Population-Based Extremal Optimization with Adaptive Lévy Mutation for Constrained Optimization

Authors:
Min-Rong Chen;Yong-Zai Lu;Genke Yang
Affiliations:
Dept. of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;Dept. of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;Dept. of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Venue:
Computational Intelligence and Security
Year:
2007

Citing 6
Cited 0

Nature's way of optimizing

Artificial Intelligence
Extremal optimization: heuristics via coevolutionary avalanches

Computing in Science and Engineering
Efficient initial solution to extremal optimization algorithm for weighted MAXSAT problem

IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Evolutionary programming made faster

IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization

IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, a local-search heuristic algorithm called Extremal Optimization (EO) has been successfully applied in some combinatorial optimization problems. However, there are only limited papers studying on the mechanism of EO applied to the numerical optimization problems so far. This paper presents the studies on the applications of EO to numerical constrained optimization problems with a set of popular benchmark problems. To enhance and improve the search performance and efficiency of EO, we developed a novel EO strategy with population based search. The newly developed EO algorithm is named population-based EO (PEO). Additionally, we adopted the adaptive Lévy mutation, which is more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Compared with three state-of-the-art stochastic search methods with six popular benchmark problems, it has been shown that our approach is a good choice to deal with the numerical constrained optimization problems.