstract: The permutation flowshop scheduling problem (PFSP) is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algorithm which combines the strong global search ability of artificial immune system (AIS) with a strong local search ability of extremal optimization (EO) algorithm. The proposed algorithm is applied to a set of benchmark problems with a makespan criterion. Performance of the algorithm is evaluated. Comparison results indicate that this new method is an effective and competitive approach to the PFSP.
3 citations