विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| एजेंट-आधारित जेनेटिक एल्गोरिथम× | एजेंट-आधारित बहु-उद्देश्यीय अनुकूलन× | |
|---|---|---|
| क्षेत्र | अनुकरण | अनुकरण |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1990s | 1990s–2000s |
| प्रवर्तक≠ | Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s | Bonabeau, Dorigo, Theraulaz; Coello Coello et al. |
| प्रकार≠ | Hybrid evolutionary-agent simulation | Simulation-driven multi-objective search |
| मौलिक स्रोत≠ | 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 |
| उपनाम | ABGA, Agent-Based GA, Multi-Agent Genetic Algorithm, Distributed Agent GA | ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO |
| संबंधित | 5 | 5 |
| सारांश≠ | 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. |
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