Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Metaheuristic Optimization Algorithms Research
Abstract
This paper presents a novel approach to distributed algorithm design leveraging the power of multi-agent collaboration. The core idea is to automate the design and optimization process by training a population of intelligent agents through techniques such as evolutionary algorithms or reinforcement learning. These agents learn to coordinate and communicate, ultimately solving distributed computing problems. The system utilizes a simulated environment for both evaluation and iterative improvement. This approach contrasts with traditional manual algorithm design, offering the potential for discovering more efficient and robust solutions, especially in complex scenarios where human intuition may be limited. The research explores the potential of multi-agent systems to fundamentally change the landscape of algorithm design, moving towards adaptive and self-optimizing solutions. The key contribution lies in the automated discovery process, driven by the collective intelligence of the agents.
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
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This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.
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This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
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It is found that roles of MVPs in startups were not fully aware by entrepreneurs, and entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP).
Anh Nguyen-Duc, P. Abrahamsson· International Conference on...· 93 citations· ⚡9
It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.
Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al.· International Conference on...· 62 citations· ⚡6
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
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