A Hybrid Agent-Oriented Stochastic Diffusion Search and Beam Search Architecture

Abstract

Various swarm intelligence-based algorithms have been developed and explored over the years. These algorithms include particle swarm optimisation, spider monkey optimisation, artificial bee colony algorithm, ant colony optimisation, and bacterial foraging optimisation, among many others. However, according to the reviewed literature, classical or traditional optimisation methods are confronted with difficulties when scaling up to real-world optimisation problems; therefore, there is a need to develop efficient and robust computational algorithms that can solve problems numerically, irrespective of their sizes. Inspired by natureinspired swarm intelligence algorithms, this study has created a hybrid-based algorithm utilising Stochastic Diffusion Search (SDS) and Beam Search algorithms. In this  s ability to operate as a multi-agent population-based global search and utilised to initialise, update, and maintain a list of candidate regions in the search space. In addition, it is responsible for recruiting agents for those regions in the search space. A variation of the knapsack problem was employed to test the created hybrid model. In this problem, constraints were established, as discussed later in the paper (in section 3.5). The results discussed in section 4 indicated that the algorithm found a better solution in the search space. The results also showed a strong and consistent beam after a series of iterations during the simulation. The specific improvements observed with the hybrid algorithm are that, because it is implemented as an actororiented system, it is completely parallelised,  every actor is independent of every other actor and can be run automatically, on its own individual green thread but can, in fact, be run on another computer. This parallelisability, composability, and the resulting distributable nature of the new algorithm are the main advantages over the standard implementation of either stochastic diffusion search or beam search, neither of which are parallelised by default. Its implication is based on the fact that the pace of improvement in available computing power has levelled off following several decades of sustained growth characterised by Moore's law. The hybrid algorithm is highly parallelisable, making it easy to take advantage of multiple cores on a single computer or multiple machine instances in a cloud computing scenario. Therefore, this is a modern version of both component algorithms within the proposed hybrid approach as it translates better into environments where it is easier to scale outwards rather than upwards. 

https://doi.org/10.59200/ICONIC.2024.023