A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
By Sanghamitra Bandyopadhyay, Sriparna Saha, Ujjwal Maulik, Kalyanmoy Deb. IEEE Transactions on Evolutionary Computation, 2008
838 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/07e4e9b2b22f891466bd617f259930bbafcceac7
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In Artificial Intelligence
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Citations in the corpus
- Direct Multisearch for Multiobjective Optimization (2011, methodology): “We have considered in our numerical studies the following publicly available solvers for MOO without derivatives: • AMOSA (Archived MultiObjective Simulated Annealing) [5] — www.isical.ac.in/ ~sriparna_r/software.html; • BIMADS (BI-Objective Mesh Adaptive Direct Search) [3] tested only for problems ”
- A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, methodology): “Physical algorithms Simulated Annealing (SA) [48] Multi-objective SA [313]”
- The Social Engineering Optimizer (SEO) (2018, neutral): “In SA, the intensification phase is done by neighboring whereas the”
- Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition (2014, methodology): “The first one is Pareto dominance based; the well-known algorithms in this category are NSGA-II [12], SPEA2 [13], PAES [14], and AMOSA [15].”