পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| এজেন্ট-ভিত্তিক জেনেটিক অ্যালগরিদম× | এজেন্ট-ভিত্তিক বহু-উদ্দেশ্যমূলক অপ্টিমাইজেশান× | |
|---|---|---|
| ক্ষেত্র | অনুকরণ | অনুকরণ |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1990s | 1990s–2000s |
| প্রবর্তক≠ | Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s | Bonabeau, Dorigo, Theraulaz; Coello Coello et al. |
| ধরন≠ | Hybrid evolutionary-agent simulation | Simulation-driven multi-objective search |
| মৌলিক উৎস≠ | Adamidis, P., & Petridis, V. (1996). Co-operating populations with different evolution behaviors. Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC 1996), 188-191. IEEE. link ↗ | Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598 |
| অপর নাম | ABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GA | ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO |
| সম্পর্কিত | 5 | 5 |
| সারসংক্ষেপ≠ | An Agent-Based Genetic Algorithm (ABGA) partitions a genetic algorithm's population across a network of autonomous agents, each maintaining a local sub-population and evolving it independently. Agents periodically exchange individuals (migration) based on proximity or communication rules, enabling parallel exploration of the search space while preserving population diversity and avoiding premature convergence. | Agent-based multi-objective optimization (ABMOO) embeds autonomous agents inside a simulation environment and evolves their behavior or parameters to simultaneously optimize two or more conflicting objectives, yielding a Pareto-efficient frontier of solutions rather than a single optimum. It is suited to complex adaptive systems where objectives emerge from micro-level interactions rather than closed-form equations. |
| ScholarGateডেটাসেট ↗ |
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