Yong-Zai Lu

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

Multiobjective optimization using population-based extremal optimization

In recent years, a general-purpose local-search heuristic method called Extremal Optimization (EO) has been successfully applied in some NP-hard combinatorial optimization problems. In this paper, we present a novel Pareto-based algorithm, which can be regarded as an extension of EO, to solve multiobjective optimization problems. The proposed method, called Multiobjective Population-based Extremal Optimization (MOPEO), is validated by using five benchmark functions and metrics taken from the standard literature on multiobjective evolutionary optimization. The experimental results demonstrate that MOPEO is competitive with the state-of-the-art multiobjective evolutionary algorithms. Thus MOPEO can be considered as a viable alternative to solve multiobjective optimization problems.