A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems

Abstract

A novel optimization algorithm called hybrid salp swarm algorithm with teaching-learning based optimization (HSSATLBO) is proposed in this paper to solve reliability redundancy allocation problems (RRAP) with nonlinear resource constraints. Salp swarm algorithm (SSA) is one of the newest meta-heuristic algorithms which mimic the swarming behaviour of salps. It is an efficient swarm optimization technique that has been used to solve various kinds of complex optimization problems. However, SSA suffers a slow convergence rate due to its poor exploitation ability. In view of this inadequacy and resulting in a better balance between exploration and exploitation, the proposed hybrid method HSSATLBO has been developed where the searching procedures of SSA are renovated based on the TLBO algorithm. The good global search ability of SSA and fast convergence of TLBO help to maximize the system reliability through the choices of redundancy and component reliability. The performance of the proposed HSSATLBO algorithm has been demonstrated by seven well-known benchmark problems related to reliability optimization that includes series system, complex (bridge) system, series-parallel system, overspeed protection system, convex system, mixed series-parallel system, and large-scale system with dimensions 36, 38, 40, 42 and 50. After illustration, the outcomes of the proposed HSSATLBO are compared with several recently developed competitive meta-heuristic algorithms and also with three improved variants of SSA. Additionally, the HSSATLBO results are statistically investigated with the wilcoxon sign-rank test and multiple comparison test to show the significance of the results. The experimental results suggest that HSSATLBO significantly outperforms other algorithms and has become a remarkable and promising tool for solving RRAP.

The Exploration of Wild Horse Optimization in Reliability Redundancy Allocation Problems

“…Results gained after applying WHO to P1, P2, and P3 are compared to those of the simplified swarm algorithm (SSO) [8], and the attraction-repulsion imperialist competitive algorithm (AR-ICA) [10], and hybrid salp swarm algorithm with teachinglearning based optimization (HSS-TLBO) [16] As for P4, the obtained results are compared to PSO [9] and GBO [13]. Results are given in Table 10, the best solution for each problem is stated and compared to the above-mentioned methods.…”

“…Tables 6, 7, 8, and 9 show the comparison among WHO and the above-mentioned methods for test problems P1, P2, and P3. In problem P1, HSS-TLBO [16] used a different value for (w5) input as previously indicated in Table 2. That is why two comparisons are made, Table 6 shows the results using (w5 = 4.5), whereas Table 7 displays the results of (w5 = 3.5).…”

“…( 15). Table 2 shows the test input data [2], the (w5) value differs according to [16] as specified in Table 2, this is to be considered in the comparisons. For problem P2, the complex/bridge system is illustrated in Fig.…”

“…Most recently in 2022, Kundu et. al., used a hybrid salp swarm algorithm (HSS-TLBO) based on teachinglearning based optimization (TLBO) for RRAPs, aiming to merge the SSA ability of global search with the fast converging of (TLBO) to maximize reliability [16].…”

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

“…Newly enhanced algorithms include the fractional-order modified Harris hawks optimizer (FMHHO) 26 , the modified manta ray foraging optimization algorithm (MMRFOA) 27 , an enhanced slime mould algorithm 28 , the hybrid marine predator algorithm (HMPA) 29 , partitioned step particle swarm optimization (PSPSO) 30 , the improved chimp optimization algorithm (ICHOA) 31 , the high performance cuckoo search algorithm (HPCSA) 32 , the comprehensive learning marine predator algorithm (CLMPA) 33 , the enhanced sparrow search algorithm (ESSA) 34 , the hybrid algorithm that is known as three-learning strategy PSO (TLS-PSO) 35 , the enhanced shuffled shepherd optimization algorithm (ESSOA) 36 , the hybrid salp swarm algorithm with teaching-learning-based optimization (HSSATLBO) 37 , and an enhanced hybrid of crisscross optimization and the arithmetic optimization algorithm (CSOAOA) 38 . Both original and enhanced metaheuristic optimization algorithms are used in a wide range of fields, including engineering, business, transportation, energy, and even the social sciences.…”