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[2602.22230] An Adaptive Multichain Blockchain: A Multiobjective Optimization Approach
Summary
This paper presents a novel adaptive multichain blockchain model that addresses scalability issues by employing a multiobjective optimization approach for resource allocation among applications and operators.
Why It Matters
As blockchain technology continues to evolve, scalability remains a critical challenge. This research provides a framework for dynamically optimizing blockchain configurations, potentially enhancing transaction throughput and stability while addressing fairness and incentive issues. Such advancements could significantly impact various industries relying on blockchain for secure transactions.
Key Takeaways
- Introduces a multiagent resource-allocation model for blockchain.
- Optimizes blockchain configurations to enhance scalability and efficiency.
- Addresses trade-offs between throughput, decentralization, and service stability.
- Provides a modular approach accommodating diverse application types.
- Analyzes fairness and incentive mechanisms within the proposed model.
Computer Science > Cryptography and Security arXiv:2602.22230 (cs) [Submitted on 14 Feb 2026] Title:An Adaptive Multichain Blockchain: A Multiobjective Optimization Approach Authors:Nimrod Talmon, Haim Zysberg View a PDF of the paper titled An Adaptive Multichain Blockchain: A Multiobjective Optimization Approach, by Nimrod Talmon and Haim Zysberg View PDF HTML (experimental) Abstract:Blockchains are widely used for secure transaction processing, but their scalability remains limited, and existing multichain designs are typically static even as demand and capacity shift. We cast blockchain configuration as a multiagent resource-allocation problem: applications and operators declare demand, capacity, and price bounds; an optimizer groups them into ephemeral chains each epoch and sets a chain-level clearing price. The objective maximizes a governance-weighted combination of normalized utilities for applications, operators, and the system. The model is modular -- accommodating capability compatibility, application-type diversity, and epoch-to-epoch stability -- and can be solved off-chain with outcomes verifiable on-chain. We analyze fairness and incentive issues and present simulations that highlight trade-offs among throughput, decentralization, operator yield, and service stability. Subjects: Cryptography and Security (cs.CR); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Multiagent Systems (cs.MA) Cite as: arXiv:2602.22230 [cs.CR] (or arXiv:2602.22...
