The principle aim of the current communication is to scrutinize the impact of distinguished effects like variable thermal conductivity and variable molecular diffusivity on non-Newtonian Reiner–Philippoff fluid moving over a stretchable surface. The process of heat transfer is carried out in the presence of nonlinear thermal radiation, variable thermal conductivity, and heat generation/absorption. Furthermore, the study of mass transfer phenomena is carried out in the existence of variable molecular diffusivity. The PDEs regarding our model are renovated into ODEs by utilizing similarity transformation. Furthermore, the dimensionless model is tackled with the help of the RK4 method in conjunction with the shooting technique. The effects of different physical parameters that emerged during the numerical simulation on mass transfer rate, heat transfer rate, and velocity field are portrayed in the form of tables and graphs. It is noteworthy that an elevation in the heat source/sink parameters causes a reduction in the temperature profile. Moreover, a positive variation in the species diffusivity parameter augments the mass fraction field. A variation in the fluid parameter is found to be significantly affecting the shear thinning and shear thickening behaviour of the fluid. Reliability of the numerical outcomes is judged by comparing the obtained outcomes with the already available literature. The article is unique in its sense that the heat and mass transfer analysis of Reiner–Philippoff fluid under the aforementioned effects has not been investigated yet.

Teaching learning based optimization (TLBO) algorithm is a distinguished nature-inspired population-based meta-heuristic, which is basically designed for unconstrained optimization. TLBO mimics teaching learning process through which learners acquire knowledge from their teachers, and improve their results/grades, accordingly. Stochastic ranking (SR) is a constrained handling technique (CHT), which produces greediness among solutions to improve their fitness values and feasibility. Violation constraint handling (VCH) technique produces more feasibility among the existing superiority of feasibility CHTs due to its additional factor of ranking based on the number of constraints violated (NCV). This work brings in a new variant of SR, namely hybrid stochastic ranking (HSR), which combines SR and VCH. For constrained optimization, the integration of some CHT with TLBO is essential. In this paper, HSR is integrated with TLBO and a new constrained version of TLBO called HSR-TLBO is designed. The efficiency of HSRTLBO is checked on constrained test functions of the suit CEC 2017. The experimental results show that HSR-TLBO got prominent position when compared and ranked with the top four papers and our two newly designed constrained variants of TLBO, MSR-TLBO and MVCH-TLBO, based on the provided budget and ranking criteria of the mentioned suit.

Adaptive penalty function methods (APFMs) are promising constraints handling techniques. In an APFM, a penalty parameter which balances constrains’ violations and objective function values is adaptively adjusted. This work modifies an APFM that uses a near feasibility threshold (NFT), a portion around the feasible region where infeasible solutions are considered as good ones, for constrained multiobjective optimization. The modified APFM with five different settings of NFT is embedded in a prominent multiobjective evolutionary algorithm based on decomposition, MOEA/D. This brings in five constrained variants of the base algorithm, denoted by CMOEA/D-TAP1 to CMOEA/D-TAP5. These variants are tested on well-known constrained multiobjective benchmark test suits, the CTP series and the CF series. The proposed variants are compared with four best performing algorithms through HV metric (hyper volume metric) statistics for CTP series, and with seven state-of-the-art algorithms through Wilcoxon rank sum test employed to mean values of both HV and IGD (inverted generational distance matric) metrics for CF series. Simulation results reflect that overall performance of the newly introduced variants is better than the competitors for the taken benchmark test suits.