Blockchains (BCs) are distributed database systems, popular for their innovative, unsupervised maintenance process. They use a so-called consensus protocol to prevent inference by any third party of absolute trust. Security, privacy, consistency, and energy consumption have been identified as the main issues involved in BC maintenance. According to the recent literature, some of these issues can be formulated as combinatorial optimization (CO) problems, and this fact motivated us to consider incorporating CO approaches into a BC. In this paper, we propose the new combinatorial optimization consensus protocol (COCP) based on the proof-of-useful-work (PoUW) concept that assumes solving instances of real-life CO problems. Due to the complexity of the underlying CO problems, we have developed various types of heuristic methods, which are utilized in the COCP. Most of these methods are problem-dependent stochastic heuristic or metaheuristic methods. As is the case with the majority of consensus protocols, PoUW exhibits the property of asymmetry. It is difficult to find a solution for the considered CO problem; however, once a solution is found, its verification is straightforward. We present here a BC framework combining the two above-mentioned fields of research: BC and CO. This framework consists of improvements aiming towards developing the COCP of the PoUW type. The main advantage of this consensus protocol is the efficient utilization of computing resources (by exploring them for finding solutions of real-life CO problem instances), and the provision of a broad range of incentives for the various BC participants. We enumerate the potential benefits of the COCP with respect to its practical impacts and savings in power consumption, describing in detail an illustrative example based on part of the real-life BC network. In addition, we identify several challenges that should be resolved in order to implement a useful, secure, and efficient PoUW consensus protocol.

The wide range of Blockchain (BC) applications and BC’s ubiquity come from the fact that BC, as a collection of records linked to each other, is strongly resistant to alteration, protected using cryptography, and maintained autonomously. All these benefits come with a cost, which in BC is expressed by a very high use of energy needed to execute consensus protocols. Traditionally, consensus protocols based on Proof-of-Work (PoW) ensure fairness, but are not very useful. The paradigm proposed in the recent literature, known as Proof-of-Useful-Work (PoUW), assumes the completion of additional useful work for the same amount of resources (energy) used. However, the majority of the proposed PoUW approaches do not adequately consider fairness in balancing and controlling the difficulty of the work miners need to perform. A minority of the studies that do address fairness in miners’ work utilize PoW as a tool to ensure it. Therefore, a general framework to provide a structure for understanding the difficulty of useful work and how it can be used to fine-tune the complexity of miners’ effort in PoUW-based consensus protocols is proposed in this paper. The main characteristic of the proposed framework is that controlling the difficulty and fairness of miners’ work in PoUW-based consensus protocols is achieved exclusively through the useful work. The modules of the framework are discussed, and many research challenges and opportunities are articulated. The benefits of the proposed approach are illustrated taking as an example two optimization algorithms for a variant of the scheduling problem. In addition, the steps that should be taken to make this general framework applicable to any PoUW-based consensus protocols are identified.

Artificial intelligence (AI) agents are increasingly shaping vital sectors of society, including healthcare, education, supply chains, and finance. As their influence grows, AI alignment research plays a pivotal role in ensuring these systems are trustworthy, transparent, and aligned with human values. Leveraging blockchain technology, proven over the past decade in enabling transparent, tamper-resistant distributed systems, offers significant potential to strengthen AI alignment. However, despite its potential, the current AI alignment literature has yet to systematically explore the effectiveness of blockchain in facilitating secure and ethical behavior in AI agents. While existing systematic literature reviews (SLRs) in AI alignment address various aspects of AI safety and AI alignment, this SLR specifically examines the gap at the intersection of AI alignment, blockchain, and ethics. To address this gap, this SLR explores how blockchain technology can overcome the limitations of existing AI alignment approaches. We searched for studies containing keywords from AI, blockchain, and ethics domains in the Scopus database, identifying 7110 initial records on 28 May 2024. We excluded studies which did not answer our research questions and did not discuss the thematic intersection between AI, blockchain, and ethics to a sufficient extent. The quality of the selected studies was assessed on the basis of their methodology, clarity, completeness, and transparency, resulting in a final number of 46 included studies, the majority of which were journal articles. Results were synthesized through quantitative topic analysis and qualitative analysis to identify key themes and patterns. The contributions of this paper include the following: (i) presentation of the results of an SLR conducted to identify, extract, evaluate, and synthesize studies on the symbiosis of AI alignment, blockchain, and ethics; (ii) summary and categorization of the existing benefits and challenges in incorporating blockchain for AI alignment within the context of ethics; (iii) development of a framework that will facilitate new research activities; and (iv) establishment of the state of evidence with in-depth assessment. The proposed blockchain-based AI alignment framework in this study demonstrates that integrating blockchain with AI alignment can substantially enhance robustness, promote public trust, and facilitate ethical compliance in AI systems.

The Bee Colony Optimization (BCO) algorithm is a meta-heuristic that belongs to the class of biologically inspired stochastic swarm optimization methods, based on the foraging habits of bees in nature. BCO operates on a population of solutions, and therefore, it represents a good basis for parallelization. The main contribution of this work is the development of new and efficient parallelization strategies for BCO. We propose two synchronous and two asynchronous parallelization strategies for a distributed memory multiprocessor architecture under the Message Passing Interface (MPI) communication protocol. The first synchronous strategy involves independent execution of several BCO algorithms, while the second one implements cooperation between these algorithms. The asynchronous strategies are implemented in two ways: with centralized and non-centralized communication controls. The presented experimental results, addressing the problem of static scheduling independent tasks on identical machines, show that our parallel BCO algorithms provide excellent performance. As for the case of independent execution, a significant speedup is obtained while preserving the solution quality. Compared to the sequential execution, cooperative strategy leads to better quality solutions within the same amount of wall-clock time, as long as it is applied to a modest number of processors engaged in parallel BCO execution. As this number increases, asynchronous strategies outperform the other ones with respect to both solution quality and running time.