A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models
نویسندگان
چکیده
Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1].
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Particle Swarm Optimization Techniques for Finding Optimal Mixture Designs
Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in finding the optimum solution or close to the optimum for a wide variety of real and complicated optimization problems in engineering and computer science. This paper adapts PSO methodology by first solving an optimization problem on the hypercube and then projecting the solution onto the q-si...
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