Particle Swarm Optimization (PSO) is a stochastic optimization method, based on the social behavior of bird flocks. The method, known for its high performance in optimization, has been mainly developed for problems involving just quantitative variables. In this paper we propose a new approach called Qualitative Particle Swarm Optimization (Q-PSO) where the variables in the optimization can be both qualitative and quantitative and the updating rule is derived adopting probabilistic measures. We apply this procedure to a complex engineering optimization problem concerning building fa¸cade design. More specifically, we address the problem of deriving an energy-efficient building design, i.e. a design that minimizes the energy consumption (and the emission of carbon dioxide) for heating, cooling and lighting. We develop a simulation study to evaluate Q-PSO procedure and we derive comparisons with most conventional approaches. The study shows a very good performance of our approach in achieving the assigned target.

Slanzi, D., Borrotti, M., De March, D., Orlando, D., Giove, S., Poli, I. (2014). Qualitative Particle Swarm Optimization (Q-PSO) for Energy-Efficient Building Designs. In Advances in Artificial Life and Evolutionary Computation (pp.13-25). Springer Nature [10.1007/978-3-319-12745-3_2].

Qualitative Particle Swarm Optimization (Q-PSO) for Energy-Efficient Building Designs

Borrotti, M;
2014

Abstract

Particle Swarm Optimization (PSO) is a stochastic optimization method, based on the social behavior of bird flocks. The method, known for its high performance in optimization, has been mainly developed for problems involving just quantitative variables. In this paper we propose a new approach called Qualitative Particle Swarm Optimization (Q-PSO) where the variables in the optimization can be both qualitative and quantitative and the updating rule is derived adopting probabilistic measures. We apply this procedure to a complex engineering optimization problem concerning building fa¸cade design. More specifically, we address the problem of deriving an energy-efficient building design, i.e. a design that minimizes the energy consumption (and the emission of carbon dioxide) for heating, cooling and lighting. We develop a simulation study to evaluate Q-PSO procedure and we derive comparisons with most conventional approaches. The study shows a very good performance of our approach in achieving the assigned target.
paper
Energy-efficient building design; Engineering optimization; Qualitative particle swarm optimization
English
Italian Workshop on Artificial Life and Evolutionary Computation
2014
Advances in Artificial Life and Evolutionary Computation
978-3-319-12744-6
2014
445
13
25
none
Slanzi, D., Borrotti, M., De March, D., Orlando, D., Giove, S., Poli, I. (2014). Qualitative Particle Swarm Optimization (Q-PSO) for Energy-Efficient Building Designs. In Advances in Artificial Life and Evolutionary Computation (pp.13-25). Springer Nature [10.1007/978-3-319-12745-3_2].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/214682
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