Blockchain Scheme Based on Evolutionary Proof of Work

In recent years, applications of the Blockchain concept, esp. as ledger for bitcoin transactions, has already resulted in huge amounts of wasted electrical energy for performing the Proof-of-Work tasks (cryptographic puzzles). Here, we consider an alternative concept to have this energy used at least for a useful purpose, the solution of real-world optimization problems. By means of the Traveling Salesperson Problem as model problem, we propose a concept to use optimization algorithms in an iterative manner to provide the Proof-of-Work needed to expand the Blockchain by a new block. The basic idea is to improve the tour cost for the best tour found for block n, extended by adding one more city, as a requirement for the inclusion of a new block in the Blockchain. This allows for the design of limited Blockchains, solving the underlying combinatorial optimization problems at the same time. Independently, it calls in for new efficient optimization algorithms and can serve as a real-world contest. It is discussed that metaheuristic algorithms perform an attractive class of optimization algorithms that can be used for the proposed approach. Numerical experiments also demonstrate the growth in problem complexity being handled by a binary PSO, which is a basic requirement for the full concept to work in practice.

[1]  Pedro Larrañaga,et al.  Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators , 1999, Artificial Intelligence Review.

[2]  Zibin Zheng,et al.  An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends , 2017, 2017 IEEE International Congress on Big Data (BigData Congress).

[3]  Xin-She Yang,et al.  An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems , 2016, Eng. Appl. Artif. Intell..

[4]  Emin Gün Sirer,et al.  Bitcoin-NG: A Scalable Blockchain Protocol , 2015, NSDI.

[5]  Halife Kodaz,et al.  A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem , 2015, Appl. Soft Comput..

[6]  Satoshi Nakamoto Bitcoin : A Peer-to-Peer Electronic Cash System , 2009 .

[7]  Xiaojun Wu,et al.  Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection , 2012, Evolutionary Computation.

[8]  Xin-She Yang,et al.  Random-key cuckoo search for the travelling salesman problem , 2015, Soft Comput..

[9]  Jinzhao Wu,et al.  A discrete invasive weed optimization algorithm for solving traveling salesman problem , 2015, Neurocomputing.

[10]  Xuesong Yan,et al.  Solve Traveling Salesman Problem Using Particle Swarm Optimization Algorithm , 2012 .

[11]  Hans Weigand,et al.  Understanding the Blockchain Using Enterprise Ontology , 2017, CAiSE.

[12]  Ralph C. Merkle,et al.  Secrecy, authentication, and public key systems , 1979 .