Solving Cold‐Standby Reliability‐Redundancy Allocation Problems with Particle‐Based Simplified Swarm Optimization

Abstract

Particle swarm optimization (PSO) and simplified swarm optimization (SSO) are two of the state-of-the-art swarm intelligence technique that is widely utilized for optimization purposes. This paper describes a particle-based simplified swarm optimization (PSSO) procedure which combines the update mechanisms (UMs) of PSO and SSO to determine optimal system reliability for reliability-redundancy allocation problems (RRAPs) with cold-standby strategy while aimed at maximizing the system reliability. With comprehensive experimental test on the typical and famous four benchmarks of RRAP, PSSO is compared with other recently introduced algorithms in four different widely used systems, i.e., a series system, a series-parallel system, a complex (bridge) system, and an overspeed protection system for a gas turbine. Finally, the results of the experiments demonstrate that the PSSO can effectively solve the system of RRAP with cold-standby strategy and has good performance in the system reliability obtained although the best system reliability is not obtained in all four benchmarks.

A Hybrid Algorithm Based on Simplified Swarm Optimization for Multi-Objective Optimizing on Combined Cooling, Heating and Power System

“…This research developed an algorithm suitable for the CCHP optimization problem based on the SSO algorithm. Its biggest feature is that the adjustment of parameters is very simple, which has been shown as effective in solving many optimization problems in various fields such as data mining in medicine [38], disassembly sequencing problems [39], redundancy allocation problems (RAP) [40,41], reliability redundancy allocation problems (RRAP) [42][43][44][45][46], RAP in sensor systems [47], vehicle routing problems in the supply chain [48,49], price problems in the supply chain [50], optimizing sensing coverage in wireless sensor networks [51], data mining [52], airplane cockpits [53], energy and signal optimization in wireless sensor networks [54], improving UM of SSO [55], task scheduling optimization in fog computing [56], and various networks [57][58][59].…”