Open Access

REVIEW

Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms

Haykel Marouani^1,2, Omar Al-mutiri^1,*

1 College of Engineering, Muzahimiyah Branch, King Saud University, Riyadh, 11451, Saudi Arabia
2 University of Monastir, LGM, ENIM, Avenue Ibn-Eljazzar, 5019, Monastir, Tunisia

* Corresponding Author: Omar Al-mutiri. Email:

Computers, Materials & Continua 2022, 71(1), 537-571. https://doi.org/10.32604/cmc.2022.020098

Received 09 May 2021; Accepted 25 June 2021; Issue published 03 November 2021

Abstract

The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field. New algorithms are continually being designed on the basis of observations of nature, wildlife, and humanity. In this paper, we review eight major evolutionary algorithms that emulate the behavior of civilization, ants, bees, fishes, and birds (i.e., genetic algorithms, bee colony optimization, simulated annealing, particle swarm optimization, biogeography-based optimization, artificial immune system optimization, cuckoo algorithm and imperialist competitive algorithm). We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation problems. Results from a literature survey show the best results found for series, series–parallel, bridge, and applied case problems (e.g., overspeeding gas turbine benchmark). Review of literature from recent years indicates an extensive improvement in the algorithm reliability performance. However, this improvement has been difficult to achieve for high-reliability applications. Insights and future challenges in reliability–redundancy allocation problems optimization are also discussed in this paper.

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