Nature's Way of Optimizing

TL;DR

This paper introduces Extremal Optimization, a natural-inspired method for solving complex optimization problems by iteratively removing the worst components, demonstrating competitive performance on graph partitioning and traveling salesman problems.

Contribution

It presents a novel, simple, and effective optimization technique inspired by natural self-organizing processes, with only one adjustable parameter, outperforming some existing stochastic methods.

Findings

Competitive performance on graph partitioning

Effective on traveling salesman problem

Outperforms some existing stochastic methods

Abstract

We propose a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organizing processes often found in nature. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions. Drawing upon models used to simulate far-from-equilibrium dynamics, it complements approximation methods inspired by equilibrium statistical physics, such as Simulated Annealing. With only one adjustable parameter, its performance proves competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.