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التحسين المعزز بالمستعمرات النملية متعدد الأهداف (MOACO)×الخوارزمية الجينية متعددة الأهداف (MOGA)×
المجالالمحاكاةالمحاكاة
العائلةProcess / pipelineProcess / pipeline
سنة النشأة19991984
صاحب الطريقةGambardella, Taillard & Agazzi; Dorigo & StützleSchaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations)
النوعPopulation-based metaheuristicPopulation-based evolutionary optimizer
المصدر التأسيسيGambardella, L. M., Taillard, E., & Agazzi, G. (1999). MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows. In D. Corne, M. Dorigo, & F. Glover (Eds.), New Ideas in Optimization (pp. 63–76). McGraw-Hill. link ↗Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673
الأسماء البديلةMOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACOMOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO
ذات صلة44
الملخصMulti-Objective Ant Colony Optimization (MOACO) is a swarm-intelligence metaheuristic that extends the classic Ant Colony Optimization framework to simultaneously optimize two or more conflicting objectives. Artificial ants construct candidate solutions guided by pheromone trails and heuristic information, progressively building an archive of Pareto-optimal solutions rather than converging to a single best answer.A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among.
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ScholarGateقارن الطرق: Multi-objective ant colony optimization · Multi-objective genetic algorithm. استُرجع بتاريخ 2026-06-15 من https://scholargate.app/ar/compare