Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uboreshaji wa Malengo Mengi Unaotegemea Wakala× | Uboreshaji wa Malengo Mengi ya Kistochastiki× | |
|---|---|---|
| Nyanja | Uigaji | Uigaji |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili | 1990s–2000s | 1990s–2000s |
| Mwanzilishi≠ | Bonabeau, Dorigo, Theraulaz; Coello Coello et al. | Various (Fonseca, Fleming, Deb, Zitzler, and others) |
| Aina≠ | Simulation-driven multi-objective search | Stochastic metaheuristic optimization |
| Chanzo asilia≠ | Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Majina mbadala | ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | 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. | Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty. |
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