Studies on Extremal Optimization and Its Applications in Solving RealWorld Optimization Problems

Recently, a local-search heuristic algorithm called extremal optimization (EO) has been proposed and successfully applied in some NP-hard combinatorial optimization problems. This paper presents an investigation on the fundamentals of EO with its applications in discrete and numerical optimization problems. The EO was originally developed from the fundamental of statistic physics. However, in this study we also explore the mechanism of EO from all three aspects: statistical physics, biological evolution or co-evolution and ecosystem. Furthermore, we introduce our contributions to the applications of EO in solving traveling salesman problem (TSP) and production scheduling, and multi-objective optimization problems with novel perspective in discrete and continuous search spaces, respectively. The simulation results demonstrate the competitive performance with EO optimization solutions due to its extremal dynamics mechanism

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