Abstract
Optimization of systems at the design stage is a key element for competitive industrial installations and plants. The subsystems of a system may be connected in various structural configurations such as in series, parallel–series, and bridge networks. The design of such systems involves challenges of reliability, cost, availability, weight, and volume. Often, in the literature, this optimization problem is addressed as a single-objective one. This chapter investigates the design of a parallel–series system by considering both the system cost and availability as objectives. The multi-objective optimization problem is converted into a single-objective problem using two weighed sum methods. Numerical results of five nature-inspired computing techniques are compared in order to highlight their performances in solving this problem. These are the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), the flower pollination algorithm (FPA), and the plant propagation algorithm (PPA).
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Mellal, M.A., Salhi, A. (2020). Parallel–Series System Optimization by Weighting Sum Methods and Nature-Inspired Computing. In: Dey, N., Ashour, A., Bhattacharyya, S. (eds) Applied Nature-Inspired Computing: Algorithms and Case Studies. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-9263-4_10
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