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Optimization with extremal dynamics
- Physical Review Letters
"... We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problems. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. Extremal optimization successive ..."
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Cited by 53 (3 self)
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We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problems. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. Extremal optimization successively replaces extremely undesirable variables of a single sub-optimal solution with new, random ones. Large fluctuations ensue, that efficiently explore many local optima. With only one adjustable parameter, the heuristic’s performance has proven competitive with more elaborate methods, especially near phase transitions which are believed to coincide with the hardest instances. We use extremal optimization to elucidate the phase transition in the 3-coloring problem, and we provide independent confirmation of previously reported extrapolations for the ground-state energy of±J spin glasses in d = 3 and 4. PACS number(s): 02.60.Pn, 05.65.+b, 75.10.Nr, 64.60.Cn. Many natural systems have, without any centralized organizing facility, developed into complex structures that optimize their use of resources in sophisticated ways [1]. Biological evolution has formed efficient
A.: Extremal optimization: an evolutionary local-search algorithm
- In: Proceedings of the 8th INFORMS Computing Society Conference (2003). ArXiv:cs/0209030
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Solving the Maximum Satisfiability Problem Using an Evolutionary Local Search Algorithm
, 2004
"... Abstract: The MAXimum propositional SATisfiability problem (MAXSAT) is a well known NP-hard optimization problem with many theoretical and practical applications in artificial intelligence and mathematical logic. Heuristic local search algorithms are widely recognized as the most effective approache ..."
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Abstract: The MAXimum propositional SATisfiability problem (MAXSAT) is a well known NP-hard optimization problem with many theoretical and practical applications in artificial intelligence and mathematical logic. Heuristic local search algorithms are widely recognized as the most effective approaches used to solve them. However, their performance depends both on their complexity and their tuning parameters which are controlled experimentally and remain a difficult task. Extremal Optimization (EO) is one of the simplest heuristic methods with only one free parameter, which has proved competitive with the more elaborate general-purpose method on graph partitioning and coloring. It is inspired by the dynamics of physical systems with emergent complexity and their ability to self-organize to reach an optimal adaptation state. In this paper, we propose an extremal optimization procedure for MAXSAT and consider its effectiveness by computational experiments on a benchmark of random instances. Comparative tests showed that this procedure improves significantly previous results obtained on the same benchmark with other modern local search methods like WSAT, simulated annealing and Tabu Search (TS).
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"... Abstract—In recent years, inspired by the emerging Web services standard and peer-to-peer technology, a new federated service providing (FSP) system paradigm has attracted increasing research interests. Many existing systems have either explicitly or implicitly followed this paradigm. Instead of exc ..."
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Abstract—In recent years, inspired by the emerging Web services standard and peer-to-peer technology, a new federated service providing (FSP) system paradigm has attracted increasing research interests. Many existing systems have either explicitly or implicitly followed this paradigm. Instead of exchanging files, peers in FSP systems share their computation resources in order to offer domainspecific services. In this paper, we focused on the coordination problem of how to self-organize the service group structures in response to the varying service demand. We presented our solution in the form of a coordination mechanism, which includes a labormarket model, a recruiting protocol, and a policy-driven decision architecture. Peers make their service providing decisions based on their local policies, which can be added, removed, or modified by users. A general methodology is introduced in this paper to facilitate policy design. Specifically, a heuristic inspired by the Extremal Optimization technique is utilized to handle potential inconsistencies among policies. A stimulus-response mechanism was further applied to make the decision process adjustable. Experiments under five application scenarios verified our ideas and demonstrated the effectiveness of our coordination mechanism. Index Terms—Federated service providing system, P2P system, coordination, decision policy. Ç
Construct Pairwise Test Suites Based on the Bak-Sneppen Model of Biological Evolution
"... Abstract—Pairwise testing, which requires that every combination of valid values of each pair of system factors be covered by at lease one test case, plays an important role in software testing since many faults are caused by unexpected 2-way interactions among system factors. Although meta-heuristi ..."
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Abstract—Pairwise testing, which requires that every combination of valid values of each pair of system factors be covered by at lease one test case, plays an important role in software testing since many faults are caused by unexpected 2-way interactions among system factors. Although meta-heuristic strategies like simulated annealing can generally discover smaller pairwise test suite, they may cost more time to perform search, compared with greedy algorithms. We propose a new method, improved Extremal Optimization (EO) based on the Bak-Sneppen (BS) model of biological evolution, for constructing pairwise test suites and define fitness function according to the requirement of improved EO. Experimental results show that improved EO gives similar size of resulting pairwise test suite and yields an 85 % reduction in solution time over SA.
